Dynamic Hybrid Model for Short-Term Electricity Price Forecasting
Marin Cerjan; Marin Matijaš; Marko Delimar
2014-01-01
Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike det...
Dynamic Hybrid Model for Short-Term Electricity Price Forecasting
Directory of Open Access Journals (Sweden)
Marin Cerjan
2014-05-01
Full Text Available Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.
A method for short term electricity spot price forecasting
International Nuclear Information System (INIS)
Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V.; Laitinen, E.; Haekli, J.; Antila, E.
1998-01-01
In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market
A method for short term electricity spot price forecasting
Energy Technology Data Exchange (ETDEWEB)
Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V. [VTT Energy, Espoo (Finland); Laitinen, E.; Haekli, J. [Vaasa Univ. (Finland); Antila, E. [ABB Transmit Oy (Finland)
1998-08-01
In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market
International Nuclear Information System (INIS)
Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.
2004-01-01
This paper proposes a comprehensive model for the adaptive short-term electricity price forecasting using Artificial Neural Networks (ANN) in the restructured power markets. The model consists: price simulation, price forecasting, and performance analysis. The factors impacting the electricity price forecasting, including time factors, load factors, reserve factors, and historical price factor are discussed. We adopted ANN and proposed a new definition for the MAPE using the median to study the relationship between these factors and market price as well as the performance of the electricity price forecasting. The reserve factors are included to enhance the performance of the forecasting process. The proposed model handles the price spikes more efficiently due to considering the median instead of the average. The IEEE 118-bus system and California practical system are used to demonstrate the superiority of the proposed model. (author)
Robust estimation and forecasting of the long-term seasonal component of electricity spot prices
International Nuclear Information System (INIS)
Nowotarski, Jakub; Tomczyk, Jakub; Weron, Rafał
2013-01-01
We present the results of an extensive study on estimation and forecasting of the long-term seasonal component (LTSC) of electricity spot prices. We consider a battery of over 300 models, including monthly dummies and models based on Fourier or wavelet decomposition combined with linear or exponential decay. We find that the considered wavelet-based models are significantly better in terms of forecasting spot prices up to a year ahead than the commonly used monthly dummies and sine-based models. This result questions the validity and usefulness of stochastic models of spot electricity prices built on the latter two types of LTSC models. - Highlights: • First comprehensive study on the forecasting of the long-term seasonal components • Over 300 models examined, including commonly used and new approaches • Wavelet-based models outperform sine-based and monthly dummy models. • Validity of stochastic models built on sines or monthly dummies is questionable
Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach
International Nuclear Information System (INIS)
Catalao, J.P.S.; Pousinho, H.M.I.; Mendes, V.M.F.
2011-01-01
In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)
Hybrid ARIMA and Support Vector Regression in Short‑term Electricity Price Forecasting
Directory of Open Access Journals (Sweden)
Jindřich Pokora
2017-01-01
Full Text Available The literature suggests that, in short‑term electricity‑price forecasting, a combination of ARIMA and support vector regression (SVR yields performance improvement over separate use of each method. The objective of the research is to investigate the circumstances under which these hybrid models are superior for day‑ahead hourly price forecasting. Analysis of the Nord Pool market with 16 interconnected areas and 6 investigated monthly periods allows not only for a considerable level of generalizability but also for assessment of the effect of transmission congestion since this causes differences in prices between the Nord Pool areas. The paper finds that SVR, SVRARIMA and ARIMASVR provide similar performance, at the same time, hybrid methods outperform single models in terms of RMSE in 98 % of investigated time series. Furthermore, it seems that higher flexibility of hybrid models improves modeling of price spikes at a slight cost of imprecision during steady periods. Lastly, superiority of hybrid models is pronounced under transmission congestions, measured as first and second moments of the electricity price.
Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors
Directory of Open Access Journals (Sweden)
Chul-Yong Lee
2017-01-01
Full Text Available In the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the model’s forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil price is estimated to increase to $169.3/Bbl by 2040.
Forecasting metal prices: Do forecasters herd?
DEFF Research Database (Denmark)
Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.
2013-01-01
We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...
DEFF Research Database (Denmark)
Bork, Lasse; Møller, Stig Vinther
2016-01-01
We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS), and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future mo...
An enhanced radial basis function network for short-term electricity price forecasting
International Nuclear Information System (INIS)
Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang
2010-01-01
This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)
Uranium price forecasting methods
International Nuclear Information System (INIS)
Fuller, D.M.
1994-01-01
This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again
Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming
Directory of Open Access Journals (Sweden)
Agustín A. Sánchez de la Nieta
2016-12-01
Full Text Available Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i forecasting of the electricity price through autoregressive integrated moving average (ARIMA models; and (ii construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP model. In addition, the results are discussed, and the conclusions are drawn.
International Nuclear Information System (INIS)
Serinaldi, Francesco
2011-01-01
In the context of the liberalized and deregulated electricity markets, price forecasting has become increasingly important for energy company's plans and market strategies. Within the class of the time series models that are used to perform price forecasting, the subclasses of methods based on stochastic time series and causal models commonly provide point forecasts, whereas the corresponding uncertainty is quantified by approximate or simulation-based confidence intervals. Aiming to improve the uncertainty assessment, this study introduces the Generalized Additive Models for Location, Scale and Shape (GAMLSS) to model the dynamically varying distribution of prices. The GAMLSS allow fitting a variety of distributions whose parameters change according to covariates via a number of linear and nonlinear relationships. In this way, price periodicities, trends and abrupt changes characterizing both the position parameter (linked to the expected value of prices), and the scale and shape parameters (related to price volatility, skewness, and kurtosis) can be explicitly incorporated in the model setup. Relying on the past behavior of the prices and exogenous variables, the GAMLSS enable the short-term (one-day ahead) forecast of the entire distribution of prices. The approach was tested on two datasets from the widely studied California Power Exchange (CalPX) market, and the less mature Italian Power Exchange (IPEX). CalPX data allow comparing the GAMLSS forecasting performance with published results obtained by different models. The study points out that the GAMLSS framework can be a flexible alternative to several linear and nonlinear stochastic models. - Research Highlights: ► Generalized Additive Models for Location, Scale and Shape (GAMLSS) are used to model electricity prices' time series. ► GAMLSS provide the entire dynamicaly varying distribution function of prices resorting to a suitable set of covariates that drive the instantaneous values of the parameters
Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models
International Nuclear Information System (INIS)
Nguyen, Hang T.; Nabney, Ian T.
2010-01-01
This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. (author)
Canadian natural gas price forecast
International Nuclear Information System (INIS)
Jones, D.
1998-01-01
The basic factors that influenced NYMEX gas prices during the winter of 1997/1998 - warm temperatures, low fuel prices, new production in the Gulf of Mexico, and the fact that forecasters had predicted a mild spring due to El Nino - were reviewed. However, it was noted that for the last 18 months the basic factors had less of an impact on market direction because of an increase in Fund and technical trader participation. Overall, gas prices were strong through most of the year. For the winter of 1998-1999 the prediction was that NYMEX gas prices will remain below $2.00 through to the end of October 1998 because of high U.S. storage levels and moderate temperatures. NYMEX gas prices are expected to peak in January 1999 at $3.25. AECO natural gas prices were predicted to decrease in the short term because of increasing levels of Canadian storage, and because of delays in Northern Border pipeline expansions. It was also predicted that AECO prices will peak in January 1999 and will remain relatively strong through the summer of 1999. tabs., figs
Directory of Open Access Journals (Sweden)
Chuntian Cheng
2016-10-01
Full Text Available For the power systems, for which few data are available for mid-term electricity market clearing price (MCP forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N model is proposed in this paper. Over the traditional GM(0, N model, three major improvements of the proposed model are: (i the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii the model parameters are identified by an improved particle swarm optimization (PSO instead of the least square method (LSM for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR model, the traditional GM(0, N model and the artificial neural network (ANN model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market.
Directory of Open Access Journals (Sweden)
Gerardo J. Osório
2016-08-01
Full Text Available The uncertainty and variability in electricity market price (EMP signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT, differential evolutionary particle swarm optimization (DEEPSO, and an adaptive neuro-fuzzy inference system (ANFIS to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.
Directory of Open Access Journals (Sweden)
Claudio Monteiro
2016-09-01
Full Text Available This paper presents novel intraday session models for price forecasts (ISMPF models for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL and the analysis of mean absolute percentage errors (MAPEs obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.
Directory of Open Access Journals (Sweden)
Antonio Bello
2016-03-01
Full Text Available One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.
House Price Forecasts, Forecaster Herding, and the Recent Crisis
DEFF Research Database (Denmark)
Stadtmann, Georg; Pierdzioch; Ruelke
2013-01-01
We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from...
House Price Forecasts, Forecaster Herding, and the Recent Crisis
Directory of Open Access Journals (Sweden)
Christian Pierdzioch
2012-11-01
Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.
International Nuclear Information System (INIS)
Bigdeli, N.; Afshar, K.; Amjady, N.
2009-01-01
Market data analysis and short-term price forecasting in Iran electricity market as a market with pay-as-bid payment mechanism has been considered in this paper. The data analysis procedure includes both correlation and predictability analysis of the most important load and price indices. The employed data are the experimental time series from Iran electricity market in its real size and is long enough to make it possible to take properties such as non-stationarity of market into account. For predictability analysis, the bifurcation diagrams and recurrence plots of the data have been investigated. The results of these analyses indicate existence of deterministic chaos in addition to non-stationarity property of the system which implies short-term predictability. In the next step, two artificial neural networks have been developed for forecasting the two price indices in Iran's electricity market. The models' input sets are selected regarding four aspects: the correlation properties of the available data, the critiques of Iran's electricity market, a proper convergence rate in case of sudden variations in the market price behavior, and the omission of cumulative forecasting errors. The simulation results based on experimental data from Iran electricity market are representative of good performance of the developed neural networks in coping with and forecasting of the market behavior, even in the case of severe volatility in the market price indices. (author)
A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting
Directory of Open Access Journals (Sweden)
Youzhu Li
2014-01-01
Full Text Available This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.
Forecasting of electricity prices with neural networks
International Nuclear Information System (INIS)
Gareta, Raquel; Romeo, Luis M.; Gil, Antonia
2006-01-01
During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools
Forecasting the term structure of crude oil futures prices with neural networks
Czech Academy of Sciences Publication Activity Database
Baruník, Jozef; Malinská, B.
2016-01-01
Roč. 164, č. 1 (2016), s. 366-379 ISSN 0306-2619 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : Term structure * Nelson–Siegel model * Dynamic neural networks * Crude oil futures Subject RIV: AH - Economics Impact factor: 7.182, year: 2016 http://library.utia.cas.cz/separaty/2016/E/barunik-0453168.pdf
Forecasting electricity market pricing using artificial neural networks
International Nuclear Information System (INIS)
Pao, Hsiao-Tien
2007-01-01
Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long
Boosting Learning Algorithm for Stock Price Forecasting
Wang, Chengzhang; Bai, Xiaoming
2018-03-01
To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.
Support vector machine for day ahead electricity price forecasting
Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti
2015-05-01
Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.
Forecasting Long-Run Electricity Prices
International Nuclear Information System (INIS)
Hamm, Gregory; Borison, Adam
2006-01-01
Estimation of long-run electricity prices is extremely important but it is also very difficult because of the many uncertainties that will determine future prices, and because of the lack of sufficient historical and forwards data. The difficulty is compounded when forecasters ignore part of the available information or unnecessarily limit their thinking about the future. The authors present a practical approach that addresses these problems. (author)
Crop Insurance Inaccurate FCIC Price Forecasts Increase Program Costs
National Research Council Canada - National Science Library
1991-01-01
...) how FCIC can improve its forecast accuracy. We found that FCIC's corn, wheat, and soybeans price forecasts exhibit large bias errors that exceed those of other available alternative forecasts and that FCIC would have spent...
Electricity price forecasting through transfer function models
International Nuclear Information System (INIS)
Nogales, F.J.; Conejo, A.J.
2006-01-01
Forecasting electricity prices in present day competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes a transfer function model to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build it. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The proposed model is compared with naive and other techniques. Journal of the Operational Research Society (2006) 57, 350-356.doi:10.1057/palgrave.jors.2601995; published online 18 May 2005. (author)
Directory of Open Access Journals (Sweden)
Xing Yan
2015-01-01
Full Text Available Currently, there are many techniques available for short-term forecasting of the electricity market clearing price (MCP, but very little work has been done in the area of midterm forecasting of the electricity MCP. The midterm forecasting of the electricity MCP is essential for maintenance scheduling, planning, bilateral contracting, resources reallocation, and budgeting. A two-stage multiple support vector machine (SVM based midterm forecasting model of the electricity MCP is proposed in this paper. The first stage is utilized to separate the input data into corresponding price zones by using a single SVM. Then, the second stage is applied utilizing four parallel designed SVMs to forecast the electricity price in four different price zones. Compared to the forecasting model using a single SVM, the proposed model showed improved forecasting accuracy in both peak prices and overall system. PJM interconnection data are used to test the proposed model.
A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting
Directory of Open Access Journals (Sweden)
Weishang Guo
2017-09-01
Full Text Available Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND method, fruit fly optimization algorithm (FOA, and least square support vector machine (LSSVM model, namely BND-FOA-LSSVM model. Firstly, the original electricity price time series were decomposed into deterministic term, periodic term, and stochastic term by using BND model. Then, these three decomposed terms were forecasted by employing LSSVM model, respectively. Meanwhile, to improve the forecasting performance, a new swarm intelligence optimization algorithm FOA was used to automatically determine the optimal parameters of LSSVM model for deterministic term forecasting, periodic term forecasting, and stochastic term forecasting. Finally, the forecasting result of electricity price can be obtained by multiplying the forecasting values of these three terms. The results show the mean absolute percentage error (MAPE, root mean square error (RMSE and mean absolute error (MAE of the proposed BND-FOA-LSSVM model are respectively 3.48%, 11.18 Yuan/MWh and 9.95 Yuan/MWh, which are much smaller than that of LSSVM, BND-LSSVM, FOA-LSSVM, auto-regressive integrated moving average (ARIMA, and empirical mode decomposition (EMD-FOA-LSSVM models. The proposed BND-FOA-LSSVM model is effective and practical for electricity price forecasting, which can improve the electricity price forecasting accuracy.
Price forecast in the competitive electricity market by support vector machine
Gao, Ciwei; Bompard, Ettore; Napoli, Roberto; Cheng, Haozhong
2007-08-01
The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter C i and ε i assigned to each training data, the flexible C i Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin Rm, HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS).
A Statistical Approach for Interval Forecasting of the Electricity Price
DEFF Research Database (Denmark)
Zhao, Jun Hua; Dong, Zhao Yang; Xu, Zhao
2008-01-01
Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting...... the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value...... of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we...
Forecasting Energy Commodity Prices Using Neural Networks
Directory of Open Access Journals (Sweden)
Massimo Panella
2012-01-01
Full Text Available A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast energy commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown explosive growth in the last decade. They have become a new asset class used also for investment purposes. This creates a huge demand for better modeling as what occurred in the stock markets in the 1970s. Their price behavior presents unique features causing complex dynamics whose prediction is regarded as a challenging task. The use of a Mixture of Gaussian neural network may provide significant improvements with respect to other well-known models. We propose a computationally efficient learning of this neural network using the maximum likelihood estimation approach to calibrate the parameters. The optimal model is identified using a hierarchical constructive procedure that progressively increases the model complexity. Extensive computer simulations validate the proposed approach and provide an accurate description of commodities prices dynamics.
The long-run forecasting of energy prices using the model of shifting trend
International Nuclear Information System (INIS)
Radchenko, Stanislav
2005-01-01
Developing models for accurate long-term energy price forecasting is an important problem because these forecasts should be useful in determining both supply and demand of energy. On the supply side, long-term forecasts determine investment decisions of energy-related companies. On the demand side, investments in physical capital and durable goods depend on price forecasts of a particular energy type. Forecasting long-run rend movements in energy prices is very important on the macroeconomic level for several developing countries because energy prices have large impacts on their real output, the balance of payments, fiscal policy, etc. Pindyck (1999) argues that the dynamics of real energy prices is mean-reverting to trend lines with slopes and levels that are shifting unpredictably over time. The hypothesis of shifting long-term trend lines was statistically tested by Benard et al. (2004). The authors find statistically significant instabilities for coal and natural gas prices. I continue the research of energy prices in the framework of continuously shifting levels and slopes of trend lines started by Pindyck (1999). The examined model offers both parsimonious approach and perspective on the developments in energy markets. Using the model of depletable resource production, Pindyck (1999) argued that the forecast of energy prices in the model is based on the long-run total marginal cost. Because the model of a shifting trend is based on the competitive behavior, one may examine deviations of oil producers from the competitive behavior by studying the difference between actual prices and long-term forecasts. To construct the long-run forecasts (10-year-ahead and 15-year-ahead) of energy prices, I modify the univariate shifting trends model of Pindyck (1999). I relax some assumptions on model parameters, the assumption of white noise error term, and propose a new Bayesian approach utilizing a Gibbs sampling algorithm to estimate the model with autocorrelation. To
FORECASTING ELECTRICITY PRICES IN DEREGULATED WHOLESALE SPOT ELECTRICITY MARKET - A REVIEW
Directory of Open Access Journals (Sweden)
Girish Godekere Panchakshara Murthy,
2014-01-01
Full Text Available In the new framework of competitive electricity markets, all power market participants need accurate price forecasting tools. Electricity price forecasts characterize significant information that can help captive power producer, independent power producer, power generation companies, power distribution companies or open access consumers in careful planning of their bidding strategies for maximizing their profits, benefits and utilities from long term, medium term and short term perspective. Short term spot electricity price forecasting techniques are either inspired from electrical engineering literature (i.e. load forecasting or from economics literature (i.e. game theory models and the time-series econometric models. In this study we investigate the emergence of spot electricity markets with particular emphasis on Indian electricity market which has never been done before and review selected finance and econometrics inspired literature and models for forecasting electricity spot prices in deregulated wholesale spot electricity markets.
Application of Markov Model in Crude Oil Price Forecasting
Directory of Open Access Journals (Sweden)
Nuhu Isah
2017-08-01
Full Text Available Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.
Yan, Xing; Chowdhury, Nurul A.
2015-01-01
Currently, there are many techniques available for short-term forecasting of the electricity market clearing price (MCP), but very little work has been done in the area of midterm forecasting of the electricity MCP. The midterm forecasting of the electricity MCP is essential for maintenance scheduling, planning, bilateral contracting, resources reallocation, and budgeting. A two-stage multiple support vector machine (SVM) based midterm forecasting model of the electricity MCP is proposed in t...
Short-Termed Integrated Forecasting System: 1993 Model documentation report
Energy Technology Data Exchange (ETDEWEB)
1993-05-01
The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.
A long-term view of worldwide fossil fuel prices
International Nuclear Information System (INIS)
Shafiee, Shahriar; Topal, Erkan
2010-01-01
This paper reviews a long-term trend of worldwide fossil fuel prices in the future by introducing a new method to forecast oil, natural gas and coal prices. The first section of this study analyses the global fossil fuel market and the historical trend of real and nominal fossil fuel prices from 1950 to 2008. Historical fossil fuel price analysis shows that coal prices are decreasing, while natural gas prices are increasing. The second section reviews previously available price modelling techniques and proposes a new comprehensive version of the long-term trend reverting jump and dip diffusion model. The third section uses the new model to forecast fossil fuel prices in nominal and real terms from 2009 to 2018. The new model follows the extrapolation of the historical sinusoidal trend of nominal and real fossil fuel prices. The historical trends show an increase in nominal/real oil and natural gas prices plus nominal coal prices, as well as a decrease in real coal prices. Furthermore, the new model forecasts that oil, natural gas and coal will stay in jump for the next couple of years and after that they will revert back to the long-term trend until 2018. (author)
Evaluating information in multiple horizon forecasts. The DOE's energy price forecasts
International Nuclear Information System (INIS)
Sanders, Dwight R.; Manfredo, Mark R.; Boris, Keith
2009-01-01
The United States Department of Energy's (DOE) quarterly price forecasts for energy commodities are examined to determine the incremental information provided at the one-through four-quarter forecast horizons. A direct test for determining information content at alternative forecast horizons, developed by Vuchelen and Gutierrez [Vuchelen, J. and Gutierrez, M.-I. 'A Direct Test of the Information Content of the OECD Growth Forecasts.' International Journal of Forecasting. 21(2005):103-117.], is used. The results suggest that the DOE's price forecasts for crude oil, gasoline, and diesel fuel do indeed provide incremental information out to three-quarters ahead, while natural gas and electricity forecasts are informative out to the four-quarter horizon. In contrast, the DOE's coal price forecasts at two-, three-, and four-quarters ahead provide no incremental information beyond that provided for the one-quarter horizon. Recommendations of how to use these results for making forecast adjustments is also provided. (author)
A hybrid approach for probabilistic forecasting of electricity price
DEFF Research Database (Denmark)
Wan, Can; Xu, Zhao; Wang, Yelei
2014-01-01
The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due to the nons...... electricity price forecasting is proposed in this paper. The effectiveness of the proposed hybrid method has been validated through comprehensive tests using real price data from Australian electricity market.......The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due...... to probabilistic interval forecasts can be of great importance to quantify the uncertainties of potential forecasts, thus effectively supporting the decision making activities against uncertainties and risks ahead. This paper proposes a hybrid approach to construct prediction intervals of MCPs with a two...
Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model
Directory of Open Access Journals (Sweden)
José R. Andrade
2017-10-01
Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.
Forecasting Day-Ahead Electricity Prices : Utilizing Hourly Prices
E. Raviv (Eran); K.E. Bouwman (Kees); D.J.C. van Dijk (Dick)
2013-01-01
textabstractThe daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual
Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices
Raviv, Eran; Bouwman, Kees E.; van Dijk, Dick
2013-01-01
This discussion paper led to a publication in 'Energy Economics' , 2015, 50, 227-239. The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average ...
Wang, Jianzhou; Xiao, Ling; Shi, Jun
2014-01-01
Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models b...
Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting
Directory of Open Access Journals (Sweden)
Bijay Neupane
2017-01-01
Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.
Energy Technology Data Exchange (ETDEWEB)
Bolinger, Mark; Wiser, Ryan; Golove, William
2003-08-13
Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e
Day-ahead price forecasting in restructured power systems using artificial neural networks
International Nuclear Information System (INIS)
Vahidinasab, V.; Jadid, S.; Kazemi, A.
2008-01-01
Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg-Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania-New Jersey-Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. (author)
Forecasting Electricity Spot Prices Accounting for Wind Power Predictions
DEFF Research Database (Denmark)
Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg
2013-01-01
A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time......-varying regression model. In a second step, time-series models, i.e., ARMA and Holt–Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pool's Elspot, during a two...
Forecasting Electricity Prices in an Optimization Hydrothermal Problem
Matías, J. M.; Bayón, L.; Suárez, P.; Argüelles, A.; Taboada, J.
2007-12-01
This paper presents an economic dispatch algorithm in a hydrothermal system within the framework of a competitive and deregulated electricity market. The optimization problem of one firm is described, whose objective function can be defined as its profit maximization. Since next-day price forecasting is an aspect crucial, this paper proposes an efficient yet highly accurate next-day price new forecasting method using a functional time series approach trying to exploit the daily seasonal structure of the series of prices. For the optimization problem, an optimal control technique is applied and Pontryagin's theorem is employed.
Combining forecasts in short term load forecasting: Empirical ...
Indian Academy of Sciences (India)
We present an empirical analysis to show that combination of short term load forecasts leads to better accuracy. We also discuss other aspects of combination, i.e.,distribution of weights, effect of variation in the historical window and distribution of forecast errors. The distribution of forecast errors is analyzed in order to get a ...
Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices
Directory of Open Access Journals (Sweden)
Antonio Bello
2016-11-01
Full Text Available This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The recalibration methodology is innovative in seeking to perform the recalibration into parametrically defined density functions. The density estimation method selects from a wide diversity of general four-parameter distributions to fit hourly spot prices, in which the first four moments are dynamically estimated as latent functions of the outputs from the fundamental model and several other plausible exogenous drivers. The proposed approach demonstrated its effectiveness against benchmark methods across the full range of percentiles of the price distribution and performed particularly well in the tails.
Price formation in electricity forward markets and the relevance of systematic forecast errors
International Nuclear Information System (INIS)
Redl, Christian; Haas, Reinhard; Huber, Claus; Boehm, Bernhard
2009-01-01
Since the liberalisation of the European electricity sector, forward and futures contracts have gained significant interest of market participants due to risk management reasons. For pricing of these contracts an important fact concerns the non-storability of electricity. In this case, according to economic theory, forward prices are related to the expected spot prices which are built on fundamental market expectations. In the following article the crucial impact parameters of forward electricity prices and the relationship between forward and future spot prices will be assessed by an empirical analysis of electricity prices at the European Energy Exchange and the Nord Pool Power Exchange. In fact, price formation in the considered markets is influenced by historic spot market prices yielding a biased forecasting power of long-term contracts. Although market and risk assessment measures of market participants and supply and demand shocks can partly explain the futures-spot bias inefficiencies in the analysed forward markets cannot be ruled out. (author)
Energy price forecast by market analysis
International Nuclear Information System (INIS)
Jongepier, A.G.
2000-01-01
A power trader benefits from accurate price predictions. Based on market analyses, KEMA Connect has developed - in cooperation with Essent Energy Trading - a market model, enhancing the insight into market operation and one's own actions and thus resulting in accurate price predictions
CAViaR-based forecast for oil price risk
International Nuclear Information System (INIS)
Huang, Dashan; Yu, Baimin; Fabozzi, Frank J.; Fukushima, Masao
2009-01-01
As a benchmark for measuring market risk, value-at-risk (VaR) reduces the risk associated with any kind of asset to just a number (amount in terms of a currency), which can be well understood by regulators, board members, and other interested parties. This paper employs a new VaR approach due to Engle and Manganelli [Engle, R.F., Manganelli, S., 2004. CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business and Economic Statistics 22, 367-381] to forecasting oil price risk. In doing so, we provide two original contributions by introducing a new exponentially weighted moving average CAViaR model and developing a mixed data regression model for multi-period VaR prediction. (author)
Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method
International Nuclear Information System (INIS)
Amjady, Nima; Keynia, Farshid
2008-01-01
In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of wavelet transform (WT) and a hybrid forecast method is proposed for this purpose. The hybrid method is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithms (EA). Both time domain and wavelet domain features are considered in a mixed data model for price forecast, in which the candidate input variables are refined by a feature selection technique. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. (author)
Forecast Share Prices with Artificial Neural Network in Crisis Periods
Directory of Open Access Journals (Sweden)
Feyyaz Zeren
2014-09-01
Full Text Available Crisis periods present quite a significant moment for financial markets. Considering not losing and changing the crisis periods into opportunities, forecasts of share prices during these periods have an importance for the investors. In this study, daily closing prices of Borsa Istanbul National 100 index during the three big crisis periods, as 1994, 2001, and 2008, have been tried to be forecasted, by using artificial neural networks. As a result of this study, it is determined that in the forecasts of Borsa Istanbul, artificial neural networks show high performance. This result was proved by both comparing the values that occurred and forecasted on the graphics, and Mean Absolute Percentage Error (MAPE calculations
Electricity price forecasting using Enhanced Probability Neural Network
International Nuclear Information System (INIS)
Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang
2010-01-01
This paper proposes a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Probability Neural Network (PNN) and Orthogonal Experimental Design (OED), an Enhanced Probability Neural Network (EPNN) is proposed in the solving process. In this paper, the Locational Marginal Price (LMP), system load and temperature of PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday, and weekend. With the OED to smooth parameters in the EPNN, the forecasting error can be improved during the training process to promote the accuracy and reliability where even the ''spikes'' can be tracked closely. Simulation results show the effectiveness of the proposed EPNN to provide quality information in a price volatile environment. (author)
IS THE PRICE RIGHT? PRICING FOR LONG TERM PROFITABILITY
Directory of Open Access Journals (Sweden)
Andrea Erika NYÁRÁDI
2007-01-01
Full Text Available The way how we choose our pricing strategy has a significant impact on company’s success. Nowadays companies more and more adopt a new way of thinking in pricing, namely pricing for a long term period in order to bring higher profitability, to build an efficient pricing strategy. Marketers have only recently begun to focus seriously on effective pricing. These companies are the so called progressive companies. They have begun doing more than just worrying about pricing. To increase profitability many are abandoning traditional reactive pricing procedures in favor of proactive pricing, making explicit corporate decisions to change their focus to growth in top-line sales to growth in profitability. The long-term implications of price strategies are still under-researched, and managers should be aware of shifts in customer reactions that may result from frequent adoption of certain strategies. The company pricing strategy should be seen in relation to developments in the company variables, internal ones (capital strength, competencies, organizational conditions, efficiency of the work force etc. as well as external ones (customers, competitors, the technological development etc., adopting strategic pricing. In this paper I will present the most effective pricing strategies leading to long term profitability, and also suggest practical conditions for pricing strategies to maximize profit in the long run.
Forecasting European thermal coal spot prices
Directory of Open Access Journals (Sweden)
Alicja Krzemień
2015-01-01
Finally, in order to analyse the time series model performance a Generalized Regression Neural Network (GRNN was used and its performance compared against the whole AR(2 process. Empirical results obtained confirmed that there is no statistically significant difference between both methods. The GRNN analysis also allowed pointing out the main drivers that move the European Thermal Coal Spot prices: crude oil, USD/CNY change and supply side drivers.
Price forecasting of day-ahead electricity markets using a hybrid forecast method
International Nuclear Information System (INIS)
Shafie-khah, M.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.
2011-01-01
Research highlights: → A hybrid method is proposed to forecast the day-ahead prices in electricity market. → The method combines Wavelet-ARIMA and RBFN network models. → PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. → One of the merits of the proposed method is lower need to the input data. → The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.
CSIR Research Space (South Africa)
Das, Sonali
2010-01-01
Full Text Available This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, the authors forecast house price inflation for five...
Directory of Open Access Journals (Sweden)
Yi Yang
2014-01-01
Full Text Available Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO based on the generalized autoregressive conditional heteroskedasticity (GARCH model and support vector machine (SVM to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models.
Directory of Open Access Journals (Sweden)
Hossein Naderi
2012-08-01
Full Text Available Stock market prediction is one of the most important interesting areas of research in business. Stock markets prediction is normally assumed as tedious task since there are many factors influencing the market. The primary objective of this paper is to forecast trend closing price movement of Tehran Stock Exchange (TSE using financial accounting ratios from year 2003 to year 2008. The proposed study of this paper uses two approaches namely Artificial Neural Networks and multi-layer perceptron. Independent variables are accounting ratios and dependent variable of stock price , so the latter was gathered for the industry of Motor Vehicles and Auto Parts. The results of this study show that neural networks models are useful tools in forecasting stock price movements in emerging markets but multi-layer perception provides better results in term of lowering error terms.
A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting
Weishang Guo; Zhenyu Zhao
2017-01-01
Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND) method, fruit fly optimization algorit...
Short-term natural gas consumption forecasting
International Nuclear Information System (INIS)
Potocnik, P.; Govekar, E.; Grabec, I.
2007-01-01
Energy forecasting requirements for Slovenia's natural gas market were investigated along with the cycles of natural gas consumption. This paper presented a short-term natural gas forecasting approach where the daily, weekly and yearly gas consumption were analyzed and the information obtained was incorporated into the forecasting model for hourly forecasting for the next day. The natural gas market depends on forecasting in order to optimize the leasing of storage capacities. As such, natural gas distribution companies have an economic incentive to accurately forecast their future gas consumption. The authors proposed a forecasting model with the following properties: two submodels for the winter and summer seasons; input variables including past consumption data, weather data, weather forecasts and basic cycle indexes; and, a hierarchical forecasting structure in which a daily model was used as the basis, with the hourly forecast obtained by modeling the relative daily profile. This proposed method was illustrated by a forecasting example for Slovenia's natural gas market. 11 refs., 11 figs
A model for Long-term Industrial Energy Forecasting (LIEF)
Energy Technology Data Exchange (ETDEWEB)
Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)
1992-02-01
The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.
A model for Long-term Industrial Energy Forecasting (LIEF)
Energy Technology Data Exchange (ETDEWEB)
Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))
1992-02-01
The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.
Directory of Open Access Journals (Sweden)
Jarmo Partanen
2013-11-01
Full Text Available A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and price spike predictions. The normal price module is a mixture of wavelet transform, linear AutoRegressive Integrated Moving Average (ARIMA and nonlinear neural network models. The probability of a price spike occurrence is produced by a compound classifier in which three single classification techniques are used jointly to make a decision. Combined with the spike value prediction technique, the output from the price spike module aims to provide a comprehensive price spike forecast. The overall electricity price forecast is formed as combined normal price and price spike forecasts. The forecast accuracy of the proposed method is evaluated with real data from the Finnish Nord Pool Spot day-ahead energy market. The proposed method provides significant improvement in both normal price and price spike prediction accuracy compared with some of the most popular forecast techniques applied for case studies of energy markets.
Modeling and forecasting electricity price jumps in the Nord Pool power market
DEFF Research Database (Denmark)
Knapik, Oskar
i) price drivers, ii) persistence, iii) seasonality of electricity prices. The models are shown to outperform commonly-used benchmark. The paper shows how crucial for price jumps forecasting is to incorporate additional knowledge on price drivers like loads, temperature and water reservoir level......For risk management traders in the electricity market are mainly interested in the risk of negative (drops) or of positive (spikes) price jumps, i.e. the sellers face the risk of negative price jumps while the buyers face the risk of positive price jumps. Understanding the mechanism that drive...... extreme prices and forecasting of the price jumps is crucial for risk management and market design. In this paper, we consider the problem of the impact of fundamental price drivers on forecasting of price jumps in NordPool intraday market. We develop categorical time series models which take into account...
Directory of Open Access Journals (Sweden)
Olalekan Oshodi
2017-09-01
Full Text Available The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box-Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models.
Building a House Prices Forecasting Model in Hong Kong
Directory of Open Access Journals (Sweden)
Xin Janet
2012-11-01
Full Text Available This paper builds a house prices forecasting model for private residential houses in HongKong, based on general macroeconomic indicators, housing related data and demographicfactors for the period of 1980 to 2001. A reduce form economic model has been derivedfrom a multiple regression analysis where three sets and eight models were derived foranalysis and comparison. It is found that household income, land supply, population andmovements in the Hang Seng Index play an important role in explaining house pricemovements in Hong Kong. In addition, political events, as identified, cannot be ignored.However, the results of the models are unstable. It is suggested that the OLS may nota best method for house prices model in Hong Kong situation. Alternative methods aresuggested.
Threshold Forecasting of Electricity Market Clearing Prices in Volatile Electricity Markets
Janjani, Arya
Forecasting the hourly electricity prices has been a topic of interest since the introduction of deregulation and competition in electricity markets. This thesis introduces the new idea of "threshold forecasting" or "classification" of market prices as a new approach to electricity price prediction, versus point forecasting of prices, where the exact values of future prices are determined. The motivation for this idea is that the accuracy level presented in the literature for point price forecasting methods is usually low and not all market participants need to know the exact values of prices. Support Vector Machines (SVMs) are used as the core classifier in this work, and two alternative classification models are proposed. The classification results are compared with those of other alternative classifiers as well as those obtained using point forecasting methods in the previous literature. These comparisons verify the effectiveness of the proposed models.
The economic benefit of short-term forecasting for wind energy in the UK electricity market
International Nuclear Information System (INIS)
Barthelmie, R.J.; Murray, F.; Pryor, S.C.
2008-01-01
In the UK market, the total price of renewable electricity is made up of the Renewables Obligation Certificate and the price achieved for the electricity. Accurate forecasting improves the price if electricity is traded via the power exchange. In order to understand the size of wind farm for which short-term forecasting becomes economically viable, we develop a model for wind energy. Simulations were carried out for 2003 electricity prices for different forecast accuracies and strategies. The results indicate that it is possible to increase the price obtained by around pound 5/MWh which is about 14% of the electricity price in 2003 and about 6% of the total price. We show that the economic benefit of using short-term forecasting is also dependant on the accuracy and cost of purchasing the forecast. As the amount of wind energy requiring integration into the grid increases, short-term forecasting becomes more important to both wind farm owners and the transmission/distribution operators. (author)
Determinants of Potato Prices and its Forecasting: A Case Study of Punjab, Pakistan
Anwar, Dr. Mumtaz; Shabbir, Dr. Ghulam; Shahid, M. Hassam; Samreen, Wajiha
2015-01-01
Potato figures among the principal crop in Pakistan. This paper describes the determinants of potato prices in Punjab, Pakistan. Annual data for the period 1998-2014 were analyzed to identify factors affecting the prices of potato. Results indicated that temperature and world oil prices were significantly affecting price. Seasonal variation of prices are also analyzed in this paper. This paper also use ARIMA and ARMA model to forecast the prices. These results suggest that temperature increas...
Ziel, Florian; Steinert, Rick; Husmann, Sven
2015-01-01
In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market we employ econometric models to incorporate the EXAA price and compare them with their counterparts without the price of the Austrian exchange. By employing a forecasting study...
Mixed price and load forecasting of electricity markets by a new iterative prediction method
International Nuclear Information System (INIS)
Amjady, Nima; Daraeepour, Ali
2009-01-01
Load and price forecasting are the two key issues for the participants of current electricity markets. However, load and price of electricity markets have complex characteristics such as nonlinearity, non-stationarity and multiple seasonality, to name a few (usually, more volatility is seen in the behavior of electricity price signal). For these reasons, much research has been devoted to load and price forecast, especially in the recent years. However, previous research works in the area separately predict load and price signals. In this paper, a mixed model for load and price forecasting is presented, which can consider interactions of these two forecast processes. The mixed model is based on an iterative neural network based prediction technique. It is shown that the proposed model can present lower forecast errors for both load and price compared with the previous separate frameworks. Another advantage of the mixed model is that all required forecast features (from load or price) are predicted within the model without assuming known values for these features. So, the proposed model can better be adapted to real conditions of an electricity market. The forecast accuracy of the proposed mixed method is evaluated by means of real data from the New York and Spanish electricity markets. The method is also compared with some of the most recent load and price forecast techniques. (author)
Directory of Open Access Journals (Sweden)
Jianzhou Wang
2014-01-01
Full Text Available Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO weight-determined combination models.” These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.
Influence of forecasting electricity prices in the optimization of complex hydrothermal systems
Bayón, L.; Suárez, P.; Matías, J. M.; Taboada, J.
2009-10-01
This paper proposes a new method for addressing the short-term optimal operation of a generation company, fully adapted to represent the characteristics of the new competitive markets. We propose an efficient and highly accurate novel method for next-day price forecasting. We model the functional time series with a linear autoregressive functional model which formulates the relationships between each daily function of prices and the functions of previous days. For the optimization problem (formulated within the framework of nonsmooth analysis using Pontryagin's Maximum Principle), we propose a new method that uses diverse mathematical techniques (the Shooting Method, Euler's Method, the Cyclic Coordinate Descent Method). These techniques are well known for the case of functions, but are adapted here to the case of functionals and are efficiently combined to provide a novel contribution. Finally, the paper presents the results of applying our method to a price-taker company in the Spanish electricity market.
Price strategy and pricing strategy: terms and content identification
Panasenko Tetyana
2015-01-01
The article is devoted to the terminology and content identification of seemingly identical concepts "price strategy" and "pricing strategy". The article contains evidence that the price strategy determines the direction, principles and procedure of implementing the company price policy and pricing strategy creates a set of rules and practical methods of price formation in accordance with the pricing strategy of the company.
Dynamical behavior of price forecasting in structures of group correlations
Lim, Kyuseong; Kim, Soo Yong; Kim, Kyungsik
2015-07-01
We investigate the prediction of the future prices from the structures and the networks of the companies in special financial groups. After the financial group network has been constructed from the value of the high cross-correlation, each company in a group is simulated and analyzed how it buys or sells stock is anaylzed and how it makes rational investments is forecasted. In the shortmemory behavior rather than the long-memory behavior, each company among a group can make a rational investment decision by using a stochastic evolution rule in the financial network. In particular, we simulate and analyze the investment situation in connection with the empirical data and the simulated result.
Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection
DEFF Research Database (Denmark)
Bork, Lasse; Møller, Stig Vinther
2015-01-01
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia...
Spatial Bayesian methods of forecasting house prices in six metropolitan areas of South Africa
CSIR Research Space (South Africa)
Gupta, R
2008-06-01
Full Text Available :07 to 2005:06. The authors then forecast one- to six-months-ahead house prices over the forecast horizon of 2005:07 to 2007:06. They then compare forecasts generated from the SBVAR's with those from an unrestricted Vector Autoregressive (VAR) and the Bayesian...
Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model
Directory of Open Access Journals (Sweden)
Ani Shabri
2014-01-01
Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.
International Nuclear Information System (INIS)
Kou, Peng; Liang, Deliang; Gao, Lin; Lou, Jianyong
2015-01-01
Highlights: • A novel active learning model for the probabilistic electricity price forecasting. • Heteroscedastic Gaussian process that captures the local volatility of the electricity price. • Variational Bayesian learning that avoids over-fitting. • Active learning algorithm that reduces the computational efforts. - Abstract: Electricity price forecasting is essential for the market participants in their decision making. Nevertheless, the accuracy of such forecasting cannot be guaranteed due to the high variability of the price data. For this reason, in many cases, rather than merely point forecasting results, market participants are more interested in the probabilistic price forecasting results, i.e., the prediction intervals of the electricity price. Focusing on this issue, this paper proposes a new model for the probabilistic electricity price forecasting. This model is based on the active learning technique and the variational heteroscedastic Gaussian process (VHGP). It provides the heteroscedastic Gaussian prediction intervals, which effectively quantify the heteroscedastic uncertainties associated with the price data. Because the high computational effort of VHGP hinders its application to the large-scale electricity price forecasting tasks, we design an active learning algorithm to select a most informative training subset from the whole available training set. By constructing the forecasting model on this smaller subset, the computational efforts can be significantly reduced. In this way, the practical applicability of the proposed model is enhanced. The forecasting performance and the computational time of the proposed model are evaluated using the real-world electricity price data, which is obtained from the ANEM, PJM, and New England ISO
Advances in electric power and energy systems load and price forecasting
2017-01-01
A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...
Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels
Directory of Open Access Journals (Sweden)
Taiyong Li
2016-12-01
Full Text Available Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD, adaptive particle swarm optimization (APSO, and relevance vector machine (RVM—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE, mean absolute percent error (MAPE, and directional statistic (Dstat, showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.
Forecasting electricity spot-prices using linear univariate time-series models
International Nuclear Information System (INIS)
Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael
2004-01-01
This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)
International Nuclear Information System (INIS)
Andalib, Arash; Atry, Farid
2009-01-01
The prediction of electricity prices is very important to participants of deregulated markets. Among many properties, a successful prediction tool should be able to capture long-term dependencies in market's historical data. A nonlinear autoregressive model with exogenous inputs (NARX) has proven to enjoy a superior performance to capture such dependencies than other learning machines. However, it is not examined for electricity price forecasting so far. In this paper, we have employed a NARX network for forecasting electricity prices. Our prediction model is then compared with two currently used methods, namely the multivariate adaptive regression splines (MARS) and wavelet neural network. All the models are built on the reconstructed state space of market's historical data, which either improves the results or decreases the complexity of learning algorithms. Here, we also criticize the one-step ahead forecasts for electricity price that may suffer a one-term delay and we explain why the mean square error criterion does not guarantee a functional prediction result in this case. To tackle the problem, we pursue multi-step ahead predictions. Results for the Ontario electricity market are presented
E, Jianwei; Bao, Yanling; Ye, Jimin
2017-10-01
As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.
The term structure of oil futures prices
International Nuclear Information System (INIS)
Gabillon, J.
1991-01-01
In recent years, there has been a massive development of derivative financial products in oil markets. The main interest came from large energy end-users who found in them a welcome opportunity to lock in fixed or maximum prices for their supplies over a period of time. Oil companies and oil traders were able to provide tailor-made swaps or options for the specific needs of the end-users. In this paper, we present a two-variable model of the term structures of futures prices and volatilities assuming that the spot and long-term prices of oil are stochastic, and are the main determinants of the convenience yield function. Although the resulting convenience yield is stochastic, the model admits an analytic formulation under some restrictions. (author)
A regime-switching stochastic volatility model for forecasting electricity prices
DEFF Research Database (Denmark)
Exterkate, Peter; Knapik, Oskar
In a recent review paper, Weron (2014) pinpoints several crucial challenges outstanding in the area of electricity price forecasting. This research attempts to address all of them by i) showing the importance of considering fundamental price drivers in modeling, ii) developing new techniques...... for probabilistic (i.e. interval or density) forecasting of electricity prices, iii) introducing an universal technique for model comparison. We propose new regime-switching stochastic volatility model with three regimes (negative jump, normal price, positive jump (spike)) where the transition matrix depends...
An empirical comparison of alternative schemes for combining electricity spot price forecasts
International Nuclear Information System (INIS)
Nowotarski, Jakub; Raviv, Eran; Trück, Stefan; Weron, Rafał
2014-01-01
In this comprehensive empirical study we critically evaluate the use of forecast averaging in the context of electricity prices. We apply seven averaging and one selection scheme and perform a backtesting analysis on day-ahead electricity prices in three major European and US markets. Our findings support the additional benefit of combining forecasts of individual methods for deriving more accurate predictions, however, the performance is not uniform across the considered markets and periods. In particular, equally weighted pooling of forecasts emerges as a simple, yet powerful technique compared with other schemes that rely on estimated combination weights, but only when there is no individual predictor that consistently outperforms its competitors. Constrained least squares regression (CLS) offers a balance between robustness against such well performing individual methods and relatively accurate forecasts, on average better than those of the individual predictors. Finally, some popular forecast averaging schemes – like ordinary least squares regression (OLS) and Bayesian Model Averaging (BMA) – turn out to be unsuitable for predicting day-ahead electricity prices. - Highlights: • So far the most extensive study on combining forecasts for electricity spot prices • 12 stochastic models, 8 forecast combination schemes and 3 markets considered • Our findings support the additional benefit of combining forecasts for deriving more accurate predictions • Methods that allow for unconstrained weights, such as OLS averaging, should be avoided • We recommend a backtesting exercise to identify the preferred forecast averaging method for the data at hand
Pricing Term Structure Risk in Futures Markets
Nijman, T.E.; de Roon, F.A.; Veld, C.H.
1996-01-01
One-period expected returns on futures contracts with di erent maturities di er because of risk premia in the spreads between futures and spot prices.We analyze the expected returns for futures contracts with di erent maturities using the information that is present in the current term structure of
Forecasting electricity spot market prices with a k-factor GIGARCH process
Energy Technology Data Exchange (ETDEWEB)
Diongue, Abdou Ka [Universite Gaston Berger de Saint-Louis, UFR SAT, BP 234, Saint-Louis Senegal and Research Fellow at Queensland University of Technology, 2 George Street, GPO Box 2434, Brisbane QLD 4001 (Australia); Guegan, Dominique [Paris School of economics, CES-MSE, Universite Paris1 Pantheon-Sorbonne, 106 boulevard de l' Hopital, 75647 Paris, Cedex 13 (France); Vignal, Bertrand [Ingenieur EDF R and D, 1 avenue du general de Gaulle, 92141 Clamart cedex (France)
2009-04-15
In this article, we investigate conditional mean and conditional variance forecasts using a dynamic model following a k-factor GIGARCH process. Particularly, we provide the analytical expression of the conditional variance of the prediction error. We apply this method to the German electricity price market for the period August 15, 2000-December 31, 2002 and we test spot prices forecasts until one-month ahead forecast. The forecasting performance of the model is compared with a SARIMA-GARCH benchmark model using the year 2003 as the out-of-sample. The proposed model outperforms clearly the benchmark model. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria. (author)
Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables
Zhang, Lu; Zhang, Junbiao; Xiong, Tao; Su, Chiao
2017-01-01
This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have...
U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change
Isengildina-Massa, Olga; MacDonald, Stephen
2009-01-01
The purpose of this study was to analyze structural changes that took place in the cotton industry in recent years and develop a statistical model that reflects the current drivers of U.S. cotton prices. Legislative changes authorized the U.S. Department of Agriculture to resume publishing cotton price forecasts for the first time in 79 years. In addition, systematic problems have become apparent in the forecasting models used by USDA and elsewhere, highlighting the need for an updated review...
International Nuclear Information System (INIS)
Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.
2015-01-01
Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.
Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market
Directory of Open Access Journals (Sweden)
Claudio Monteiro
2015-09-01
Full Text Available This paper presents the analysis of the importance of a set of explanatory (input variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL. The available input variables include extensive hourly time series records of weather forecasts, previous prices, and regional aggregation of power generations and power demands. The paper presents the comparisons of the forecasting results achieved with a model which includes all these available input variables (EMPF model with respect to those obtained by other forecasting models containing a reduced set of input variables. These comparisons identify the most important variables for forecasting purposes. In addition, a novel Reference Explanatory Model for Price Estimations (REMPE that achieves hourly price estimations by using actual power generations and power demands of such day is described in the paper, which offers the lowest limit for the forecasting error of the EMPF model. All the models have been implemented using the same technique (artificial neural networks and have been satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. The relative importance of each explanatory variable is identified for the day-ahead price forecasts in the MIBEL. The comparisons also allow outlining guidelines of the value of the different types of input information.
Controlling Electricity Consumption by Forecasting its Response to Varying Prices
DEFF Research Database (Denmark)
Corradi, Olivier; Ochsenfeld, Henning Peter; Madsen, Henrik
2013-01-01
In a real-time electricity pricing context where consumers are sensitive to varying prices, having the ability to anticipate their response to a price change is valuable. This paper proposes models for the dynamics of such price-response, and shows how these dynamics can be used to control...... electricity consumption using a one-way price signal. Estimation of the price-response is based on data measurable at grid level, removing the need to install sensors and communication devices between each individual consumer and the price-generating entity. An application for price-responsive heating systems...... is studied based on real data, before conducting a control by price experiment using a mixture of real and synthetic data. With the control objective of following a constant consumption reference, peak heating consumption is reduced by nearly 5%, and 11% of the mean daily heating consumption is shifted....
Short-term forecasting of internal migration.
Frees, E W
1993-11-01
A new methodological approach to the forecasting of short-term trends in internal migration in the United States is introduced. "Panel-data (or longitudinal-data) models are used to represent the relationship between destination-specific out-migration and several explanatory variables. The introduction of this methodology into the migration literature is possible because of some new and improved databases developed by the U.S. Bureau of the Census.... Data from the Bureau of Economic Analysis are used to investigate the incorporation of exogenous factors as variables in the model." The exogenous factors considered include employment and unemployment, income, population size of state, and distance between states. The author concludes that "when one...includes additional parameters that are estimable in longitudinal-data models, it turns out that there is little additional information in the exogenous factors that is useful for forecasting." excerpt
Short-term wind power combined forecasting based on error forecast correction
International Nuclear Information System (INIS)
Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng
2016-01-01
Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed
Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price
Yaziz, Siti Roslindar; Azizan, Noor Azlinna; Ahmad, Maizah Hura; Zakaria, Roslinazairimah; Agrawal, Manju; Boland, John
2015-02-01
Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.
A Study on the Determination of the World Crude Oil Price and Methods for Its Forecast
Energy Technology Data Exchange (ETDEWEB)
Kim, J.K. [Korea Energy Economics Institute, Euiwang (Korea)
2001-11-01
The primary purpose of this report is to provide the groundwork to develop the methods to forecast the world crude oil price. The methodology is used by both literature survey and empirical study. For this purpose, first of all, this report reviewed the present situation and the outlook of the world oil market based on oil demand, supply and prices. This analysis attempted to provide a deeper understanding to support the development of oil forecasting methods. The result of this review, in general, showed that the oil demand will be maintained annually at an average rate of around 2.4% under assumption that oil supply has no problem until 2020. The review showed that crude oil price will be a 3% increasing rate annually in the 1999 real term. This report used the contents of the summary review as reference data in order to link the KEEIOF model. In an effort to further investigate the contents of oil political economy, this report reviewed the articles of political economy about oil industry. It pointed out that the world oil industry is experiencing the change of restructuring oil industry after the Gulf War in 1990. The contents of restructuring oil industry are characterized by the 'open access' to resources not only in the Persian Gulf, but elsewhere in the world as well - especially the Caspian Sea Basin. In addition, the contents showed that the oil industries are shifted from government control to government and industry cooperation after the Gulf War. In order to examine the characters and the problems surrounding oil producing countries, this report described the model of OPEC behavior and strategy of oil management with political and military factors. Among examining the models of OPEC behavior, this report focused on hybrid model to explain OPEC behavior. In reviewing political and religious power structure in the Middle East, the report revealed that US emphasizes the importance of the Middle East for guaranteeing oil security. However, three
An Overview of Short-term Statistical Forecasting Methods
DEFF Research Database (Denmark)
Elias, Russell J.; Montgomery, Douglas C.; Kulahci, Murat
2006-01-01
An overview of statistical forecasting methodology is given, focusing on techniques appropriate to short- and medium-term forecasts. Topics include basic definitions and terminology, smoothing methods, ARIMA models, regression methods, dynamic regression models, and transfer functions. Techniques...
Formation and forecast of the daily price of the electric power in the chain Nare-Guatape-San Carlos
International Nuclear Information System (INIS)
Romero, Alejandro; Carvajal, Luis
2003-01-01
This work shows three different methodologies for the understanding and forecast of the electric energy prices in the chain Nare - Guatape - San Carlos: lineal multivariate model, autoregressive deterministic model and Fourier series decomposition. The electric energy price depends basically of the reservoir level and river flow, not only its own but the reservoir down and up, waters. About prices forecast, they can be modeled with an autoregressive process. Prices forecast follows the tendency and captures with acceptable precision the maximum prices due especially to the low hydrology and price variability for daily and weekly regulation reservoirs
Testing the rationality of DOE's energy price forecasts under asymmetric loss preferences
International Nuclear Information System (INIS)
Mamatzakis, E.; Koutsomanoli-Filippaki, A.
2014-01-01
This paper examines the rationality of the price forecasts for energy commodities of the United States Department of Energy's (DOE), departing from the common assumption in the literature that DOE's forecasts are based on a symmetric underlying loss function with respect to positive vs. negative forecast errors. Instead, we opt for the methodology of Elliott et al. (2005) that allows testing the joint hypothesis of an asymmetric loss function and rationality and reveals the underlying preferences of the forecaster. Results indicate the existence of asymmetries in the shape of the loss function for most energy categories with preferences leaning towards optimism. Moreover, we also examine whether there is a structural break in those preferences over the examined period, 1997–2012. - Highlights: • Examine the rationality of DOE energy forecasts. • Departing from a symmetric underlying loss function. • Asymmetries exist in most energy prices. • Preferences lean towards optimism. • Examine structural breaks in those preferences
Directory of Open Access Journals (Sweden)
Saber Talari
2017-11-01
Full Text Available Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA method and Radial Basis Function Neural Network (RBFN. To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.
DEFF Research Database (Denmark)
Khalid, Muhammad; Aguilera, Ricardo P.; Savkin, Andrey V.
2017-01-01
This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting...... Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity...... of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes...
DEFF Research Database (Denmark)
Khalid, Muhammad; Aguilera, Ricardo P.; Savkin, Andrey V.
2017-01-01
of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes......This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting...... Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity...
Electricity spot price forecasting in free power market
International Nuclear Information System (INIS)
Lilleberg, J.; Laitinen, E.K.
1998-01-01
Deregulation has brought many changes to the electricity market. Freedom of choice has been granted to both the consumers and the utilities. Consumers may choose the seller of their energy. Utilities have a wider array of sources to acquire their electricity from. Also the types of sales contracts used are changing to fill the needs of this new situation. The consumers' right to choose has introduced a new risk uncertainty of volume, which was not true during the times of monopoly. As sold volume is unsure and the energy is not sold on same terms as it is bought, a price risk has to be dealt with also. The electric utility has to realize this, select a risk level that suits its business strategy and optimize its actions according to the selected risk level. The number of participants will grow as the electricity market integrates into a common market for Scandinavia and even Europe. Big customers are also taking a more active role in the market, further increasing the number of participants. This makes old bilateral arrangements outdated. New tools are needed to control the new business environment. The goal of this project has been to develop a theoretical model to predict the price in the Finnish electricity exchange, El-Ex Oy. An extensive literature review was conducted in order to (1) examine the solutions in deregulation of electricity markets in other countries, esp. in Norway and UK, (2) find similarities and differences in electricity exchange and exchanges generally and (3) find major sources of problems and inefficiency in the market
Electricity spot price forecasting in free power market
Energy Technology Data Exchange (ETDEWEB)
Lilleberg, J.; Laitinen, E.K. [Vaasa Univ. (Finland)
1998-08-01
Deregulation has brought many changes to the electricity market. Freedom of choice has been granted to both the consumers and the utilities. Consumers may choose the seller of their energy. Utilities have a wider array of sources to acquire their electricity from. Also the types of sales contracts used are changing to fill the needs of this new situation. The consumers` right to choose has introduced a new risk uncertainty of volume, which was not true during the times of monopoly. As sold volume is unsure and the energy is not sold on same terms as it is bought, a price risk has to be dealt with also. The electric utility has to realize this, select a risk level that suits its business strategy and optimize its actions according to the selected risk level. The number of participants will grow as the electricity market integrates into a common market for Scandinavia and even Europe. Big customers are also taking a more active role in the market, further increasing the number of participants. This makes old bilateral arrangements outdated. New tools are needed to control the new business environment. The goal of this project has been to develop a theoretical model to predict the price in the Finnish electricity exchange, El-Ex Oy. An extensive literature review was conducted in order to (1) examine the solutions in deregulation of electricity markets in other countries, esp. in Norway and UK, (2) find similarities and differences in electricity exchange and exchanges generally and (3) find major sources of problems and inefficiency in the market
Yang, Yi; Dong, Yao; Chen, Yanhua; Li, Caihong
2014-01-01
Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting metho...
Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables
Directory of Open Access Journals (Sweden)
Lu Zhang
2017-01-01
Full Text Available This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR and particle swarm optimization (PSO, in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market.
Simultaneous day-ahead forecasting of electricity price and load in smart grids
International Nuclear Information System (INIS)
Shayeghi, H.; Ghasemi, A.; Moradzadeh, M.; Nooshyar, M.
2015-01-01
Highlights: • This paper presents a novel MIMO-based support vector machine for forecasting. • Considered uncertainties for better simulation for filtering in input data. • Used LSSVM technique for learning. • Proposed a new modification for standard artificial bee colony algorithm to optimize LSSVM engine. - Abstract: In smart grids, customers are promoted to change their energy consumption patterns by electricity prices. In fact, in this environment, the electricity price and load consumption are highly corrected such that the market participants will have complex model in their decisions to maximize their profit. Although the available forecasting mythologies perform well in electricity market by way of little or no load and price interdependencies, but cannot capture load and price dynamics if they exist. To overcome this shortage, a Multi-Input Multi-Output (MIMO) model is presented which can consider the correlation between electricity price and load. The proposed model consists of three components known as a Wavelet Packet Transform (WPT) to make valuable subsets, Generalized Mutual Information (GMI) to select best input candidate and Least Squares Support Vector Machine (LSSVM) based on MIMO model, called LSSVM-MIMO, to make simultaneous load and price forecasts. Moreover, the LSSVM-MIMO parameters are optimized by a novel Quasi-Oppositional Artificial Bee Colony (QOABC) algorithm. Some forecasting indices based on error factor are considered to evaluate the forecasting accuracy. Simulations carried out on New York Independent System Operator, New South Wales (NSW) and PJM electricity markets data, and showing that the proposed hybrid algorithm has good potential for simultaneous forecasting of electricity price and load
The Delicate Analysis of Short-Term Load Forecasting
Song, Changwei; Zheng, Yuan
2017-05-01
This paper proposes a new method for short-term load forecasting based on the similar day method, correlation coefficient and Fast Fourier Transform (FFT) to achieve the precision analysis of load variation from three aspects (typical day, correlation coefficient, spectral analysis) and three dimensions (time dimension, industry dimensions, the main factors influencing the load characteristic such as national policies, regional economic, holidays, electricity and so on). First, the branch algorithm one-class-SVM is adopted to selection the typical day. Second, correlation coefficient method is used to obtain the direction and strength of the linear relationship between two random variables, which can reflect the influence caused by the customer macro policy and the scale of production to the electricity price. Third, Fourier transform residual error correction model is proposed to reflect the nature of load extracting from the residual error. Finally, simulation result indicates the validity and engineering practicability of the proposed method.
An Overview of Short-term Statistical Forecasting Methods
DEFF Research Database (Denmark)
Elias, Russell J.; Montgomery, Douglas C.; Kulahci, Murat
2006-01-01
An overview of statistical forecasting methodology is given, focusing on techniques appropriate to short- and medium-term forecasts. Topics include basic definitions and terminology, smoothing methods, ARIMA models, regression methods, dynamic regression models, and transfer functions. Techniques...... for evaluating and monitoring forecast performance are also summarized....
Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Antonio J. Sanchez-Esguevillas
2013-03-01
Full Text Available Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc., which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN that performs Short-Term Load Forecasting (STLF. In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.
Short-term data forecasting based on wavelet transformation and chaos theory
Wang, Yi; Li, Cunbin; Zhang, Liang
2017-09-01
A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of “data nail” on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.
International Nuclear Information System (INIS)
Skiadopoulos, George; Chantziara, Thalia
2008-01-01
We investigate whether the daily evolution of the term structure of petroleum futures can be forecasted. To this end, the principal components analysis is employed. The retained principal components describe the dynamics of the term structure of futures prices parsimoniously and are used to forecast the subsequent daily changes of futures prices. Data on the New York Mercantile Exchange (NYMEX) crude oil, heating oil, gasoline, and the International Petroleum Exchange (IPE) crude oil futures are used. We find that the retained principal components have small forecasting power both in-sample and out-of-sample. Similar results are obtained from standard univariate and vector autoregression models. Spillover effects between the four petroleum futures markets are also detected. (author)
International Nuclear Information System (INIS)
Weiss, Martin; Patel, Martin K.; Junginger, Martin; Perujo, Adolfo; Bonnel, Pierre; Grootveld, Geert van
2012-01-01
Hybrid-electric vehicles (HEVs) and battery-electric vehicles (BEVs) are currently more expensive than conventional passenger cars but may become cheaper due to technological learning. Here, we obtain insight into the prospects of future price decline by establishing ex-post learning rates for HEVs and ex-ante price forecasts for HEVs and BEVs. Since 1997, HEVs have shown a robust decline in their price and price differential at learning rates of 7±2% and 23±5%, respectively. By 2010, HEVs were only 31±22 € 2010 kW −1 more expensive than conventional cars. Mass-produced BEVs are currently introduced into the market at prices of 479±171 € 2010 kW −1 , which is 285±213 € 2010 kW −1 and 316±209 € 2010 kW −1 more expensive than HEVs and conventional cars. Our forecast suggests that price breakeven with these vehicles may only be achieved by 2026 and 2032, when 50 and 80 million BEVs, respectively, would have been produced worldwide. We estimate that BEVs may require until then global learning investments of 100–150 billion € which is less than the global subsidies for fossil fuel consumption paid in 2009. These findings suggest that HEVs, including plug-in HEVs, could become the dominant vehicle technology in the next two decades, while BEVs may require long-term policy support. - Highlights: ► Learning rates for hybrid-electric and battery-electric vehicles. ► Prices and price differentials of hybrid-electric vehicles show a robust decline. ► Battery-electric vehicles may require policy support for decades.
Modeling and Forecasting Average Temperature for Weather Derivative Pricing
Directory of Open Access Journals (Sweden)
Zhiliang Wang
2015-01-01
Full Text Available The main purpose of this paper is to present a feasible model for the daily average temperature on the area of Zhengzhou and apply it to weather derivatives pricing. We start by exploring the background of weather derivatives market and then use the 62 years of daily historical data to apply the mean-reverting Ornstein-Uhlenbeck process to describe the evolution of the temperature. Finally, Monte Carlo simulations are used to price heating degree day (HDD call option for this city, and the slow convergence of the price of the HDD call can be found through taking 100,000 simulations. The methods of the research will provide a frame work for modeling temperature and pricing weather derivatives in other similar places in China.
International Nuclear Information System (INIS)
Amjady, Nima; Keynia, Farshid
2009-01-01
With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods. (author)
Electricity market price forecasting by grid computing optimizing artificial neural networks
Niimura, T.; Ozawa, K.; Sakamoto, N.
2007-01-01
This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources. The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using re...
Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.
Directory of Open Access Journals (Sweden)
Junghwan Jin
Full Text Available Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.
SHORT-TERM FORECASTING OF MORTGAGE LENDING
Directory of Open Access Journals (Sweden)
Irina V. Orlova
2013-01-01
Full Text Available The article considers the methodological and algorithmic problems arising in modeling and forecasting of time series of mortgage loans. Focuses on the processes of formation of the levels of time series of mortgage loans and the problem of choice and identification of models in the conditions of small samples. For forecasting options are selected and implemented a model of autoregressive and moving average, which allowed to obtain reliable forecasts.
Online Short-term Solar Power Forecasting
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours.......This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours....
Online Short-term Solar Power Forecasting
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
2011-01-01
This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours.......This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours....
Price Forecast Of Selected Staple Foodstuff In Borno State, Nigeria ...
African Journals Online (AJOL)
... wrongly predicted in the urban market. Also in the rural market, 73% of the prices were rightly predicted while 27% were wrongly predicted. The knowledge of this projection will help the policy makers in Borno state towards the achievement of efficient marketing strategies. Journal of Agriculture and Social Research Vol.
A mathematical model for stock price forecasting | Ogwuche | West ...
African Journals Online (AJOL)
) and the covariance (the volatility) of the change were computed leading to the formulation of the system of linear stochastic differential equations. To fit data to the model, changes in the prices of the stocks were studied for an average of 30 ...
Anghileri, D.; Castelletti, A.; Burlando, P.
2016-12-01
European energy markets have experienced dramatic changes in the last years because of the massive introduction of Variable Renewable Sources (VRSs), such as wind and solar power sources, in the generation portfolios in many countries. VRSs i) are intermittent, i.e., their production is highly variable and only partially predictable, ii) are characterized by no correlation between production and demand, iii) have negligible costs of production, and iv) have been largely subsidized. These features result in lower energy prices, but, at the same time, in increased price volatility, and in network stability issues, which pose a threat to traditional power sources because of smaller incomes and higher maintenance costs associated to a more flexible operation of power systems. Storage hydropower systems play an important role in compensating production peaks, both in term of excess and shortage of energy. Traditionally, most of the research effort in hydropower reservoir operation has focused on modeling and forecasting reservoir inflow as well as designing reservoir operation accordingly. Nowadays, price variability may be the largest source of uncertainty in the context of hydropower systems, especially when considering medium-to-large reservoirs, whose storage can easily buffer small inflow fluctuations. In this work, we compare the effects of uncertain inflow and energy price forecasts on hydropower production and profitability. By adding noise to historic inflow and price trajectories, we build a set of synthetic forecasts corresponding to different levels of predictability and assess their impact on reservoir operating policies and performances. The study is conducted on different hydropower systems, including storage systems and pumped-storage systems, with different characteristics, e.g., different inflow-capacity ratios. The analysis focuses on Alpine hydropower systems where the hydrological regime ranges from purely ice and snow-melt dominated to mixed snow
Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques
Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi
2017-08-01
The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.
Lago Garcia, J.; De Ridder, Fjo; Vrancx, Peter; De Schutter, B.H.K.
2018-01-01
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from
Forecasting of the industrial power consumption in the conditions of volatility price signals
Directory of Open Access Journals (Sweden)
Igor Aleksandrovich Baev
2012-12-01
Full Text Available Article is devoted to problems of purchase of the electric power in the wholesale market for the industry of Russia. Authors considered the mechanism of pricing and various combinations between the prices of the market for days forward and the prices of the balancing market. Favorable and adverseratios between the prices of the balancing market and submitted plans for power consumption are revealed. The urgency of forecasting of the industrial power consumption, allowing providing a sustainable development not only power supply systems and the power companies, but also region economy as a whole is proved. Recommendations about improvement of forecasting of the power consumption, based on the account not only the factors defining requirement for the electric power, but also factors considering tendencies of the balancing market are offered. As methods of forecasting sharing of methods of the regression analysis and method of expert evaluations is offered. Results of research will allow to increase accuracy of forecasting and to reduce financial losses not only at level of the concrete enterprises, but also at region level as a whole.
Lago Garcia, J.; De Ridder, Fjo; De Schutter, B.H.K.
2018-01-01
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning
International Nuclear Information System (INIS)
Maurice, J.
2001-01-01
The oil market is the most volatile of all markets, with the exception of the Nasdaq. It is also the biggest commodity market in the world. Therefore one cannot avoid forecasting oil prices, nor can one expect to avoid the forecasting errors that have been made in the past. In his report, Joel Maurice draws a distinction between the short term and the medium-long term in analysing the outlook for oil prices. (author)
Gu, Chenghong; Xie, Da; Sun, Junbo; Wang, Xitian; Ai, Qian
2015-01-01
This paper develops a discrete operation optimization model for combined heat and powers (CHPs) in deregulated energy markets to maximize owners' profits, where energy price forecasting is included. First, a single input and multi-output (SIMO) model for typical CHPs is established, considering the varying ratio between heat and electricity outputs at different loading levels. Then, the energy prices are forecasted with a gray forecasting model and revised in real-time based on the actual pri...
International Nuclear Information System (INIS)
Salahor, G.S.; Laughton, D.G.
1993-01-01
Methods that have been empirically validated in the analysis of short-term traded securities are adapted to evaluate long-term natural gas direct-sale contracts. A sample contract is examined from the perspective of the producer, and analyzed as a series of forward and option contracts. The assessment of contract value is based on the gas price forecast, the volatility in that forecast, and the valuation of risk caused by that volatility. The method presented allows the gas producer to quantify these elements, and to evaluate the variety of terms encountered in direct-sale natural gas agreements, including features such as load factors and penalty charges. The analysis uses as inputs a probabilistic price forecast and a determination of a price of risk for gas prices. Once the forecast volatility is derived from the probabilistic forecast, the forward contracts imbedded in the long-term gas contract can be valued with a risk-discounting model, and optional aspects can be evaluated using the Black-Scholes option pricing method. 10 refs., 3 figs., 2 tabs
Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost
Energy Technology Data Exchange (ETDEWEB)
Kim, Sung Ki; Ko, Won Il; Nam, Hyoon [Nuclear Fuel Cycle Analysis, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Kim, Chul Min; Chung, Yang Hon; Bang, Sung Sig [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)
2017-08-15
This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.
Iterative near-term ecological forecasting: Needs, opportunities, and challenges.
Dietze, Michael C; Fox, Andrew; Beck-Johnson, Lindsay M; Betancourt, Julio L; Hooten, Mevin B; Jarnevich, Catherine S; Keitt, Timothy H; Kenney, Melissa A; Laney, Christine M; Larsen, Laurel G; Loescher, Henry W; Lunch, Claire K; Pijanowski, Bryan C; Randerson, James T; Read, Emily K; Tredennick, Andrew T; Vargas, Rodrigo; Weathers, Kathleen C; White, Ethan P
2018-02-13
Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.
International Nuclear Information System (INIS)
Shayeghi, H.; Ghasemi, A.
2013-01-01
Highlights: • Presenting a hybrid CGSA-LSSVM scheme for price forecasting. • Considering uncertainties for filtering in input data and feature selection to improve efficiency. • Using DWT input featured LSSVM approach to classify next-week prices. • Used three real markets to illustrate performance of the proposed price forecasting model. - Abstract: At the present time, day-ahead electricity market is closely associated with other commodity markets such as fuel market and emission market. Under such an environment, day-ahead electricity price forecasting has become necessary for power producers and consumers in the current deregulated electricity markets. Seeking for more accurate price forecasting techniques, this paper proposes a new combination of a Feature Selection (FS) technique based mutual information (MI) technique and Wavelet Transform (WT) in this study. Moreover, in this paper a new modified version of Gravitational Search Algorithm (GSA) optimization based chaos theory, namely Chaotic Gravitational Search Algorithm (CGSA) is developed to find the optimal parameters of Least Square Support Vector Machine (LSSVM) to predict electricity prices. The performance and price forecast accuracy of the proposed technique is assessed by means of real data from Iran’s, Ontario’s and Spain’s price markets. The simulation results from numerical tables and figures in different cases show that the proposed technique increases electricity price market forecasting accuracy than the other classical and heretical methods in the scientific researches
Online short-term solar power forecasting
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
2009-01-01
This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 hours. The data used is fifteen-minute obser......This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 hours. The data used is fifteen......-minute observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques....... Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to two hours...
Forecasting of Market Clearing Price by Using GA Based Neural Network
Yang, Bo; Chen, Yun-Ping; Zhao, Zun-Lian; Han, Qi-Ye
Forecasting of Market Clearing Price (MCP) is important to economic benefits of electricity market participants. To accurately forecast MCP, a novel two-stage GA-based neural network model (GA-NN) is proposed. In the first stage, GA chromosome is designed into two parts: boolean coding part for neural network topology and real coding part for connection weights. By hybrid genetic operation of selection, crossover and mutation under the criterion of error minimization between the actual output and the desired output, optimal architecture of neural network is obtained. In the second stage, gradient learning algorithm with momentum rate is imposed on neural network with optimal architecture. After learning process, optimal connection weights are obtained. The proposed model is tested on MCP forecasting in California electricity market. The test results show that GA-NN has self-adaptive ability in its topology and connection weights and can obtain more accurate MCP forecasting values than BP neural network.
A fuzzy inference model for short-term load forecasting
International Nuclear Information System (INIS)
Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad
2009-01-01
This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes
A Simple Hybrid Model for Short-Term Load Forecasting
Directory of Open Access Journals (Sweden)
Suseelatha Annamareddi
2013-01-01
Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.
A New Strategy for Short-Term Load Forecasting
Directory of Open Access Journals (Sweden)
Yi Yang
2013-01-01
Full Text Available Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.
Market efficiency, cross hedging and price forecasts: California's natural-gas markets
International Nuclear Information System (INIS)
Woo, C.K.; Olson, A.; Horowitz, I.
2006-01-01
An extensive North American pipeline grid that physically integrates individual natural-gas markets, in conjunction with economic ties binding the California markets to those at Henry Hub, Louisiana and the New York mercantile exchange via an array of financial instruments, suggests that the spot prices at Henry Hub will impact those in California. We verify the suggestion via a partial-adjustment regression model, thus affirming that California traders can exploit the cross-hedging opportunities made available to them via market integration with Henry Hub, and that they can accurately forecast the price they will have to pay to meet future demand based solely on the price of futures at Henry Hub and the price of a California natural-gas basis swaps contract. (author)
A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting
Directory of Open Access Journals (Sweden)
Chengshi Tian
2018-03-01
Full Text Available Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.
Density Forecasts of Crude-Oil Prices Using Option-Implied and ARCH-Type Models
DEFF Research Database (Denmark)
Tsiaras, Leonidas; Høg, Esben
of derivative contracts. Risk-neutral densities, obtained from panels of crude-oil option prices, are adjusted to reflect real-world risks using either a parametric or a non-parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well...... as for regions and intervals that are of special interest for the economic agent. We find that non-parametric adjustments of risk-neutral density forecasts perform significantly better than their parametric counterparts. Goodness-of-fit tests and out-of-sample likelihood comparisons favor forecast densities...
Castro Heredia, L. M.; Suarez, F. I.; Fernandez, B.; Maass, T.
2016-12-01
For forecasting of water resources, weather outputs provide a valuable source of information which is available online. Compared to traditional ground-based meteorological gauges, weather forecasts data offer spatially and temporally continuous data not yet evaluated and used in the forecasting of water resources in mountainous regions in Chile. Nevertheless, the use of this non-conventional data has been limited or null in developing countries, basically because of the spatial resolution, despite the high potential in the management of water resources. The adequate incorporation of these data in hydrological models requires its evaluation while taking into account the features of river basins in mountainous regions. This work presents an integrated forecasting system which represents a radical change in the way of making the streamflow forecasts in Chile, where the snowmelt forecast is the principal component of water resources management. The integrated system is composed of a physically based hydrological model, which is the prediction tool itself, together with a methodology for remote sensing data gathering that allows feed the hydrological model in real time, and meteorological forecasts from NCEP-CFSv2. Previous to incorporation of meteorological forecasts into the hydrological model, the weather outputs were evaluated and downscaling using statistical downscaling methods. The hydrological forecasts were evaluated in two mountain basins in Chile for a term of six months for the snowmelt period. In every month an assimilation process was performed, and the hydrological forecast was improved. Each month, the snow cover area (from remote sensing) and the streamflow observed were used to assimilate the model parameters in order to improve the next hydrological forecast using meteorological forecasts. The operation of the system in real time shows a good agreement between the streamflow and the snow cover area observed. The hydrological model and the weather
Areekul, Phatchakorn; Senjyu, Tomonobu; Urasaki, Naomitsu; Yona, Atsushi
Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.
ℓ(p)-Norm multikernel learning approach for stock market price forecasting.
Shao, Xigao; Wu, Kun; Liao, Bifeng
2012-01-01
Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.
Zhang, Li
With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman
Directory of Open Access Journals (Sweden)
Yi Wang
2016-12-01
Full Text Available With the levels of confidence and system complexity, interval forecasts and entropy analysis can deliver more information than point forecasts. In this paper, we take receivers’ demands as our starting point, use the trade-off model between accuracy and informativeness as the criterion to construct the optimal confidence interval, derive the theoretical formula of the optimal confidence interval and propose a practical and efficient algorithm based on entropy theory and complexity theory. In order to improve the estimation precision of the error distribution, the point prediction errors are STRATIFIED according to prices and the complexity of the system; the corresponding prediction error samples are obtained by the prices stratification; and the error distributions are estimated by the kernel function method and the stability of the system. In a stable and orderly environment for price forecasting, we obtain point prediction error samples by the weighted local region and RBF (Radial basis function neural network methods, forecast the intervals of the soybean meal and non-GMO (Genetically Modified Organism soybean continuous futures closing prices and implement unconditional coverage, independence and conditional coverage tests for the simulation results. The empirical results are compared from various interval evaluation indicators, different levels of noise, several target confidence levels and different point prediction methods. The analysis shows that the optimal interval construction method is better than the equal probability method and the shortest interval method and has good anti-noise ability with the reduction of system entropy; the hierarchical estimation error method can obtain higher accuracy and better interval estimation than the non-hierarchical method in a stable system.
Artificial Neural Network Models for Forecasting Stock Price Index in Bombay Stock Exchange
Mohan Neeraj; Jha Pankaj; Laha, A. K.; Dutta, Goutam
2005-01-01
Artificial Neural Network (ANN) has been shown to be an efficient tool for non-parametric modeling of data in a variety of different contexts where the output is a non-linear function of the inputs. These include business forecasting, credit scoring, bond rating, business failure prediction, medicine, pattern recognition, and image processing. A large number of studies have been reported in the literature with reference to use of ANN in modeling stock prices in the western countries However, ...
Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network
Directory of Open Access Journals (Sweden)
Ching-Ping Wu
2012-11-01
Full Text Available Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market participants in developing their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using a principal component analysis (PCA network cascaded with a multi-layer feedforward (MLF network for forecasting LMPs in a day-ahead market. The PCA network extracts essential features from periodic information in the market. These features serve as inputs to the MLF network for forecasting LMPs. The historical LMPs in the PJM market are employed to test the proposed method. It is found that the proposed method is capable of forecasting day-ahead LMP values efficiently.
Short-Term Solar Collector Power Forecasting
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Perers, Bengt
2011-01-01
This paper describes a new approach to online forecasting of power output from solar thermal collectors. The method is suited for online forecasting in many applications and in this paper it is applied to predict hourly values of power from a standard single glazed large area flat plate collector....... The method is applied for horizons of up to 42 hours. Solar heating systems naturally come with a hot water tank, which can be utilized for energy storage also for other energy sources. Thereby such systems can become an important part of energy systems with a large share of uncontrollable energy sources......-adaptive linear models. The approach is similar to the one by Bacher et al. (2009), but contains additional effects due to differences between solar thermal collectors and photovoltaics. Numerical weather predictions provided by Danish Meteorological Institute are used as input. The applied models adapt over time...
International Nuclear Information System (INIS)
Wang, Jie; Wang, Jun
2016-01-01
In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural network architecture is established in this work which combines Multilayer perception and ERNN (Elman recurrent neural networks) with stochastic time effective function. ERNN is a time-varying predictive control system and is developed with the ability to keep memory of recent events in order to predict future output. The stochastic time effective function represents that the recent information has a stronger effect for the investors than the old information. With the established model the empirical research has a good performance in testing the predictive effects on four different time series indices. Compared to other models, the present model is possible to evaluate data from 1990s to today with extreme accuracy and speedy. The applied CID (complexity invariant distance) analysis and multiscale CID analysis, are provided as the new useful measures to evaluate a better predicting ability of the proposed model than other traditional models. - Highlights: • A new forecasting model is developed by a random Elman recurrent neural network. • The forecasting accuracy of crude oil price fluctuations is improved by the model. • The forecasting results of the proposed model are more accurate than compared models. • Two new distance analysis methods are applied to confirm the predicting results.
In search of the elusive long-term price
International Nuclear Information System (INIS)
Connor, M.J.; Combs, J.
1989-01-01
The Uranium Institute, WNFM, and past USCEA sessions described and compared existing price reporting systems. The McGraw-Hill conference led to a rather heated discussion as to the propriety of spot prices having the influence they do on amounts paid in long-term contracts. The Ux representative proposed a future's market as a way that producers could hedge against some of the uncertainty of volatile spot market. In discussing the search for the elusive long-term price, there are two interrelated issues. The first is obvious-the search for a starting or initializing price that is representative of recently-signed or pending long-term contracts. The second is less obvious, but perhaps more important-the search for a successful mechanism for determining later delivery values in long-term contracts. This paper addresses the question of pricing mechanisms first
Short-term forecasting model for aggregated regional hydropower generation
International Nuclear Information System (INIS)
Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo
2014-01-01
Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations
Effective Short-term Forecasting of Wind Farms Power
Directory of Open Access Journals (Sweden)
Elżbieta Bogalecka
2015-09-01
Full Text Available Forecasting a specific wind farm’s (WF generation capacity within a 24 hour perspective requires both a reliable forecast of wind, as well as supporting tools. This tool is a dedicated model of wind farm power. This model should include not only general rules of wind to mechanical energy conversion, but also the farm’s specific features. There are many factors that influence a farm’s generation capacity, and any forecast of it, even with an accurate weather forecast, carries error. This paper presents analytical, statistical, and neuron models of wind farm power. The study is based on data from a real wind farm. Most attention is paid to the neuron models, due to a neuron network’s capability to restore farm-specific details. The research aims to answer the headline question: whether and to what extent a wind farm’s power can be forecast short-term?
Iterative near-term ecological forecasting: Needs, opportunities, and challenges
Dietze, Michael C.; Fox, Andrew; Beck-Johnson, Lindsay; Betancourt, Julio L.; Hooten, Mevin B.; Jarnevich, Catherine S.; Keitt, Timothy H.; Kenney, Melissa A.; Laney, Christine M.; Larsen, Laurel G.; Loescher, Henry W.; Lunch, Claire K.; Pijanowski, Bryan; Randerson, James T.; Read, Emily; Tredennick, Andrew T.; Vargas, Rodrigo; Weathers, Kathleen C.; White, Ethan P.
2018-01-01
Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.
Xiong, Tao; Bao, Yukun; Hu, Zhongyi
2014-01-01
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furthermore, this study proposes a firefly algorithm (FA)-based approach, built on the established MSVR, for...
Synthetic river flow time series generator for dispatch and spot price forecast
Energy Technology Data Exchange (ETDEWEB)
Flores, R.A. [Chalmers Univ. of Technology, Gothenburg (Sweden). Signal Processing Dept.; Szczupak, J. [Pontifical Catholic Univ., Rio de Janeiro (Brazil). Electrical Engineering Dept.; Pinto, L. [Engenho, Rio de Janeiro (Brazil)
2007-07-01
Decision-making in electricity markets is complicated by uncertainties in demand growth, power supplies and fuel prices. In Peru, where the electrical power system is highly dependent on water resources at dams and river flows, hydrological uncertainties play a primary role in planning, price and dispatch forecast. This paper proposed a signal processing method for generating new synthetic river flow time series as a support for planning and spot market price forecasting. River flow time series are natural phenomena representing a continuous-time domain process. As an alternative synthetic representation of the original river flow time series, this proposed signal processing method preserves correlations, basic statistics and seasonality. It takes into account deterministic, periodic and non periodic components such as those due to the El Nino Southern Oscillation phenomenon. The new synthetic time series has many correlations with the original river flow time series, rendering it suitable for possible replacement of the classical method of sorting historical river flow time series. As a dispatch and planning approach to spot pricing, the proposed method offers higher accuracy modeling by decomposing the signal into deterministic, periodic, non periodic and stochastic sub signals. 4 refs., 4 tabs., 13 figs.
Synthetic river flow time series generator for dispatch and spot price forecast
International Nuclear Information System (INIS)
Flores, R.A.
2007-01-01
Decision-making in electricity markets is complicated by uncertainties in demand growth, power supplies and fuel prices. In Peru, where the electrical power system is highly dependent on water resources at dams and river flows, hydrological uncertainties play a primary role in planning, price and dispatch forecast. This paper proposed a signal processing method for generating new synthetic river flow time series as a support for planning and spot market price forecasting. River flow time series are natural phenomena representing a continuous-time domain process. As an alternative synthetic representation of the original river flow time series, this proposed signal processing method preserves correlations, basic statistics and seasonality. It takes into account deterministic, periodic and non periodic components such as those due to the El Nino Southern Oscillation phenomenon. The new synthetic time series has many correlations with the original river flow time series, rendering it suitable for possible replacement of the classical method of sorting historical river flow time series. As a dispatch and planning approach to spot pricing, the proposed method offers higher accuracy modeling by decomposing the signal into deterministic, periodic, non periodic and stochastic sub signals. 4 refs., 4 tabs., 13 figs
Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm
International Nuclear Information System (INIS)
Yu, Lean; Wang, Shouyang; Lai, Kin Keung
2008-01-01
In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)
Directory of Open Access Journals (Sweden)
Marcela Lascsáková
2015-09-01
Full Text Available In the paper the numerical model based on the exponential approximation of commodity stock exchanges was derived. The price prognoses of aluminium on the London Metal Exchange were determined as numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate the idea of the modification of the initial condition value by the stock exchange was realized. By having analyzed the forecasting success of the chosen initial condition drift types, the initial condition drift providing the most accurate prognoses for the commodity price movements was determined. The suggested modification of the original model made the commodity price prognoses more accurate.
76 FR 9696 - Equipment Price Forecasting in Energy Conservation Standards Analysis
2011-02-22
... sufficient savings to warrant delaying or altering purchases (e.g. an inefficient ventilation fan in a new... prices and costs may, in many cases, over- estimate long-term appliance and equipment price trends... in fact trend downward over time according to ``learning'' or ``experience'' curves. A draft paper...
Short-Term Wind Speed Forecasting for Power System Operations
Zhu, Xinxin
2012-04-01
The emphasis on renewable energy and concerns about the environment have led to large-scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High-quality short-term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented. © 2012 The Authors. International Statistical Review © 2012 International Statistical Institute.
Short term solar radiation forecasting: Island versus continental sites
International Nuclear Information System (INIS)
Boland, John; David, Mathieu; Lauret, Philippe
2016-01-01
Due its intermittency, the large-scale integration of solar energy into electricity grids is an issue and more specifically in an insular context. Thus, forecasting the output of solar energy is a key feature to efficiently manage the supply-demand balance. In this paper, three short term forecasting procedures are applied to island locations in order to see how they perform in situations that are potentially more volatile than continental locations. Two continental locations, one coastal and one inland are chosen for comparison. At the two time scales studied, ten minute and hourly, the island locations prove to be more difficult to forecast, as shown by larger forecast errors. It is found that the three methods, one purely statistical combining Fourier series plus linear ARMA models, one combining clear sky index models plus neural net models, and a third using a clear sky index plus ARMA, give similar forecasting results. It is also suggested that there is great potential of merging modelling approaches on different horizons. - Highlights: • Solar energy forecasting is more difficult for insular than continental sites. • Fourier series plus linear ARMA models are one forecasting method tested. • Clear sky index models plus neural net models are also tested. • Clear sky index models plus linear ARMA is also an option. • All three approaches have similar skill.
A computationally efficient electricity price forecasting model for real time energy markets
International Nuclear Information System (INIS)
Feijoo, Felipe; Silva, Walter; Das, Tapas K.
2016-01-01
Highlights: • A fast hybrid forecast model for electricity prices. • Accurate forecast model that combines K-means and machine learning techniques. • Low computational effort by elimination of feature selection techniques. • New benchmark results by using market data for year 2012 and 2015. - Abstract: Increased significance of demand response and proliferation of distributed energy resources will continue to demand faster and more accurate models for forecasting locational marginal prices. This paper presents such a model (named K-SVR). While yielding prediction accuracy comparable with the best known models in the literature, K-SVR requires a significantly reduced computational time. The computational reduction is attained by eliminating the use of a feature selection process, which is commonly used by the existing models in the literature. K-SVR is a hybrid model that combines clustering algorithms, support vector machine, and support vector regression. K-SVR is tested using Pennsylvania–New Jersey–Maryland market data from the periods 2005–6, 2011–12, and 2014–15. Market data from 2006 has been used to measure performance of many of the existing models. Authors chose these models to compare performance and demonstrate strengths of K-SVR. Results obtained from K-SVR using the market data from 2012 and 2015 are new, and will serve as benchmark for future models.
Directory of Open Access Journals (Sweden)
O. V. Russkov
2015-01-01
Full Text Available The article considers a hot issue to forecast electric power demand amounts and prices for the entities of wholesale electricity market (WEM, which are in capacity of a large user with production technology requirements prevailing over hourly energy planning ones. An electric power demand of such entities is on irregular schedule. The article analyses mathematical models, currently applied to forecast demand amounts and prices. It describes limits of time-series models and fundamental ones in case of hourly forecasting an irregular demand schedule of the electricity market entity. The features of electricity trading at WEM are carefully analysed. Factors that influence on irregularity of demand schedule of the metallurgical plant are shown. The article proposes method for the qualitative forecast of market price ratios as a tool to reduce a dependence on the accuracy of forecasting an irregular schedule of demand. It describes the differences between the offered method and the similar ones considered in research studies and scholarly works. The correlation between price ratios and relaxation in the requirements for the forecast accuracy of the electric power consumption is analysed. The efficiency function of forecast method is derived. The article puts an increased focus on description of the mathematical model based on the method of qualitative forecast. It shows main model parameters and restrictions the electricity market imposes on them. The model prototype is described as a programme module. Methods to assess an effectiveness of the proposed forecast model are examined. The positive test results of the model using JSC «Volzhsky Pipe Plant» data are given. A conclusion is drawn concerning the possibility to decrease dependence on the forecast accuracy of irregular schedule of entity’s demand at WEM. The effective trading tool has been found for the entities of irregular demand schedule at WEM. The tool application allows minimizing cost
Short-term Power Load Forecasting Based on Balanced KNN
Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei
2018-03-01
To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.
Online short-term forecast of greenhouse heat load using a weather forecast service
DEFF Research Database (Denmark)
Vogler-Finck, P. J.C.; Bacher, P.; Madsen, Henrik
2017-01-01
In some district heating systems, greenhouses represent a significant share of the total load, and can lead to operational challenges. Short term load forecast of such consumers has a strong potential to contribute to the improvement of the overall system efficiency. This work investigates...... the performance of recursive least squares for predicting the heat load of individual greenhouses in an online manner. Predictor inputs (weekly curves terms and weather forecast inputs) are selected in an automated manner using a forward selection approach. Historical load measurements from 5 Danish greenhouses...... with different operational characteristics were used, together with weather measurements and a weather forecast service. It was found that these predictors of reduced complexity and computational load performed well at capturing recurring load profiles, but not fast frequency random changes. Overall, the root...
SHORT-TERM BAYESIAN INFLATION FORECASTING FOR TUNISIA: SOME EMPIRICAL EVIDENCE
Directory of Open Access Journals (Sweden)
Ahlem DAHEM
2016-02-01
Full Text Available In order to explain clearly inflation forecasting and the dynamic of Tunisian prices, this paper uses two econometric approaches, the Standard VAR and Bayesian VAR, to assess three models for predicting inflation, the mark-up model, the monetary model and Phillips curve over the period 1990 Q1 – 2013 Q4.In order to compare predictions, an out-of-sample estimation was conducted. We used the structural break test of Bai &Perron (1998, 2003 and the RMSE criterion for both inflation indices: CPI and PPI. We found that the BVECM mark-up model is best suited to forecast inflation for Tunisia. Our conclusions corroborate the literature of Bayesian VAR forecasting. Our findings indicate that the models which incorporate more economic information outperform the benchmark autoregressive models (AR (1 and AR (2. The results reveal that forecasting with the BVECM markup model leads to a reduction in forecasting error compared to the other models. The results of the study are relevant to decision-makers to predict inflation in the short- and long-terms in Tunisia and may help them adopt the appropriate strategies to contain inflation.
On the internal consistency of the term structure of forecasts of housing starts
DEFF Research Database (Denmark)
Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.
2013-01-01
We use the term structure of forecasts of housing starts to test for rationality of forecasts. Our test is based on the idea that short-term and long-term forecasts should be internally consistent. We test the internal consistency of forecasts using data for Australia, Canada, Japan and the United...
Forecasting of palm oil price in Malaysia using linear and nonlinear methods
Nor, Abu Hassan Shaari Md; Sarmidi, Tamat; Hosseinidoust, Ehsan
2014-09-01
The first question that comes to the mind is: "How can we predict the palm oil price accurately?" This question is the authorities, policy makers and economist's question for a long period of time. The first reason is that in the recent years Malaysia showed a comparative advantage in palm oil production and has become top producer and exporter in the world. Secondly, palm oil price plays significant role in government budget and represents important source of income for Malaysia, which potentially can influence the magnitude of monetary policies and eventually have an impact on inflation. Thirdly, knowledge on the future trends would be helpful in the planning and decision making procedures and will generate precise fiscal and monetary policy. Daily data on palm oil prices along with the ARIMA models, neural networks and fuzzy logic systems are employed in this paper. Empirical findings indicate that the dynamic neural network of NARX and the hybrid system of ANFIS provide higher accuracy than the ARIMA and static neural network for forecasting the palm oil price in Malaysia.
Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian
Teneng, Dean
2013-09-01
We fit the normal inverse Gaussian(NIG) distribution to foreign exchange closing prices using the open software package R and select best models by Käärik and Umbleja (2011) proposed strategy. We observe that daily closing prices (12/04/2008 - 07/08/2012) of CHF/JPY, AUD/JPY, GBP/JPY, NZD/USD, QAR/CHF, QAR/EUR, SAR/CHF, SAR/EUR, TND/CHF and TND/EUR are excellent fits while EGP/EUR and EUR/GBP are good fits with a Kolmogorov-Smirnov test p-value of 0.062 and 0.08 respectively. It was impossible to estimate normal inverse Gaussian parameters (by maximum likelihood; computational problem) for JPY/CHF but CHF/JPY was an excellent fit. Thus, while the stochastic properties of an exchange rate can be completely modeled with a probability distribution in one direction, it may be impossible the other way around. We also demonstrate that foreign exchange closing prices can be forecasted with the normal inverse Gaussian (NIG) Lévy process, both in cases where the daily closing prices can and cannot be modeled by NIG distribution.
Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model
Haixiang Zang; Lei Fan; Mian Guo; Zhinong Wei; Guoqiang Sun; Li Zhang
2016-01-01
Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EE...
International Nuclear Information System (INIS)
Meade, Nigel
2010-01-01
For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation processes with GARCH and no mean reversion. (author)
Directory of Open Access Journals (Sweden)
Da Liu
2013-01-01
Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.
Directory of Open Access Journals (Sweden)
Chenghong Gu
2015-12-01
Full Text Available This paper develops a discrete operation optimization model for combined heat and powers (CHPs in deregulated energy markets to maximize owners’ profits, where energy price forecasting is included. First, a single input and multi-output (SIMO model for typical CHPs is established, considering the varying ratio between heat and electricity outputs at different loading levels. Then, the energy prices are forecasted with a gray forecasting model and revised in real-time based on the actual prices by using the least squares method. At last, a discrete optimization model and corresponding dynamic programming algorithm are developed to design the optimal operation strategies for CHPs in real-time. Based on the forecasted prices, the potential operating strategy which may produce the maximum profits is pre-developed. Dynamic modification is then conducted to adjust the pre-developed operating strategy after the actual prices are known. The proposed method is implemented on a 1 MW CHP on a typical day. Results show the optimized profits comply well with those derived from real-time prices after considering dynamic modification process.
Short-term integrated forecasting system : 1993 model documentation report
1993-12-01
The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the U.S. Energy Department (DOE) developed the STIFS model to generate shor...
Short-term load forecasting of power system
Xu, Xiaobin
2017-05-01
In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.
Short term load forecasting using fuzzy neural networks
Energy Technology Data Exchange (ETDEWEB)
Bakirtzis, A.G.; Theocharis, J.B.; Kiartzis, S.J.; Satsios, K.J. [Aristotle Univ. of Thessaloniki (Greece). Dept. of Electrical and Computer Engineering
1995-08-01
This paper presents the development of a fuzzy system for short term load forecasting. The fuzzy system has the network structure and the training procedure of a neural network and is called Fuzzy Neural Network (FNN). A FNN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that the output of the FNN adequately matches the available historical load data. Once trained, the FNN can be used to forecast future loads. Test results show that the FNN can forecast future loads with an accuracy comparable to that of neural networks, while its training is much faster than that of neural networks.
On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models
Fay, D.; Ringwood, John; Condon, M.
2004-01-01
Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...
Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model
Zhu, Qing; Zou, Yingchao; Lai, Kin Keung
2014-01-01
As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations. PMID:25061614
Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
Directory of Open Access Journals (Sweden)
Federico Divina
2018-04-01
Full Text Available The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.
A novel economy reflecting short-term load forecasting approach
International Nuclear Information System (INIS)
Lin, Cheng-Ting; Chou, Li-Der
2013-01-01
Highlights: ► We combine MA line of TAIEX and SVR to overcome the load demands over-prediction problems caused by the economic downturn. ► The Taiwan island-wide electricity power system was used as the case study. ► Short- to middle-term MA lines of TAIEX are found to be good economic input variables for load forecasting models. - Abstract: The global economic downturn in 2008 and 2009, which was spurred by the bankruptcy of Lehman Brothers, sharply reduced the demand for electricity load. Conventional load-forecasting approaches were unable to respond to sudden changes in the economy, because these approaches do not consider the effect of economic factors. Therefore, the over-prediction problem occurred. To overcome this problem, this paper proposes a novel, economy-reflecting, short-term load forecasting (STLF) approach based on theories of moving average (MA) line of stock index and machine learning. In this approach, the stock indices decision model is designed to reflect fluctuations in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) series, which is selected as an optimal input variable in support vector regression load forecasting model at an appropriate timing. The Taiwan island-wide hourly electricity load demands from 2008 to 2010 are used as the case study for performance benchmarking. Results show that the proposed approach with a 60-day MA of the TAIEX as economic learning pattern achieves good forecasting performance. It outperforms the conventional approach by 29.16% on average during economic downturn-affected days. Overall, the proposed approach successfully overcomes the over-prediction problems caused by the economic downturn. To the best of our knowledge, this paper is the first attempt to apply MA line theory of stock index on STLF.
Neural Network-based Load Forecasting and Error Implication for Short-term Horizon
Khuntia, S.R.; Rueda Torres, José L.; van der Meijden, M.A.M.M.
2016-01-01
Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often
Two empirical models for short-term forecast of Kp
Luo, B.; Liu, S.; Gong, J.
2017-03-01
In this paper, two empirical models are developed for short-term forecast of the Kp index, taking advantage of solar wind-magnetosphere coupling functions proposed by the research community. Both models are based on the data for years 1995 to 2004. Model 1 mainly uses solar wind parameters as the inputs, while model 2 also utilizes the previous measured Kp value. Finally, model 1 predicts Kp with a linear correlation coefficient (r) of 0.91, a prediction efficiency (PE) of 0.81, and a root-mean-square (RMS) error of 0.59. Model 2 gives an r of 0.92, a PE of 0.84, and an RMS error of 0.57. The two models are validated through out-of-sample test for years 2005 to 2013, which also yields high forecast accuracy. Unlike in the other models reported in the literature, we are taking the response time of the magnetosphere to external solar wind at the Earth explicitly in the modeling. Statistically, the time delay in the models turns out to be about 30 min. By introducing this term, both the accuracy and lead time of the model forecast are improved. Through verification and validation, the models can be used in operational geomagnetic storm warnings with reliable performance.
Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques
Monteiro, Claudio; Fernandez-Jimenez, L. Alfredo; Ramirez-Rosado, Ignacio J.; Muñoz-Jimenez, Andres; Lara-Santillan, Pedro M.
2013-01-01
We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adj...
Evaluation of short-term weather forecasts in South Africa | Banitz ...
African Journals Online (AJOL)
In this paper a brief overview will be given for the reasons for doing evaluations of short-term weather forecasts as well as the methodology thereof. Short-term weather forecasts are defined as a forecast valid for the current day as well as the next day. In other words up to 48 h ahead. Results are given for South African ...
Effects of long-term price increases for oil
International Nuclear Information System (INIS)
Voehringer, F.; Mueller, A.; Boehringer, C.
2007-03-01
This comprehensive report for the Swiss Federal Office of Energy (SFOE) takes a look at the effects of higher oil prices in the long-term. Scenarios examined include those with high oil prices of 80 to 140 dollars per barrel and those with drastic shortages resulting from peak extraction in the years 2010 and 2020. Long-term economic balances form the basis of the report, short-term influences and psychological effects are not addressed. The possible dangers for the earth's climate caused by the substitution of oil by coal-based products are discussed, as well as the sequestration of carbon dioxide. Ethanol and the associated conflicts of land use are examined and the decreasing cost-effectiveness of co-generation power generation is looked at. Alternatives such as atomic power, hydropower, solar energy, geothermal energy, biogas and wind power are discussed. The effect of the changing energy scene on economic growth and welfare aspects in Switzerland are examined. The authors conclude that high oil prices have considerable impacts on the economy and are not a substitute for an internationally co-ordinated climate policy
Directory of Open Access Journals (Sweden)
Ping Jiang
2016-08-01
Full Text Available The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist in the literature, there have been few investigations on improving the forecasting effectiveness of electricity price from the perspective of reducing the volatility of data with satisfactory accuracy. Based on reducing the volatility of the electricity price and the forecasting nature of the radial basis function network (RBFN, this paper successfully develops a two-stage model to forecast the day-ahead electricity price, of which the first stage is particle swarm optimization (PSO-core mapping (CM with self-organizing-map and fuzzy set (PCMwSF, and the second stage is selection rule (SR. The PCMwSF stage applies CM, fuzzy set and optimized weights to obtain the future price, and the SR stage is inspired by the forecasting nature of RBFN and effectively selects the best forecast during the test period. The proposed model, i.e., CM-PCMwSF-SR, not only overcomes the difficulty of reducing the high volatility of the electricity price but also leads to a superior forecasting effectiveness than benchmarks.
Pyo, Sujin; Lee, Jaewook; Cha, Mincheol; Jang, Huisu
2017-01-01
The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.
Directory of Open Access Journals (Sweden)
Sujin Pyo
Full Text Available The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200 prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.
Directory of Open Access Journals (Sweden)
Sayed Mahdi Mostafavi
2016-07-01
Full Text Available Electrical energy is as one of the important effective factors on economic growth and development. In recent decades, numerous studies in different countries to estimate and forecast electricity demand in different parts of the economy have been made. In this paper, using the method ARDL, estimation and forecasting of electricity demand in the services sector of Iran are determined for the time period from 1983 to 2012. Estimated equations show that the added value of the services sector and a significant positive impact on the demand for electricity in this sector. The price elasticity for services sector is smaller than 1 due to low electricity prices and subsidized electricity. Hence, electricity prices have little impact on the demand for electricity. The results of the estimate represents a long-term relationship between the variables in the services sector. In this paper, based on amendments to the law on subsidies and estimated values, anticipated electricity demand until the end of the fifth development plan was carried out. The results indicate an increase in power consumption in the services sector.
Considering extraction constraints in long-term oil price modelling
International Nuclear Information System (INIS)
Rehrl, Tobias; Friedrich, Rainer; Voss, Alfred
2005-01-01
Apart from divergence about the remaining global oil resources, the peak oil discussion can be reduced to a dispute about the time rate at which these resources can be supplied. On the one hand it is problematic to project oil supply trends without taking both - prices as well as supply costs - explicitly into account. On the other hand are supply cost estimates however itself heavily dependent on the underlying extraction rates and are actually only valid within a certain business-as-usual extraction rate scenario (which itself is the task to determine). In fact, even after having applied enhanced recovery technologies, the rate at which an oil field can be exploited is quite restricted. Above a certain level an additional extraction rate increase can only be costly achieved at risks of losses in the overall recoverable amounts of the oil reservoir and causes much higher marginal cost. This inflexibility in extraction can be overcome in principle by the access to new oil fields. This indicates why the discovery trend may roughly form the long-term oil production curve, at least for price-taking suppliers. The long term oil discovery trend itself can be described as a logistic process with the two opposed effects of learning and depletion. This leads to the well-known Hubbert curve. Several attempts have been made to incorporate economic variables econometrically into the Hubbert model. With this work we follow a somewhat inverse approach and integrate Hubbert curves in our Long-term Oil Price and EXtraction model LOPEX. In LOPEX we assume that non-OPEC oil production - as long as the oil can be profitably discovered and extracted - is restricted to follow self-regulative discovery trends described by Hubbert curves. Non-OPEC production in LOPEX therefore consists of those Hubbert cycles that are profitable, depending on supply cost and price. Endogenous and exogenous technical progress is extra integrated in different ways. LOPEX determines extraction and price
Estimating the commodity market price of risk for energy prices
International Nuclear Information System (INIS)
Kolos, Sergey P.; Ronn, Ehud I.
2008-01-01
The purpose of this paper is to estimate the ''market price of risk'' (MPR) for energy commodities, the ratio of expected return to standard deviation. The MPR sign determines whether energy forward prices are upward- or downward-biased predictors of expected spot prices. We estimate MPRs using spot and futures prices, while accounting for the Samuelson effect. We find long-term MPRs generally positive and short-term negative, consistent with positive energy betas and hedging, respectively. In spot electricity markets, MPRs in Day-Ahead Prices agree with short-dated futures. Our results relate risk premia to informed hedging decisions, and futures prices to forecast/expected prices. (author)
Short term forecasting of petroleum product demand in France
International Nuclear Information System (INIS)
Cadren, M.
1998-01-01
The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It's filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter's. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach's and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author)
Short-term residential load forecasting: Impact of calendar effects and forecast granularity
DEFF Research Database (Denmark)
Lusis, Peter; Khalilpour, Kaveh Rajab; Andrew, Lachlan
2017-01-01
forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies...
Review of short-term demand forecasting methods and selection of ...
African Journals Online (AJOL)
Review of short-term demand forecasting methods and selection of the appropriate model for softwood sawmills in Tanzania. ... Tanzania Journal of Forestry and Nature Conservation. Journal Home ... This paper reviewed and tested forecasting methods for use-appropriateness in forecasting lumber demands in the sawmill
Beating the random walk: a performance assessment of long-term interest rate forecasts
den Butter, F.A.G.; Jansen, P.W.
2013-01-01
This article assesses the performance of a number of long-term interest rate forecast approaches, namely time series models, structural economic models, expert forecasts and combinations thereof. The predictive performance of these approaches is compared using outside sample forecast errors, where a
Application of Quantitative Models, MNLR and ANN in Short Term Forecasting of Ship Data
P.Oliver Jayaprakash; K. Gunasekaran
2011-01-01
Forecasting has been the trouble-free way for the port authorities to derive the future expected values of service time of Bulk cargo ships handled at ports of South India. The short term forecasting could be an effective tool for estimating the resource requirements of recurring ships of similar tonnage and Cargo. Forecasting the arrival data related to port based ship operations customarily done using the standard algorithms and assumptions. The regular forecasting methods were decompositio...
Short-term heat load forecasting for single family houses
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
2013-01-01
This paper presents a method for forecasting the load for space heating in a single-family house. The forecasting model is built using data from sixteen houses located in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each...... house the following two days is forecasted. The forecast models are adaptive linear time-series models and the climate inputs used are: ambient temperature, global radiation and wind speed. A computationally efficient recursive least squares scheme is used. The models are optimized to fit the individual...
Application of SVM methods for mid-term load forecasting
Directory of Open Access Journals (Sweden)
Božić Miloš
2011-01-01
Full Text Available This paper presents an approach for the medium-term load forecasting using Support Vector Machines (SVMs. The proposed SVM model was employed to predict the maximum daily load demand for the period of a month. Analyses of available data were performed and the most important features for the construction of SVM model are selected. It was shown that the size and the structure of the training set may significantly affect the accuracy of predictions. The presented model was tested by applying it on real-life load data obtained from distribution company 'ED Jugoistok' for the territory of city Niš and its surroundings. Experimental results show that the proposed approach gives acceptable results for the entire period of prediction, which are in range with other solutions in this area.
Fuzzy system applications for short-term electric load forecasting
Al-Kandari, Ahmad Mohammad
Load forecasting is an important function in economic power generation, allocation between plants (Unit Commitment Scheduling), maintenance scheduling, and for system security applications such as peak shaving by power interchange with interconnected utilities. In this thesis the problem of fuzzy short term load forecasting is formulated and solved. The thesis starts with a discussion of conventional algorithms used in short-term load forecasting. These algorithms are based on least error squares and least absolute value. The theory behind each algorithm is explained. Three different models are developed and tested in the first part of the thesis. The first model (A) is a regression model that takes into account the weather parameters in summer and winter seasons. The second model (B) is a harmonics based model, which does not account for weather parameters, but considers the parameters as a function of time. Model (B) can be used where variations in weather parameters are not available. Finally, model (C) is created as a hybrid combination of models A and B. The parameters of the three models are estimated using the two static estimation algorithms and are used later to predict the load for twenty-four hours ahead. The results obtained are discussed and conclusions are drawn for these models. In the second part of the thesis new fuzzy models are developed for crisp load power with fuzzy load parameters and for fuzzy load power with fuzzy load parameters. Three fuzzy models (A), (B) and (C) are developed. The fuzzy load model (A) is a fuzzy linear regression model for summer and winter seasons. Model (B) is a harmonic fuzzy model, which does not account for weather parameters. Finally fuzzy load model (C) is a hybrid combination of fuzzy load models (A) and (B). Estimating the fuzzy parameters for the three models turns out to be one of linear optimization. The fuzzy parameters are obtained for the three models. These parameters are used to predict the load as a
Short Term Weather Forecasting and Long Term Climate Predictions in Mesoamerica
Hardin, D. M.; Daniel, I.; Mecikalski, J.; Graves, S.
2008-05-01
The SERVIR project utilizes several predictive models to support regional monitoring and decision support in Mesoamerica. Short term forecasts ranging from a few hours to several days produce more than 30 data products that are used daily by decision makers, as well as news organizations in the region. The forecast products can be visualized in both two and three dimensional viewers such as Google Maps and Google Earth. Other viewers developed specifically for the Mesoamerican region by the University of Alabama in Huntsville and the Institute for the Application of Geospatial Technologies in Auburn New York can also be employed. In collaboration with the NASA Short Term Prediction Research and Transition (SpoRT) Center SERVIR utilizes the Weather Research and Forecast (WRF) model to produce short-term (24 hr) regional weather forecasts twice a day. Temperature, precipitation, wind, and other variables are forecast in 10km and 30km grids over the Mesoamerica region. Using the PSU/NCAR Mesoscale Model, known as MM5, SERVIR produces 48 hour- forecasts of soil temperature, two meter surface temperature, three hour accumulated precipitation, winds at different heights, and other variables. These are forecast hourly in 9km grids. Working in collaboration with the Atmospheric Science Department of the University of Alabama in Huntsville produces a suite of short-term (0-6 hour) weather prediction products are generated. These "convective initiation" products predict the onset of thunderstorm rainfall and lightning within a 1-hour timeframe. Models are also employed for long term predictions. The SERVIR project, under USAID funding, has developed comprehensive regional climate change scenarios of Mesoamerica for future years: 2010, 2015, 2025, 2050, and 2099. These scenarios were created using the Pennsylvania State University/National Center for Atmospheric Research (MM5) model and processed on the Oak Ridge National Laboratory Cheetah supercomputer. The goal of these
International Nuclear Information System (INIS)
Anon.
1991-01-01
The price terms in wheeling contracts very substantially, reflecting the differing conditions affecting the parties contracting for the service. These terms differ in the manner in which rates are calculated, the formulas used, and the philosophy underlying the accord. For example, and EEI study found that firm wheeling rates ranged from 20 cents to $1.612 per kilowatt per month. Nonfirm rates ranged from .15 mills to 5.25 mills per kilowatt-hour. The focus in this chapter is on cost-based rates, reflecting the fact that the vast majority of existing contracts are based on rate designs reflecting embedded costs. This situation may change in the future, but, for now, this fact can't be ignored
Mey, Britta; Braun, Axel; Good, Garrett; Vogt, Stephan; Wessel, Arne; Dobschinski, Jan
2016-04-01
Today, wind and solar power forecasts with time horizons from zero to about three hours are essential for the reliable grid and market integration of wind and solar energy. With respect to closure times of German intra-day markets, power forecasts with time horizons of about one to two hours and an update frequency of 15 minutes are required for final trading activities, reducing the uncertainty of the day-ahead forecast of the previous day. Regarding grid security aspects, grid operators utilize such forecasts to create continuous intra-day grid congestion forecasts. In addition to these preventive measures, wind and solar power become more and more important for the provision of ancillary services by wind and solar farm operators. This use case mainly requires power forecasts with time horizons of less than one hour. In general, forecasts with time horizons below three hours are investigated within the nowcasting research area. Nowcasting models are mainly based on current observations and extrapolation methods. With respect to wind and solar power forecasts with horizons of up to three hours, it has been shown in studies that real-time power measurements have the highest information content as compared to other potential model input parameters. We will present results from studies focusing on the benefit of meteorological data (forecasts and/or measurements) in the field of solar and wind power forecasts with time horizons of up to a few hours. Wind farm forecast errors are for example reduced by using numerical weather prediction (NWP) data in the wind power prediction model along with real-time wind farm power measurements. Furthermore, spatially distributed NWP data in combination with German total wind power measurements helped in the reduction of extreme forecast errors. By using global radiation forecasts as an input for wind power forecasts, forecast error during sunrise and sunset could be reduced. In the field of German total solar power, nowcasting
Petroleum price; Prix du petrole
Energy Technology Data Exchange (ETDEWEB)
Maurice, J
2001-07-01
The oil market is the most volatile of all markets, with the exception of the Nasdaq. It is also the biggest commodity market in the world. Therefore one cannot avoid forecasting oil prices, nor can one expect to avoid the forecasting errors that have been made in the past. In his report, Joel Maurice draws a distinction between the short term and the medium-long term in analysing the outlook for oil prices. (author)
A long-term/short-term model for daily electricity prices with dynamic volatility
International Nuclear Information System (INIS)
In this paper we introduce a new stochastic long-term/short-term model for short-term electricity prices, and apply it to four major European indices, namely to the German, Dutch, UK and Nordic one. We give evidence that all time series contain certain periodic (mostly annual) patterns, and show how to use the wavelet transform, a tool of multiresolution analysis, for filtering purpose. The wavelet transform is also applied to separate the long-term trend from the short-term oscillation in the seasonal-adjusted log-prices. In all time series we find evidence for dynamic volatility, which we incorporate by using a bivariate GARCH model with constant correlation. Eventually we fit various models from the existing literature to the data, and come to the conclusion that our approach performs best. For the error distribution, the Normal Inverse Gaussian distribution shows the best fit. (author)
Directory of Open Access Journals (Sweden)
Zhilong Wang
2014-01-01
Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.
Near-term world oil markets : economics, politics and prices
International Nuclear Information System (INIS)
Dwarkin, J.
2002-01-01
This paper discusses the three main factors that will determine how OPEC oil production will impact on energy markets. OPEC reassured the market in September 2001, following the terrorist attack in New York that it would not cut oil production, but by December 2001, OPEC was threatening that it would cut production unless many key non-OPEC producers collaborated to shore up prices. On January 1, 2002, OPEC members went ahead with a quota reduction, based on pledges of cuts from the non-OPEC oil exporting countries. World economies, oil demand, and the path which the U.S. economy will take during 2002 is critical in determining what happens next in terms of oil production from OPEC. Another important factor is knowing whether non-OPEC producers will actually cut output to a significant extent. The most critical factor will be the response by OPEC members if non-OPEC exporting countries do not keep their promise
International Nuclear Information System (INIS)
Li, Yuanjing
2015-01-01
This paper revisits the short-term price and volatility dynamics in day-ahead electricity markets in consideration of an increasing share of wind power, using an example of the Nord Pool day-ahead market and the Danish wind generation. To do so, a GARCH process is applied, and market coupling and the counterbalance effect of hydropower in the Scandinavian countries are additionally accounted for. As results, we found that wind generation weakly dampens spot prices with an elasticity of 0.008 and also reduces price volatility with an elasticity of 0.02 in the Nordic day-ahead market. The results shed lights on the importance of market coupling and interactions between wind power and hydropower in the Nordic system through cross-border exchanges, which play an essential role in price stabilization. Additionally, an EGARCH specification confirms an asymmetric influence of the price innovations, whereby negative shocks produce larger volatility in the Nordic spot market. While considering heavy tails in error distributions can improve model fits significantly, the EGARCH model outperforms the GARCH model on forecast evaluations. (author)
Deep Neural Network Based Demand Side Short Term Load Forecasting
Directory of Open Access Journals (Sweden)
Seunghyoung Ryu
2016-12-01
Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.
The Carbon Trading Price and Trading Volume Forecast in Shanghai City by BP Neural Network
Liu Zhiyuan; Sun Zongdi
2017-01-01
In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network...
A forecast of energy demand in Japan considering asymmetric price elasticities
International Nuclear Information System (INIS)
Nagata, Y.
2001-01-01
By estimating the past energy demand function by sector and types of energy in Japan, the existence of asymmetric price elasticities in most the functions was confirmed. As part of the study, a simple energy and economy model was also constructed to compare future energy demand between the cases with symmetric or asymmetric price elasticities. Results show that future energy demand with asymmetric price elasticities is greater than that with symmetric price elasticities. This result is attributed to the fact that in the asymmetric case, past maximum prices are the most significant factors and price effects will not work unless future energy prices exceed past maximum levels. One of the important implications of this study, i.e. the effect on controlling carbon dioxide emissions, is highlighted. It is pointed out that since price elasticities are small, a high rate of carbon tax is needed to decrease carbon dioxide emissions; meaning that the carbon tax should be high enough for future energy prices to exceed the historical maximum levels. The model developed in this study does not incorporate the substitution between fuels, it is, therefore, not suitable for the further quantitative analysis of the carbon tax. While the study does not deny the applicability of the econometric approach to greenhouse gas analysis, it does point to the an importance of being conscious of the limits of the econometric approach. 7 refs., 4 tabs., 3 figs, 1 appendix
The Performance of Multi-Factor Term Structure Models for Pricing and Hedging Caps and Swaptions
Driessen, J.J.A.G.; Klaassen, P.; Melenberg, B.
2000-01-01
In this paper we empirically compare different term structure models when it comes to the pricing and hedging of caps and swaptions.We analyze the influence of the number of factors on the pricing and hedging results, and investigate which type of data -interest rate data or derivative price data-
Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections
Energy Technology Data Exchange (ETDEWEB)
Feldman, D.; Barbose, G.; Margolis, R.; Wiser, R.; Darghouth, N.; Goodrich, A.
2012-11-01
This report helps to clarify the confusion surrounding different estimates of system pricing by distinguishing between past, current, and near-term projected estimates. It also discusses the different methodologies and factors that impact the estimated price of a PV system, such as system size, location, technology, and reporting methods.These factors, including timing, can have a significant impact on system pricing.
Spatio-temporal modelling for short term wind power forecasts. Why, when and how.
Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre
2017-04-01
This study is based on a case study of 349 wind farms in Western Denmark with available energy production every 15 minutes for 6 years. Our aim is to do short term forecasting up to 5 hours ahead based on previous observations. We want sharp and calibrated probabilistic forecasts for both individual wind farms and for aggregated energy production, for example the energy production in the whole region. To obtain this we propose two Bayesian spatio-temporal models, and obtain full probabilistic forecasts of wind power. The models are based on the stochastic partial differential equation (SPDE) approach to spatial-temporal modelling which enables fast inference using integrated nested Laplace approximations (INLA) as well as dimension reduction. We provide detailed analysis on the forecast performances on the individual and aggregated level based on appropriate metrics tailored for probability forecasts for both the spatial temporal models as well as for temporal models for individual wind farms. The case study as well as simulation studies demonstrate that forecasts that are individually reliable do not need to produce an aggregated forecasts that are reliable. Indeed, the case study shows that even when all individual forecasts are calibrated can the aggregated forecasts be so uncalibrated that less that 20% of the observations fall within the 95% forecast interval. T he results and methodology are both relevant for wind power forecasts in other regions as well as for spatial-temporal modeling and decisions in general.
Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application
Mingfei Niu; Shaolong Sun; Jie Wu; Yuanlei Zhang
2015-01-01
The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias c...
Is there an upward long term trend in Danish real house prices?
DEFF Research Database (Denmark)
Skak, Morten
2012-01-01
In Denmark, like in other countries, there is no agreement on the fundamental long term path of real house prices and the sustainability of the present price level. The paper presents Danish house price indices and discusses the question of quality correction of the indices. Subsequently, factors...... behind the long term trend in real house prices and its sustainability are discussed. The paper finds an annual real growth trend around 1.5 per cent for Danish single family house prices likely for the coming ten years....
NewsMarket 2.0: Analysis of News for Stock Price Forecasting
Barazzetti, Alessandro; Mastronardi, Rosangela
Most of the existing financial research tools use a stock's historical price and technical indicators to predict future price trends without taking into account the impact of web news. The recent explosion of demand for information on financial investment management is driving the search for alternative methods of quantitative data analysis.
Gerikh, Valentin; Kolosok, Irina; Kurbatsky, Victor; Tomin, Nikita
2009-01-01
The paper presents the results of experimental studies concerning calculation of electricity prices in different price zones in Russia and Europe. The calculations are based on the intelligent software "ANAPRO" that implements the approaches based on the modern methods of data analysis and artificial intelligence technologies.
Energy Technology Data Exchange (ETDEWEB)
Salles, Andre A. de; Veiga, Iago E. B. da Costa; Machado, Rafael G.T. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)
2008-07-01
The movements of the oil prices in the international market are important for any planning, so the study of this variable is relevant for the investment and financing decisions of the production. The purpose of this work is to study the time series of the quotations of the spot prices of the crude oil in the international market. The objectives of this work are to study the movements of time series of the prices, and the returns, of the crude oil prices gives emphasis in the stationary. The other focus of this work is to develop forecasting models for the oil prices, or the returns of the oil prices. The selected sample was of the daily quotation of the prices of types WTI and Brent, for the period from January 2005 to April 2007. (author)
The short-term impact of Ontario's generic pricing reforms.
Directory of Open Access Journals (Sweden)
Michael R Law
Full Text Available Canadians pay amongst the highest generic drug prices in the world. In July 2010, the province of Ontario enacted a policy that halved reimbursement for generic drugs from the public drug plan, and substantially lowered prices for private purchases. We quantified the impact of this policy on overall generic drug expenditures in the province, and projected the impact in other provinces had they mimicked this pricing change.We used quarterly prescription generic drug dispensing data from the IMS-Brogan CompuScript Audit. We used the price per unit in both the pre- and post-policy period and two economics price indexes to estimate the expenditure reduction in Ontario. Further, we used the post-policy Ontario prices to estimate the potential reduction in other provinces.We estimate that total expenditure on generic drugs in Ontario during the second half of 2010 was between $181 and $194 million below what would be expected if prices had remained at pre-policy level. Over half of the reduction in spending was due to savings on just 10 generic ingredients. If other provinces had matched Ontario's prices, their expenditures over during the latter half of 2010 would have been $445 million lower.We found that if Ontario's pricing scheme were adopted nationally, overall spending on generic drugs in Canada would drop at least $1.28 billion annually--a 5% decrease in total prescription drug expenditure. Other provinces should seriously consider both changes to their generic drug prices and the use of more competitive bulk purchasing policies.
Time-consistent calibration of short-term regional wind power ensemble forecasts
Directory of Open Access Journals (Sweden)
Stephan Späth
2015-04-01
Full Text Available With increasing wind power capacity, accurate uncertainty forecasts get more and more important for grid integration. The uncertainty of forecasts can be quantified by ensemble forecasts. We use ensemble forecasts from the COSMO-DE EPS to generate short-term ensemble forecasts of regionally aggregated wind power. The wind power forecasts are generated by an optimised regional power curve model that is based on minimum score estimation and leads to wind power forecasts with small deterministic errors. Remaining bias and dispersion errors in the wind power forecasts are removed by statistical post-processing (also called calibration with ensemble model output statistics and the temporal rank correlation of the raw ensemble is maintained by ensemble copula coupling. The verification of raw and calibrated ensembles shows both strong improvements by calibration and the benefit of ensuring time consistency with ensemble copula coupling. The improvements are indicated by the multivariate energy score as well as in a proposed univariate verification approach that is based on integrated wind power forecast and measurement trajectories. Slight deficits in time consistency of the forecasts remain because the theoretical assumptions of ensemble copula coupling are not always fulfilled as the COSMO-DE EPS is based on distinguishable ensemble members. The more training days are used for calibration against measurements of regionally aggregated wind power, the lower is the improvement by calibration which contradicts former results for different variables like wind speed.
Analysts forecast error : A robust prediction model and its short term trading
Boudt, Kris; de Goeij, Peter; Thewissen, James; Van Campenhout, Geert
We examine the profitability of implementing a short term trading strategy based on predicting the error in analysts' earnings per share forecasts using publicly available information. Since large earnings surprises may lead to extreme values in the forecast error series that disrupt their smooth
Short-term wind power forecasting: probabilistic and space-time aspects
DEFF Research Database (Denmark)
Tastu, Julija
a statistical model which would improve the quality of state-of-the-art prediction methods by accounting for the fact that forecasts errors made by such locally-optimized forecasting methods propagate in space and in time under the influence of prevailing weather conditions. Subsequently, the extension from...... work deals with the proposal and evaluation of new mathematical models and forecasting methods for short-term wind power forecasting, accounting for space-time dynamics based on geographically distributed information. Different forms of power predictions are considered, starting from traditional point...... forecasts, then extending to marginal predictive densities and, finally, considering multivariate space-time trajectories. Point predictions is the most classical approach to wind power forecasting, only providing single-valued estimates of the expected future power generation. The objective is to introduce...
Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models
Directory of Open Access Journals (Sweden)
Zhang Chi
2016-01-01
Full Text Available Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.
Modelling long-term oil price and extraction with a Hubbert approach: The LOPEX model
International Nuclear Information System (INIS)
Rehrl, Tobias; Friedrich, Rainer
2006-01-01
The LOPEX (Long-term Oil Price and EXtraction) model generates long-term scenarios about future world oil supply and corresponding price paths up to the year 2100. In order to determine oil production in non-OPEC countries, the model uses Hubbert curves. Hubbert curves reflect the logistic nature of the discovery process and the associated constraint on temporal availability of oil. Extraction paths and world oil price path are both derived endogenously from OPEC's intertemporally optimal cartel behaviour. Thereby OPEC is faced with both the price-dependent production of the non-OPEC competitive fringe and the price-dependent world oil demand. World oil demand is modelled with a constant price elasticity function and refers to a scenario from ACROPOLIS-POLES. LOPEX results indicate a significant higher oil price from around 2020 onwards compared to the reference scenario, and a stagnating market share of maximal 50% to be optimal for OPEC
Density forecasts of crude-oil prices using option-implied and ARCH-type models
DEFF Research Database (Denmark)
Høg, Esben; Tsiaras, Leonicas
2011-01-01
of derivative contracts. Risk-neutral densities, obtained from panels of crude-oil option prices, are adjusted to reflect real-world risks using either a parametric or a non-parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well...... obtained by option prices and non-parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark...
Study on Competitive Exporting Price-forecast of the SMART in the U.S
International Nuclear Information System (INIS)
Kim, In Su; Kim, Tae Ryong
2014-01-01
In line with this, the U.S. has a renewed interest in SMRs rather than large reactors. Nothing, however, has been implemented yet. The only SMRs under construction are in Russia: the first floating nuclear plants. For the most part, the primary candidates to be the first land-based counterparts of Russia's are the SMART (System integrated Modular Advanced Reactor) reactors. The Korean SMART has been developed and licensed for standard design. In addition, the SMART reactor may be suited to countries, which have a small grid capacity, low population density, and decentralization power system such as the U.S. Therefore, the purpose of this paper is to develop a target price for the SMR market opportunities in the U.S., competing against the CCGT (Combined Cycle Gas Turbine) which is currently a very attractive option for generating due to the shale innovation. Even though detailed cost estimates are not available, target price can be derived based on generally determining market price. This paper demonstrates the target exporting price of the SMART in the U.S. ranging from 3,091 - 4,011$/kWe depending on the scaling factor and carbon tax, assuming that discount rates are fixed. This value could be a target cost of construction, developing the U.S market whose demand of the SMART is potentially 4 units 2015 - 2035. Sensitivity analysis shows that the price goes up in proportion to the gas price, the capacity factor of the SMART, the overnight cost of CCGT, etc. More than anything else, this study reveals that carbon tax does not have much influence on the target price compared with those listed above. On the other hand, the price goes up in inverse proportion to the interest of the SMART, the capacity factor of CCGT, O and M costs of the SMART, and so on. For the price competitiveness, construction cost should first be reduced because construction cost is the largest component of LCOE as well as the effect of interest rate is the most sensitive for target price
Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework
DEFF Research Database (Denmark)
Dahl, Christian M.; Croonenbroeck, Carsten
2014-01-01
We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium......-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more...... accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology....
48 CFR 16.207 - Firm-fixed-price, level-of-effort term contracts.
2010-10-01
...-effort term contracts. 16.207 Section 16.207 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION CONTRACTING METHODS AND CONTRACT TYPES TYPES OF CONTRACTS Fixed-Price Contracts 16.207 Firm-fixed-price, level-of-effort term contracts. ...
48 CFR 616.207 - Firm-fixed-price, level-of-effort term contracts.
2010-10-01
...-effort term contracts. 616.207 Section 616.207 Federal Acquisition Regulations System DEPARTMENT OF STATE CONTRACTING METHODS AND CONTRACT TYPES TYPES OF CONTRACTS Fixed-Price Contracts 616.207 Firm-fixed-price, level-of-effort term contracts. ...
Influence of market factors on the pricing of exchange traded metals in the medium term
Bogdanov, S. V.; Shevelev, I. M.; Chernyi, S. A.
2017-06-01
On the basis of comparison of the influence of the stock exchange factors on the pricing of nonferrous metals for medium term with similar results for short term, it has been established that the main attention should be paid to the changes in the pricing environment on the metal market as a function of the prices of exchange traded metals. The situation on the market of energy carriers (hydrocarbons) and the European, American, and Asian stock exchanges can be based on parity and even significantly influence the variation of the metal prices. In the medium term, constructive development of metal trade should be reasonably promoted by changing the elasticity of supply with regard to prices for exchange traded metals and by applying the stock exchange factors that positively influence the pricing on commodity and stock markets.
Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
Directory of Open Access Journals (Sweden)
Wen-Yeau Chang
2013-09-01
Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.
Weather factors in the short-term forecasting of daily ambulance calls.
Wong, Ho-Ting; Lai, Poh-Chin
2014-07-01
The daily ambulance demand for Hong Kong is rising, and it has been shown that weather factors (temperature and humidity) play a role in the demand for ambulance services. This study aimed at developing short-term forecasting models of daily ambulance calls using the 7-day weather forecast data as predictors. We employed the autoregressive integrated moving average (ARIMA) method to analyze over 1.3 million cases of emergency attendance in May 2006 through April 2009 and the 7-day weather forecast data for the same period. Our results showed that the ARIMA model could offer reasonably accurate forecasts of daily ambulance calls at 1-7 days ahead of time and with improved accuracy by including weather factors. Specifically, the inclusion of average temperature alone in our ARIMA model improved the predictability of the 1-day forecast when compared to that of a simple ARIMA model (8.8% decrease in the root mean square error, RMSE=53 vs 58). The improvement in the 7-day forecast with average temperature as a predictor was more pronounced, with a 10% drop in prediction error (RMSE=62 vs 69). These findings suggested that weather forecast data can improve the 1- to 7-day forecasts of daily ambulance demand. As weather forecast data are readily accessible from Hong Kong Observatory's official website, there is virtually no cost to including them in the ARIMA models, which yield better prediction for forward planning and deployment of ambulance manpower.
Price (slump) forecast : the potential impact on pipelines, producers and marketers
Energy Technology Data Exchange (ETDEWEB)
Duncan, J. [Conoco Inc. (United States)
2002-07-01
Throughout this presentation, the speaker answers three basic questions: (1) why are the prices of natural gas so high?, (2) why were the prices of natural gas so high? and (3) will prices for natural gas ever go that high again? The evolution of gas supply and demand including the Canadian supply picture is briefly reviewed. The winter of 2000 and the paradox it presented was discussed, providing a history lesson of an industry taken for granted. The cold winter of 2000 saw industry players scrambling to determine where they would get gas, and the winter of 2001 witnessed them wondering where to put this gas. The new character of the market and the players is discussed, looking at the producer, pipeline expansion projects, and the end user. Neglected investment in the sector and its consequences are dealt with in the next stage of the presentation. The synthetic supply and demand theory are examined. The author concludes the presentation by discussing the factors affecting the market today, such as storage inventory creating volatility, decrease in production and imports due to lag in time when prices are depressed, increased participation by speculators due to increased uncertainty in the stock market, recent weather questions that magnify price movements, and the environment. figs.
The long-term forecast of Taiwan's energy supply and demand: LEAP model application
International Nuclear Information System (INIS)
Huang, Yophy; Bor, Yunchang Jeffrey; Peng, Chieh-Yu
2011-01-01
The long-term forecasting of energy supply and demand is an extremely important topic of fundamental research in Taiwan due to Taiwan's lack of natural resources, dependence on energy imports, and the nation's pursuit of sustainable development. In this article, we provide an overview of energy supply and demand in Taiwan, and a summary of the historical evolution and current status of its energy policies, as background to a description of the preparation and application of a Long-range Energy Alternatives Planning System (LEAP) model of Taiwan's energy sector. The Taiwan LEAP model is used to compare future energy demand and supply patterns, as well as greenhouse gas emissions, for several alternative scenarios of energy policy and energy sector evolution. Results of scenarios featuring 'business-as-usual' policies, aggressive energy-efficiency improvement policies, and on-schedule retirement of Taiwan's three existing nuclear plants are provided and compared, along with sensitivity cases exploring the impacts of lower economic growth assumptions. A concluding section provides an interpretation of the implications of model results for future energy and climate policies in Taiwan. - Research highlights: → The LEAP model is useful for international energy policy comparison. → Nuclear power plants have significant, positive impacts on CO 2 emission. → The most effective energy policy is to adopt demand-side management. → Reasonable energy pricing provides incentives for energy efficiency and conservation. → Financial crisis has less impact on energy demand than aggressive energy policy.
Directory of Open Access Journals (Sweden)
Xuejun Chen
2015-01-01
Full Text Available The support vector regression (SVR and neural network (NN are both new tools from the artificial intelligence field, which have been successfully exploited to solve various problems especially for time series forecasting. However, traditional SVR and NN cannot accurately describe intricate time series with the characteristics of high volatility, nonstationarity, and nonlinearity, such as wind speed and electricity price time series. This study proposes an ensemble approach on the basis of 5-3 Hanning filter (5-3H and wavelet denoising (WD techniques, in conjunction with artificial intelligence optimization based SVR and NN model. So as to confirm the validity of the proposed model, two applicative case studies are conducted in terms of wind speed series from Gansu Province in China and electricity price from New South Wales in Australia. The computational results reveal that cuckoo search (CS outperforms both PSO and GA with respect to convergence and global searching capacity, and the proposed CS-based hybrid model is effective and feasible in generating more reliable and skillful forecasts.
Directory of Open Access Journals (Sweden)
Eleni-Georgia Alevizakou
2018-03-01
Full Text Available Forecasting is one of the most growing areas in most sciences attracting the attention of many researchers for more extensive study. Therefore, the goal of this study is to develop an integrated forecasting methodology based on an Artificial Neural Network (ANN, which is a modern and attractive intelligent technique. The final result is to provide short-term and long-term forecasts for point position changing, i.e., the displacement or deformation of the surface they belong to. The motivation was the combination of two thoughts, the insertion of the forecasting concept in Geodesy as in the most scientific disciplines (e.g., Economics, Medicine and the desire to know the future position of any point on a construction or on the earth’s crustal. This methodology was designed to be accurate, stable and general for different kind of geodetic data. The basic procedure consists of the definition of the forecasting problem, the preliminary data analysis (data pre-processing, the definition of the most suitable ANN, its evaluation using the proper criteria and finally the production of forecasts. The methodology gives particular emphasis on the stages of the pre-processing and the evaluation. Additionally, the importance of the prediction intervals (PI is emphasized. A case study, which includes geodetic data from the year 2003 to the year 2016—namely X, Y, Z coordinates—is implemented. The data were acquired by 1000 permanent Global Navigation Satellite System (GNSS stations. During this case study, 2016 ANNs—with different hyper-parameters—are trained and tested for short-term forecasting and 2016 for long-term forecasting, for each of the GNSS stations. In addition, other conventional statistical forecasting methods are used for the same purpose using the same data set. Finally the most appropriate Non-linear Autoregressive Recurrent network (NAR or Non-linear Autoregressive with eXogenous inputs (NARX for the forecasting of 3D point
Short-term solar irradiation forecasting based on Dynamic Harmonic Regression
International Nuclear Information System (INIS)
Trapero, Juan R.; Kourentzes, Nikolaos; Martin, A.
2015-01-01
Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and is gaining prominence with the current shift to renewable sources of energy. In order to integrate the electricity generated by solar energy into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1–24 h) solar irradiation forecasting. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithms applied offer adaptive predictions. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves the lowest relative Root Mean Squared Error; about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24 h ahead. - Highlights: • Solar irradiation forecasts at short-term are required to operate solar power plants. • This paper assesses the Dynamic Harmonic Regression to forecast solar irradiation. • Models are evaluated using hourly GHI and DNI data collected in Spain. • The results show that forecasting accuracy is improved by using the model proposed
A new cascade NN based method to short-term load forecast in deregulated electricity market
International Nuclear Information System (INIS)
Kouhi, Sajjad; Keynia, Farshid
2013-01-01
Highlights: • We are proposed a new hybrid cascaded NN based method and WT to short-term load forecast in deregulated electricity market. • An efficient preprocessor consist of normalization and shuffling of signals is presented. • In order to select the best inputs, a two-stage feature selection is presented. • A new cascaded structure consist of three cascaded NNs is used as forecaster. - Abstract: Short-term load forecasting (STLF) is a major discussion in efficient operation of power systems. The electricity load is a nonlinear signal with time dependent behavior. The area of electricity load forecasting has still essential need for more accurate and stable load forecast algorithm. To improve the accuracy of prediction, a new hybrid forecast strategy based on cascaded neural network is proposed for STLF. This method is consists of wavelet transform, an intelligent two-stage feature selection, and cascaded neural network. The feature selection is used to remove the irrelevant and redundant inputs. The forecast engine is composed of three cascaded neural network (CNN) structure. This cascaded structure can be efficiently extract input/output mapping function of the nonlinear electricity load data. Adjustable parameters of the intelligent feature selection and CNN is fine-tuned by a kind of cross-validation technique. The proposed STLF is tested on PJM and New York electricity markets. It is concluded from the result, the proposed algorithm is a robust forecast method
Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models
Directory of Open Access Journals (Sweden)
Arash Miranian
2011-12-01
Full Text Available This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF model as the forecaster and utilizes the Hodrick–Prescott (HP filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications.
The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area
Directory of Open Access Journals (Sweden)
Zhenhai Guo
2010-01-01
Full Text Available Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the first definite season index method and the Autoregressive Moving Average (ARMA models or the Generalized Autoregressive Conditional Heteroskedasticity (GARCH forecasting models. The forecasting errors are analyzed and compared with the ones obtained from the ARMA, GARCH model, and Support Vector Machine (SVM; the simulation process and results show that the developed method is simple and quite efficient for daily average wind speed forecasting of Hexi Corridor in China.
Very-short-term wind power probabilistic forecasts by sparse vector autoregression
DEFF Research Database (Denmark)
Dowell, Jethro; Pinson, Pierre
2016-01-01
A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information...... is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...
Comparison of two new short-term wind-power forecasting systems
Energy Technology Data Exchange (ETDEWEB)
Ramirez-Rosado, Ignacio J. [Department of Electrical Engineering, University of Zaragoza, Zaragoza (Spain); Fernandez-Jimenez, L. Alfredo [Department of Electrical Engineering, University of La Rioja, Logrono (Spain); Monteiro, Claudio; Sousa, Joao; Bessa, Ricardo [FEUP, Fac. Engenharia Univ. Porto (Portugal)]|[INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)
2009-07-15
This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning. (author)
Energy Technology Data Exchange (ETDEWEB)
Feldman, D.; Barbose, G.; Margolis, R.; James, T.; Weaver, S.; Darghouth, N.; Fu, R.; Davidson, C.; Booth, S.; Wiser, R.
2014-09-01
This presentation, based on research at Lawrence Berkeley National Laboratory and the National Renewable Energy Laboratory, provides a high-level overview of historical, recent, and projected near-term PV pricing trends in the United States focusing on the installed price of PV systems. It also attempts to provide clarity surrounding the wide variety of potentially conflicting data available about PV system prices. This PowerPoint is the third edition from this series.
Photovoltaic System Pricing Trends. Historical, Recent, and Near-Term Projections, 2015 Edition
Energy Technology Data Exchange (ETDEWEB)
Feldman, David [National Renewable Energy Lab. (NREL), Golden, CO (United States); Barbose, Galen [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Margolis, Robert [National Renewable Energy Lab. (NREL), Golden, CO (United States); Bolinger, Mark [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chung, Donald [National Renewable Energy Lab. (NREL), Golden, CO (United States); Fu, Ran [National Renewable Energy Lab. (NREL), Golden, CO (United States); Seel, Joachim [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Davidson, Carolyn [National Renewable Energy Lab. (NREL), Golden, CO (United States); Darghouth, Naïm [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Wiser, Ryan [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2015-08-25
This presentation, based on research at Lawrence Berkeley National Laboratory and the National Renewable Energy Laboratory, provides a high-level overview of historical, recent, and projected near-term PV pricing trends in the United States focusing on the installed price of PV systems. It also attempts to provide clarity surrounding the wide variety of potentially conflicting data available about PV system prices. This PowerPoint is the fourth edition from this series.
Directory of Open Access Journals (Sweden)
Szanduła Jacek
2014-06-01
Full Text Available The paper develops the concept of harnessing data classification methods to recognize patterns in stock prices. The author defines a formation as a pattern vector describing the financial instrument. Elements of such a vector can be related to the stock price as well as sales volume and other characteristics of the financial instrument. The study uses data concerning selected companies listed on the stock exchange in New York. It takes into account a number of variables that describe the behavior of prices and volume, both in the short and long term. Partitioning around medoids method has been used for data classification (for pattern recognition. An evaluation of the possibility of using certain formations for practical purposes has also been presented.
CSIR Research Space (South Africa)
Das, Sonali
2009-08-01
Full Text Available rates of the census divisions, are more informative when forecasting the house price growth rate of the nine census regions. The role of fundamentals in affecting the housing market cannot be underestimated. What is needed first is a detailed look...
Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity
Directory of Open Access Journals (Sweden)
M. Sonia Terreros-Olarte
2013-05-01
Full Text Available This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV plant. The model is called HIstorical SImilar MIning (HISIMI model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.
Computational intelligence applications to option pricing, volatility forecasting and value at risk
Mostafa, Fahed; Chang, Elizabeth
2017-01-01
The results in this book demonstrate the power of neural networks in learning complex behavior from the underlying financial time series data . The results in this book also demonstrate how neural networks can successfully be applied to volatility modeling, option pricings, and value at risk modeling. These features allow them to be applied to market risk problems to overcome classical issues associated with statistical models. .
Directory of Open Access Journals (Sweden)
Julio Cesar Royer
2016-03-01
Full Text Available The information provided by accurate forecasts of solar energy time series are considered essential for performing an appropriate prediction of the electrical power that will be available in an electric system, as pointed out in Zhou et al. (2011. However, since the underlying data are highly non-stationary, it follows that to produce their accurate predictions is a very difficult assignment. In order to accomplish it, this paper proposes an iterative Combination of Wavelet Artificial Neural Networks (CWANN which is aimed to produce short-term solar radiation time series forecasting. Basically, the CWANN method can be split into three stages: at first one, a decomposition of level p, defined in terms of a wavelet basis, of a given solar radiation time series is performed, generating r+1 Wavelet Components (WC; at second one, these r+1 WCs are individually modeled by the k different ANNs, where k>5, and the 5 best forecasts of each WC are combined by means of another ANN, producing the combined forecasts of WC; and, at third one, the combined forecasts WC are simply added, generating the forecasts of the underlying solar radiation data. An iterative algorithm is proposed for iteratively searching for the optimal values for the CWANN parameters, as we will see. In order to evaluate it, ten real solar radiation time series of Brazilian system were modeled here. In all statistical results, the CWANN method has achieved remarkable greater forecasting performances when compared with a traditional ANN (described in Section 2.1.
Kalman-fuzzy algorithm in short term load forecasting
International Nuclear Information System (INIS)
Shah Baki, S.R.; Saibon, H.; Lo, K.L.
1996-01-01
A combination of Kalman-Fuzzy-Neural is developed to forecast the next 24 hours load. The input data fed to neural network are presented with training data set composed of historical load data, weather, day of the week, month of the year and holidays. The load data is fed through Kalman-Fuzzy filter before being applied to Neural Network for training. With this techniques Neural Network converges faster and the mean percentage error of predicted load is reduced as compared to the classical ANN technique
On the Information in the Interest Rate Term Structure and Option Prices
de Jong, F.; Driessen, J.; Pelsser, A.
2004-01-01
We examine whether the information in cap and swaption prices is consistent with realized movements of the interest rate term structure. To extract an option-implied interest rate covariance matrix from cap and swaption prices, we use Libor market models as a modelling framework. We propose a
Fundamentals or Trend? A Long-Term Perspective on House Prices
Eichholtz, P.; Huisman, R.; Zwinkels, R.C.J.
2015-01-01
Using a long-term time series covering 350 years of house prices along the Herengracht in Amsterdam, we examine whether a fundamental factor or a trend explains house prices and whether their explanatory power is time varying. We find that agents in the housing market switch in their formation of
DEFF Research Database (Denmark)
Baadsgaard, Mikkel; Nielsen, Jan Nygaard; Madsen, Henrik
2000-01-01
An econometric analysis of continuous-timemodels of the term structure of interest rates is presented. A panel of coupon bond prices with different maturities is used to estimate the embedded parameters of a continuous-discrete state space model of unobserved state variables: the spot interest rate......, the central tendency and stochastic volatility. Emphasis is placed on the particular class of exponential-affine term structure models that permits solving the bond pricing PDE in terms of a system of ODEs. It is assumed that coupon bond prices are contaminated by additive white noise, where the stochastic...... and empirical results based on the Danish bond market are presented....
An Operational Short-Term Forecasting System for Regional Hydropower Management
Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.
2017-12-01
The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.
Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
Wen-Yeau Chang
2013-01-01
High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...
Long-term fashion forecast based on the sociological model of cyclic changes
Directory of Open Access Journals (Sweden)
А V Lebsak-Kleimans
2010-09-01
Full Text Available The concepts of social changes coined by classical sociology may be incorporated as the basis for the elaboration of social prognostication models which, in turn, may suitable for fashion forecast applied technologies development. In the framework of the given paper fashion is described as the phenomenon of collective behaviour. The principles of long-term fashion trends forecast are shown to be in line with the concepts of cyclic development.
Drought analysis and short-term forecast in the Aison River Basin (Greece)
Kavalieratou, S.; Karpouzos, D. K.; Babajimopoulos, C.
2012-01-01
A combined regional drought analysis and forecast is elaborated and applied to the Aison River Basin (Greece). The historical frequency, duration and severity were estimated using the standardized precipitation index (SPI) computed on variable time scales, while short-term drought forecast was investigated by means of 3-D loglinear models. A quasi-association model with homogenous diagonal effect was proposed to fit the observed frequencies of class transitions of the SPI values computed on t...
The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area
Guo, Zhenhai; Dong, Yao; Wang, Jianzhou; Lu, Haiyan
2010-01-01
Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the fir...
The Term Structure of Interest Rates and Inflation Forecast Targeting
Eijffinger, S.C.W.; Schaling, E.; Verhagen, W.H.
1998-01-01
This paper examines the implications of the expectations theory of the term structure for the implementation of inflation targeting. We show that the term structure weakens the transmission of short term interest rates to ultimate policy objectives. Therefore, short term interest rates in the
Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch
Xie, Le
2014-01-01
We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.
Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques
Directory of Open Access Journals (Sweden)
Claudio Monteiro
2013-01-01
Full Text Available We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV plants: the analytical PV power forecasting model (APVF and the multiplayer perceptron PV forecasting model (MPVF. Both models use forecasts from numerical weather prediction (NWP tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs. The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.
Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting
Zhu, Xinxin
2014-09-01
Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.
Calibration of short rate term structure models from bid-ask coupon bond prices
Gomes-Gonçalves, Erika; Gzyl, Henryk; Mayoral, Silvia
2018-02-01
In this work we use the method of maximum entropy in the mean to provide a model free, non-parametric methodology that uses only market data to provide the prices of the zero coupon bonds, and then, a term structure of the short rates. The data used consists of the prices of the bid-ask ranges of a few coupon bonds quoted in the market. The prices of the zero coupon bonds obtained in the first stage, are then used as input to solve a recursive set of equations to determine a binomial recombinant model of the short term structure of the interest rates.
Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-23
In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.
Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-07-26
In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.
Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint
Energy Technology Data Exchange (ETDEWEB)
Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen
2017-05-17
A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severe voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.
Directory of Open Access Journals (Sweden)
Herui Cui
2015-01-01
Full Text Available Short-term electric load is significantly affected by weather, especially the temperature effects in summer. External factors can result in mutation structures in load data. Under the influence of the external temperature factors, city electric load cannot be easily forecasted as usual. This research analyzes the relationship between electricity load and daily temperature in city. An improved ARIMAX model is proposed in this paper to deal with the mutation data structures. It is found that information amount of the improved ARIMAX model is smaller than that of the classic method and its relative error is less than AR, ARMA and Sigmoid-Function ANN models. The forecasting results are more accurately fitted. This improved model is highly valuable when dealing with mutation data structure in the field of load forecasting. And it is also an effective technique in forecasting electric load with temperature effects.
Short Term Load Forecasting in Smart Grids: Case Study of the City of Évora
Directory of Open Access Journals (Sweden)
Svetlana Roudolfovna Chemetova
2017-08-01
Full Text Available Currently, load forecasting is a fundamental task for planning, operation and exploration of the electric power systems. The importance of forecasting has become more evident with the restructuring of the national energy sector, thus, promoting projects linked to smart grids, namely in Portugal - InovGrid. This study proposes the computational forecast model of the load diagram based on the Levenberg-Marquardt algorithm of Artificial Neural Networks. The used data are the time series of active power, recorded by EDP Distribution Telemetry System, and the climatic time series of the Portuguese Institute of the Sea and Atmosphere, collected on the city of Évora. The forecast horizon is short term: from one hour to a week. The results showed that main statistical error parameter (mean absolute percentage error was not exceed 5%.
An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan
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Syed Aziz Ur Rehman
2017-11-01
Full Text Available Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan’s energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA, Holt-Winter and Long-range Energy Alternate Planning (LEAP model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector’s demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16% followed by natural gas (36.57%, electricity (16.22%, coal (7.52% and LPG (1.52% in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.
Directory of Open Access Journals (Sweden)
Radziukynas V.
2016-04-01
Full Text Available The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011 and planned wind power capacities (the year 2023.
A generalized one-factor term structure model and pricing of interest rate derivative securities
Jiang, George J.
1997-01-01
The purpose of this paper is to propose a nonparametric interest rate term structure model and investigate its implications on term structure dynamics and prices of interest rate derivative securities. The nonparametric spot interest rate process is estimated from the observed short-term interest
Directory of Open Access Journals (Sweden)
Paliu - Popa Lucia
2011-12-01
Full Text Available On the international markets of goods, pricing is usually done by the confrontation between supply and demand, under pressure from global competition; such pricing is influenced by many other factors that reflect the structural crisis phenomena triggered in the world economy, or factors specific to different groups of goods. After negotiation, the contracting parties should obtain the best price, taking into account the circumstantial situation of the world market upon the transaction, the quality and the technical and functional parameters of the goods subject to negotiations, comparable to those of the competition, the delivery terms and the payment method. From this perspective, we believe that the provision of substantiated external prices makes it easier to obtain maximum benefits and achieve the trade with foreign countries under the best terms. Because the external price is an essential element of the agreement of international sale of goods that contributes substantially to the profitability of an entity, we will deal below with the main categories of prices used in foreign trade activities, both in intra-Community and international transactions, taking into account the models for calculating the external price, compared to the delivery terms Incoterms 2010.
Pan, Zhiyuan; Liu, Li
2018-02-01
In this paper, we extend the GARCH-MIDAS model proposed by Engle et al. (2013) to account for the leverage effect in short-term and long-term volatility components. Our in-sample evidence suggests that both short-term and long-term negative returns can cause higher future volatility than positive returns. Out-of-sample results show that the predictive ability of GARCH-MIDAS is significantly improved after taking the leverage effect into account. The leverage effect for short-term volatility component plays more important role than the leverage effect for long-term volatility component in affecting out-of-sample forecasting performance.
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V. A. Bell
2000-01-01
Full Text Available A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid within the higher resolution model (5 km grid. A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii advected, steady-state predictions from the dynamic model, using 'inferred updraughts', provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment
International Nuclear Information System (INIS)
Halepoto, I.A.; Uqaili, M.A.
2014-01-01
Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintained. (author)
An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry
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Hoang-Sa Dang
2016-10-01
Full Text Available In real practice, forecasting under the limited data has attracted more attention in business activities, especially in the healthcare traveling industry in its current stage. However, there are only a few research studies focusing on this issue. Thus, the purposes of this paper were to determine the forecasted performance of several current forecasting methods as well as to examine their applications. Taking advantage of the small data requirement for model construction, three models including the exponential smoothing model, the Grey model GM(1,1, and the modified Lotka-Volterra model (L.V., were used to conduct forecasting analyses based on the data of foreign patients from 2001 to 2013 in six destinations. The results indicated that the L.V. model had higher prediction power than the other two models, and it obtained the best forecasting performance with an 89.7% precision rate. In conclusion, the L.V. model is the best model for estimating the market size of the healthcare traveling industry, followed by the GM(1,1 model. The contribution of this study is to offer a useful statistical tool for short-term planning, which can be applied to the healthcare traveling industry in particular, and for other business forecasting under the conditions of limited data in general.
A New Two-Stage Approach to Short Term Electrical Load Forecasting
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Dragan Tasić
2013-04-01
Full Text Available In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach.
Oil price shocks and their short- and long-term effects on the Chinese economy
International Nuclear Information System (INIS)
Tang, Weiqi; Wu, Libo; Zhang, ZhongXiang
2010-01-01
A considerable body of economic literature shows the adverse economic impacts of oil-price shocks for the developed economies. However, there has been a lack of similar empirical study on China and other developing countries. This paper attempts to fill this gap by answering how and to what extent oil-price shocks impact China's economy, emphasizing on the price transmission mechanisms. To that end, we develop a structural vector auto-regressive model. Our results show that an oil-price increase negatively affects output and investment, but positively affects inflation rate and interest rate. However, with price control policies in China, the impact on real economy, represented by real output and real investment, lasts much longer than that to price/monetary variables. Our decomposition results also show that the short-term impact, namely output decrease induced by the cut in capacity-utilization rate, is greater in the first 6 periods (namely half a year), but the portion of the long-term impact, defined as the impact realized through an investment change, increases steadily and exceeds that of short-term impact in the 7th period. Afterwards, the long-term impact dominates, and maintains for quite some time. (author)
The impact of wind power on electricity prices
Energy Technology Data Exchange (ETDEWEB)
Brancucci Martinez-Anido, Carlo; Brinkman, Greg; Hodge, Bri-Mathias
2016-08-01
This paper investigates the impact of wind power on electricity prices using a production cost model of the Independent System Operator - New England power system. Different scenarios in terms of wind penetration, wind forecasts, and wind curtailment are modeled in order to analyze the impact of wind power on electricity prices for different wind penetration levels and for different levels of wind power visibility and controllability. The analysis concludes that electricity price volatility increases even as electricity prices decrease with increasing wind penetration levels. The impact of wind power on price volatility is larger in the shorter term (5-min compared to hour-to-hour). The results presented show that over-forecasting wind power increases electricity prices while under-forecasting wind power reduces them. The modeling results also show that controlling wind power by allowing curtailment increases electricity prices, and for higher wind penetrations it also reduces their volatility.
A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation
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Yuan-Kang Wu
2014-01-01
Full Text Available The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.
Application of quantum master equation for long-term prognosis of asset-prices
Khrennikova, Polina
2016-05-01
This study combines the disciplines of behavioral finance and an extension of econophysics, namely the concepts and mathematical structure of quantum physics. We apply the formalism of quantum theory to model the dynamics of some correlated financial assets, where the proposed model can be potentially applied for developing a long-term prognosis of asset price formation. At the informational level, the asset price states interact with each other by the means of a ;financial bath;. The latter is composed of agents' expectations about the future developments of asset prices on the finance market, as well as financially important information from mass-media, society, and politicians. One of the essential behavioral factors leading to the quantum-like dynamics of asset prices is the irrationality of agents' expectations operating on the finance market. These expectations lead to a deeper type of uncertainty concerning the future price dynamics of the assets, than given by a classical probability theory, e.g., in the framework of the classical financial mathematics, which is based on the theory of stochastic processes. The quantum dimension of the uncertainty in price dynamics is expressed in the form of the price-states superposition and entanglement between the prices of the different financial assets. In our model, the resolution of this deep quantum uncertainty is mathematically captured with the aid of the quantum master equation (its quantum Markov approximation). We illustrate our model of preparation of a future asset price prognosis by a numerical simulation, involving two correlated assets. Their returns interact more intensively, than understood by a classical statistical correlation. The model predictions can be extended to more complex models to obtain price configuration for multiple assets and portfolios.
Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm
BeomJun Park; Jin Hur
2017-01-01
Short-term wind power forecasting is a technique which tells system operators how much wind power can be expected at a specific time. Due to the increasing penetration of wind generating resources into the power grids, short-term wind power forecasting is becoming an important issue for grid integration analysis. The high reliability of wind power forecasting can contribute to the successful integration of wind generating resources into the power grids. To guarantee the reliability of forecas...
a system approach to the long term forecasting of the climat data in baikal region
Abasov, N.; Berezhnykh, T.
2003-04-01
The Angara river running from Baikal with a cascade of hydropower plants built on it plays a peculiar role in economy of the region. With view of high variability of water inflow into the rivers and lakes (long-term low water periods and catastrophic floods) that is due to climatic peculiarities of the water resource formation, a long-term forecasting is developed and applied for risk decreasing at hydropower plants. Methodology and methods of long-term forecasting of natural-climatic processes employs some ideas of the research schools by Academician I.P.Druzhinin and Prof. A.P.Reznikhov and consists in detailed investigation of cause-effect relations, finding out physical analogs and their application to formalized methods of long-term forecasting. They are divided into qualitative (background method; method of analogs based on solar activity), probabilistic and approximative methods (analog-similarity relations; discrete-continuous model). These forecasting methods have been implemented in the form of analytical aids of the information-forecasting software "GIPSAR" that provides for some elements of artificial intelligence. Background forecasts of the runoff of the Ob, the Yenisei, the Angara Rivers in the south of Siberia are based on space-time regularities that were revealed on taking account of the phase shifts in occurrence of secular maxima and minima on integral-difference curves of many-year hydrological processes in objects compared. Solar activity plays an essential role in investigations of global variations of climatic processes. Its consideration in the method of superimposed epochs has allowed a conclusion to be made on the higher probability of the low-water period in the actual inflow to Lake Baikal that takes place on the increasing branch of solar activity of its 11-year cycle. The higher probability of a high-water period is observed on the decreasing branch of solar activity from the 2nd to the 5th year after its maximum. Probabilistic method
Short-Term Forecasting of Electric Energy Generation for a Photovoltaic System
Directory of Open Access Journals (Sweden)
Dinh V.T.
2018-01-01
Full Text Available This article presents a short-term forecast of electric energy output of a photovoltaic (PV system towards Tomsk city, Russia climate variations (module temperature and solar irradiance. The system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University. The obtained results show good agreement between actual data and prediction values.
Forecasting long-term energy demand of Croatian transport sector
DEFF Research Database (Denmark)
Pukšec, Tomislav; Krajačić, Goran; Lulić, Zoran
2013-01-01
The transport sector in Croatia represents one of the largest consumers of energy today, with a share of almost one third of the country's final energy demand. Considering this fact, it is very challenging to assess future trends influencing that demand. In this paper, long-term energy demand...... predictions for the Croatian transport sector are presented. Special emphasis is given to different influencing mechanisms, both legal and financial. The energy demand predictions presented in this paper are based on an end-use simulation model developed and tested with Croatia as a case study. The model...... incorporates the detailed modal structure of the Croatian transport sector, including road, rail, air, public and water transport modes. Four long-term energy demand scenarios were analysed till the year 2050; frozen efficiency, implementation of EU legislation, electrification and modal split. Based...
Short-term Operating Strategy with Consideration of Load Forecast and Generating Unit Uncertainty
Sarjiya; Eua-Arporn, Bundhit; Yokoyama, Akihiko
One of the common problems faced by many electric utilities concernes with the uncertainty from both load forecast error and generating unit unavailability. This uncertainty might lead to uneconomic operation if it is not managed properly in the planning stage. Utilities may have many operational tools, e.g. unit commitment, economic dispatch. However, they require a proper operating strategy, taking into account uncertainties. This paper explicitly demonstrates how to include the uncertainties to obtain the best operating strategy for any power systems. The uncertainty of the load forecast is handled using decision analysis method, meanwhile the uncertainty of the generating unit is approached by inclusion of risk cost to the total cost. In addition, three spinning reserve strategies based on deterministic criteria are incorporated in the development of scenario. Meanwhile, Mixed Integer Linear Programming method is utilized to generate unit commitment decision in each created scenario. The best strategy which gives the minimum total cost is selected among the developed scenarios. The proposed method has been tested using a modified of IEEE 24-bus system. Sensitivity analysis with respect to the number of unit, expected unserved energy price, standard deviation of load forecast, and probability of load level is reported.
Fine tuning support vector machines for short-term wind speed forecasting
International Nuclear Information System (INIS)
Zhou Junyi; Shi Jing; Li Gong
2011-01-01
Research highlights: → A systematic approach to tuning SVM models for wind speed prediction is proposed. → Multiple kernel functions and a wide range of tuning parameters are evaluated, and optimal parameters for each kernel function are obtained. → It is found that the forecasting performance of SVM is closely related to the dynamic characteristics of wind speed. → Under the optimal combination of parameters, different kernels give comparable forecasting accuracy. -- Abstract: Accurate forecasting of wind speed is critical to the effective harvesting of wind energy and the integration of wind power into the existing electric power grid. Least-squares support vector machines (LS-SVM), a powerful technique that is widely applied in a variety of classification and function estimation problems, carries great potential for the application of short-term wind speed forecasting. In this case, tuning the model parameters for optimal forecasting accuracy is a fundamental issue. This paper, for the first time, presents a systematic study on fine tuning of LS-SVM model parameters for one-step ahead wind speed forecasting. Three SVM kernels, namely linear, Gaussian, and polynomial kernels, are implemented. The SVM parameters considered include the training sample size, SVM order, regularization parameter, and kernel parameters. The results show that (1) the performance of LS-SVM is closely related to the dynamic characteristics of wind speed; (2) all parameters investigated greatly affect the performance of LS-SVM models; (3) under the optimal combination of parameters after fine tuning, the three kernels give comparable forecasting accuracy; (4) the performance of linear kernel is worse than the other two kernels when the training sample size or SVM order is small. In addition, LS-SVMs are compared against the persistence approach, and it is found that they can outperform the persistence model in the majority of cases.
DEFF Research Database (Denmark)
Golestaneh, Faranak; Pinson, Pierre; Gooi, Hoay Beng
2016-01-01
Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only...... approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power...... generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts...
Short-Term Load Forecast in Electric Energy System in Bulgaria
Directory of Open Access Journals (Sweden)
Irina Asenova
2010-01-01
Full Text Available As the accuracy of the electricity load forecast is crucial in providing better cost effective risk management plans, this paper proposes a Short Term Electricity Load Forecast (STLF model with high forecasting accuracy. Two kind of neural networks, Multilayer Perceptron network model and Radial Basis Function network model, are presented and compared using the mean absolute percentage error. The data used in the models are electricity load historical data. Even though the very good performance of the used model for the load data, weather parameters, especially the temperature, take important part for the energy predicting which is taken into account in this paper. A comparative evaluation between a traditional statistical method and artificial neural networks is presented.
Impacts of high energy prices on long-term energy-economic scenarios for Germany
International Nuclear Information System (INIS)
Krey, V.; Markewitz, P.; Horn, M.; Matthes, C.; Graichen, V.; Harthan, R.O.; Repenning, J.
2007-01-01
Prices of oil and other fossil fuels on global markets have reached a high level in recent years. These levels were not able to be reproduced on the basis of scenarios and prognoses that were published in the past. New scenarios, based on higher energy price trajectories, have appeared only recently. The future role of various energy carriers and technologies in energy-economic scenarios will greatly depend on the level of energy prices. Therefore, an analysis of the impact of high energy prices on long-term scenarios for Germany was undertaken. Based on a reference scenario with moderate prices, a series of consistent high price scenarios for primary and secondary energy carriers were developed. Two scenarios with (i) continuously rising price trajectories and (ii) a price shock with a price peak during the period 2010-15 and a subsequent decline to the reference level are analysed. Two types of models have been applied in the analysis. The IKARUS energy systems optimisation model covers the whole of the German energy system from primary energy supply down to the end-use sectors. Key results in both high price scenarios include a replacement of natural gas by hard coal and renewable energy sources in electricity and heat generation. Backstop technologies like coal liquefaction begin to play a role under such conditions. Up to 10% of final energy consumption is saved in the end-use sectors, with the residential and transport sector being the greatest contributors. Even without additional restrictions, CO 2 emissions significantly drop in comparison to the reference scenario. The ELIAS electricity investment analysis model focuses on the power sector. In the reference scenario with current allocation rules in the emissions trading scheme, the CO 2 emissions decrease relatively steadily. The development is characterised by the phaseout of nuclear energy which is counterweighted by the increase of renewable. In the high price scenario, the CO 2 emissions temporarily
Long-term memory in electricity prices: Czech market evidence
Czech Academy of Sciences Publication Activity Database
Krištoufek, Ladislav; Luňáčková, P.
2013-01-01
Roč. 63, č. 5 (2013), s. 407-424 ISSN 0015-1920 R&D Projects: GA ČR GA402/09/0965 Grant - others:GA ČR(CZ) GAP402/11/0948 Program:GA Institutional support: RVO:67985556 Keywords : electricity * long-term memory Subject RIV: AH - Economics Impact factor: 0.358, year: 2013 http://library.utia.cas.cz/separaty/2014/E/kristoufek-0427660.pdf
Analysis of recurrent neural networks for short-term energy load forecasting
Di Persio, Luca; Honchar, Oleksandr
2017-11-01
Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.
Verification of“Trend-Volatility Model”in Short-Term Forecast of Grain Production Potential
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MI Chang-hong
2016-02-01
Full Text Available The "trend-volatility model" in short-term forecasting of grain production potential was verified and discussed systematically by using the grain production data from 1949 to 2014, in 16 typical counties and 6 typical districts, and 31 provinces, of China. The results showed as follows:(1 Size of forecast error reflected the precision of short-term production potential, the main reason of large prediction error was a great amount of high yield farmlands were occupied in developed areas and a great increase of vegetable and fruit planted that made grain yield decreased in a short time;(2 The micro-trend amendment method was a necessary part of "trend-volatility model", which could involve the short-term factors such as meteorological factors, science and technology input, social factors and other effects, while macro-trend prediction could not. Therefore, The micro-trend amendment method could improve the forecast precision.(3 In terms of actual situation in recent years in China, the more developed the areas was, the bigger the volatility of short-term production potential was; For the short-term production potential, the stage of increasing-decreasing-recovering also existed in developed areas;(4 In the terms of forecast precision of short-terms production potential, the scale of national was higher than the scale of province, the scale of province was higher than the scale of district, the scale of district was higher than the scale of county. And it was large differences in precision between different provinces, different districts and different counties respectively, which was concerned to the complementarity of domestic climate and the ability of the farmland resistance to natural disasters.
Evolution of the European gas market on the long term. Organisation and price
International Nuclear Information System (INIS)
Ouvry, V.
1998-01-01
The objective of this work is to shed light upon the future organization of the European gas market with an emphasis on price matters. There are nowadays few producers of gas on the market, most of whom hold long-term contracts with gas companies. Gas pricing is based on the net-back principle. The actual debate on liberalization of the gas market and the growing pressure from industrial customers to obtain lower prices addresses the problem of the future organisation of the market and the potential impact of the introduction of third party access. We first analyse the main actors of the gas market, their strategy and the actual market organization market. Two different logics are considered hereunder: a market approach: the competition theory provides efficient tools to analyse the evolution of competition depending on numerous factors. It appears that the strategy of all actors and particularly of producers will be the main determinant of the future competition. The oligopoly theory includes oligopolistic behaviours modelizations. The application of the Cournot's model leads to prices ranging from 1,6 to 3,7 $/MBtu; a contractual approach: today, gas is essentially exchanged through long term contracts, which allow for long-term management of investments and supply security. Two operators negotiate the price, which ultimately mirrors their respective leverage. The transaction cost theory clearly shows the necessity of including transaction costs, especially when optimizing the duration of the contract. The gas prices escalation is nowadays partially obsolete and unadapted to customer needs. Escalation on coal, electricity price or inflation should soon be considered. The theories of negotiation highlight the importance of the operators' marketing power during gas price fixation Applying Nash and Harsanyi-Selten's negotiation models results in a scale of 2,4 to 3,5 $/MBtu of the gas price at the actual supply and demand conditions. Both approaches lead to similar
Dynamical prediction and pattern mapping in short-term load forecasting
Energy Technology Data Exchange (ETDEWEB)
Aguirre, Luis Antonio; Rodrigues, Daniela D.; Lima, Silvio T. [Departamento de Engenharia Eletronica, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil); Martinez, Carlos Barreira [Departamento de Engenharia Hidraulica e Recursos Hidricos, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil)
2008-01-15
This work will not put forward yet another scheme for short-term load forecasting but rather will provide evidences that may improve our understanding about fundamental issues which underlay load forecasting problems. In particular, load forecasting will be decomposed into two main problems, namely dynamical prediction and pattern mapping. It is argued that whereas the latter is essentially static and becomes nonlinear when weekly features in the data are taken into account, the former might not be deterministic at all. In such cases there is no determinism (serial correlations) in the data apart from the average cycle and the best a model can do is to perform pattern mapping. Moreover, when there is determinism in addition to the average cycle, the underlying dynamics are sometimes linear, in which case there is no need to resort to nonlinear models to perform dynamical prediction. Such conclusions were confirmed using real load data and surrogate data analysis. In a sense, the paper details and organizes some general beliefs found in the literature on load forecasting. This sheds some light on real model-building and forecasting problems and helps understand some apparently conflicting results reported in the literature. (author)
An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power
Directory of Open Access Journals (Sweden)
Antonio Bracale
2015-09-01
Full Text Available Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach.
Improving the principles of short-term electric load forecasting of the Irkutsk region
Directory of Open Access Journals (Sweden)
Kornilov Vladimir
2017-01-01
Full Text Available Forecasting of electric load (EL is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region.
Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint
Energy Technology Data Exchange (ETDEWEB)
Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Elgindy, Tarek [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dobbs, Alex [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-10-03
A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmospheric measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.
Klibanov, Michael V.; Kuzhuget, Andrey V.; Golubnichiy, Kirill V.
2016-01-01
A new empirical mathematical model for the Black-Scholes equation is proposed to forecast option prices. This model includes new interval for the price of the underlying stock, new initial and new boundary conditions. Conventional notions of maturity time and strike prices are not used. The Black-Scholes equation is solved as a parabolic equation with the reversed time, which is an ill-posed problem. Thus, a regularization method is used to solve it. To verify the validity of our model, real market data for 368 randomly selected liquid options are used. A new trading strategy is proposed. Our results indicates that our method is profitable on those options. Furthermore, it is shown that the performance of two simple extrapolation-based techniques is much worse. We conjecture that our method might lead to significant profits of those financial insitutions which trade large amounts of options. We caution, however, that further studies are necessary to verify this conjecture.
Real-time short-term forecast of water inflow into Bureyskaya reservoir
Motovilov, Yury
2017-04-01
model performance as compared to observed inflow data into the Bureya reservoir and high diagnostic potential of data-modeling system of the runoff formation. With the use of this system the following flowchart for short-range forecasting inflow into Bureyskoe reservoir and forecast correction technique using continuously updated hydrometeorological data has been developed: 1 - Daily renewal of weather observations and forecasts database via the Internet; 2 - Daily runoff calculation from the beginning of the current year to current date is conducted; 3 - Short-range (up to 7 days) forecast is generated based on weather forecast. The idea underlying the model assimilation of newly obtained hydro meteorological information to adjust short-range hydrological forecasts lies in the assumption of the forecast errors inertia. Then the difference between calculated and observed streamflow at the forecast release date is "scattered" with specific weights to calculated streamflow for the forecast lead time. During 2016 this forecasts method of the inflow into the Bureyskaya reservoir up to 7 days is tested in online mode. Satisfactory evaluated short-range inflow forecast success rate is obtained. Tests of developed method have shown strong sensitivity to the results of short-term precipitation forecasts.
Sterling, K.; Denbo, D. W.; Eble, M. C.
2016-12-01
Short-term Inundation Forecasting for Tsunamis (SIFT) software was developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) for use in tsunami forecasting and has been used by both U.S. Tsunami Warning Centers (TWCs) since 2012, when SIFTv3.1 was operationally accepted. Since then, advancements in research and modeling have resulted in several new features being incorporated into SIFT forecasting. Following the priorities and needs of the TWCs, upgrades to SIFT forecasting were implemented into SIFTv4.0, scheduled to become operational in October 2016. Because every minute counts in the early warning process, two major time saving features were implemented in SIFT 4.0. To increase processing speeds and generate high-resolution flooding forecasts more quickly, the tsunami propagation and inundation codes were modified to run on Graphics Processing Units (GPUs). To reduce time demand on duty scientists during an event, an automated DART inversion (or fitting) process was implemented. To increase forecasting accuracy, the forecasted amplitudes and inundations were adjusted to include dynamic tidal oscillations, thereby reducing the over-estimates of flooding common in SIFTv3.1 due to the static tide stage conservatively set at Mean High Water. Further improvements to forecasts were gained through the assimilation of additional real-time observations. Cabled array measurements from Bottom Pressure Recorders (BPRs) in the Oceans Canada NEPTUNE network are now available to SIFT for use in the inversion process. To better meet the needs of harbor masters and emergency managers, SIFTv4.0 adds a tsunami currents graphical product to the suite of disseminated forecast results. When delivered, these new features in SIFTv4.0 will improve the operational tsunami forecasting speed, accuracy, and capabilities at NOAA's Tsunami Warning Centers.
A branch-and-price algorithm for the long-term home care scheduling problem
DEFF Research Database (Denmark)
Gamst, Mette; Jensen, Thomas Sejr
2012-01-01
In several countries, home care is provided for certain citizens living at home. The long-term home care scheduling problem is to generate work plans such that a high quality of service is maintained, the work hours of the employees are respected, and the overall cost is kept as low as possible. We...... propose a branchand-price algorithm for the long-term home care scheduling problem. The pricing problem generates a one-day plan for an employee, and the master problem merges the plans with respect to regularity constraints. The method is capable of generating plans with up to 44 visits during one week....
Uranium price trends for use in strategy analyses
International Nuclear Information System (INIS)
James, R.A.
1979-09-01
Long-term price forecasts for mined uranium are quoted. These will be used in Ontario Hydro's nuclear fuel cycle strategy analyses. They are, of necessity, speculative. The accuracy of the forecasts is considered adequate for long-term strategy analyses, but not for other purposes. (auth)
Energy Technology Data Exchange (ETDEWEB)
NONE
2013-03-15
This report provides a description of the Swedish energy system in 2011 and an assessment of its development between 2012 - 2014. The forecast shall be interpreted as a consequence of the limitations and assumptions underlying it. Thus, it is important to remember that if any of the conditions or assumptions change, the forecast's results will also change. The forecast is based on economic conditions that have been developed from the National Institute of Economic Trend. Other conditions such as electricity prices, fuel prices, outdoor temperature and inflow into reservoirs are based on information available up to January 2013, when forecasting began.
Energy Technology Data Exchange (ETDEWEB)
NONE
2013-08-01
The Energy Agency has a mandate that under 'Ordinance on climate reporting' (SFS 2005:626) out projections for the energy sector of the European Parliament and Council Decision No 280/2004/EC concerning a 'Mechanism for monitoring the emissions of the Community greenhouse gas'. This report contains a reference trajectory until 2030, and two sensitivity scenarios. The forecast is based on existing instruments, which means that results of the report should not be regarded as a proper projection of future energy, but as the impact of current policy instruments given different conditions such as economic growth and fuel prices. The Energy Authority's long-term forecasts are studied energy system's long-term development on the basis of policy instruments and several assumed conditions. The conditions for this long-term prognosis was established in January 2012 and has its basis in the policy instruments decided until the turn of 2011/2012. The work was partially done in conjunction with the Environmental Protection Agency assignments 'Assignment to provide input to a Swedish road map for Sweden without greenhouse gas emissions in 2050' as reported in December 2012. For a short-term development of the energy system the reader is referred to the Energy Authority's short-term forecasts that extend two to three years into the future and that are produced twice a year. Energy Agency's long-term projections are impact assessments with time horizon of 10-20 years which aims to describe the energy system's future development, provided a range of assumed conditions. If any of these conditions change it will also change forecast results. Economic development is an important assumption for the assessment of future energy.
International Nuclear Information System (INIS)
Shammas, P.
1996-01-01
Supply of LPG is forecast to grow over the next decade from the present level of 180 million t/y to about 185-190 million t/y, depending on demand in Asia which is rising rapidly and on new LPG export projects. Most of the increase in supply will come from new gas and crude oil production, from new LPG ventures, and from refineries reducing the n-butane content of motor gasoline. Pricing will remain volatile as a result of crude oil price volatility, variations in the winter weather in the Northern Hemisphere, and as result of competition between ethane, PPG, naphtha and condensate as ethylene cracker feedstocks. Demand for LPG in OECD countries will continue to show steady growth. The increase in demand will be more rapid in the relatively less developed OECD countries, as the trend in Spain has shown in recent years. Provided that the LPG price is competitive, demand in China, South-East Asia and the Indian sub-continent could grow beyond current projections. Consumption in these countries will depend on the installation of distribution facilities and the rate at which LPG can substitute for traditional fuels and kerosene as well as compete for limited disposable incomes. (author)
A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain
Directory of Open Access Journals (Sweden)
Francesca Gagliardi
2017-07-01
Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.
Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach
International Nuclear Information System (INIS)
Kucukali, Serhat; Baris, Kemal
2010-01-01
This paper aims to forecast Turkey's short-term gross annual electricity demand by applying fuzzy logic methodology while general information on economical, political and electricity market conditions of the country is also given. Unlike most of the other forecast models about Turkey's electricity demand, which usually uses more than one parameter, gross domestic product (GDP) based on purchasing power parity was the only parameter used in the model. Proposed model made good predictions and captured the system dynamic behavior covering the years of 1970-2014. The model yielded average absolute relative errors of 3.9%. Furthermore, the model estimates a 4.5% decrease in electricity demand of Turkey in 2009 and the electricity demand growth rates are projected to be about 4% between 2010 and 2014. It is concluded that forecasting the Turkey's short-term gross electricity demand with the country's economic performance will provide more reliable projections. Forecasting the annual electricity consumption of a country could be made by any designer with the help of the fuzzy logic procedure described in this paper. The advantage of this model lies on the ability to mimic the human thinking and reasoning.
Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
Directory of Open Access Journals (Sweden)
Konstantinos N. Genikomsakis
2017-11-01
Full Text Available The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy.
Drought analysis and short-term forecast in the Aison River Basin (Greece
Directory of Open Access Journals (Sweden)
S. Kavalieratou
2012-05-01
Full Text Available A combined regional drought analysis and forecast is elaborated and applied to the Aison River Basin (Greece. The historical frequency, duration and severity were estimated using the standardized precipitation index (SPI computed on variable time scales, while short-term drought forecast was investigated by means of 3-D loglinear models. A quasi-association model with homogenous diagonal effect was proposed to fit the observed frequencies of class transitions of the SPI values computed on the 12-month time scale. Then, an adapted submodel was selected for each data set through the backward elimination method. The analysis and forecast of the drought class transition probabilities were based on the odds of the expected frequencies, estimated by these submodels, and the respective confidence intervals of these odds. The parsimonious forecast models fitted adequately the observed data. Results gave a comprehensive insight on drought behavior, highlighting a dominant drought period (1988–1991 with extreme drought events and revealing, in most cases, smooth drought class transitions. The proposed approach can be an efficient tool in regional water resources management and short-term drought warning, especially in irrigated districts.
Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather
Directory of Open Access Journals (Sweden)
Weiliang Liu
2018-02-01
Full Text Available Fog and haze (F-H weather has been occurring frequently in China since 2012, which affects the output power of photovoltaic (PV generation dramatically by directly weakening solar irradiance and aggravating dust deposition on PV panels. The ultra-short-term forecast method presented in this study would help to fully reflect the dual effects of F-H on PV output power. Aiming at the weakening effect on solar irradiance, estimation models of atmospheric aerosol optical depth (AOD based on particle matter (PM concentration were established with machine learning (ML method, and the total irradiance received by PV panels was calculated based on simplified REST2 model. Aiming at the aggravating effect on dust deposition on PV panels, sample set of “cumulative PM concentration—efficiency reduction” was constructed through special measurement experiments, then the efficiency reduction under certain dust deposition state was estimated with similar-day choosing method. Based on photoelectric conversion model, PM concentration prediction and weather forecast information, ultra-short-term forecast of PV output power was realized. Experimental results proved the validity and feasibility of the presented forecast method.
The capital-asset pricing model reconsidered: tests in real terms on ...
African Journals Online (AJOL)
This paper extends previous work of the authors to reconsider the capital-asset pricing model (CAPM) in South Africa in real terms. As in that work, the main question this study aimed to answer remains: Can the CAPM be accepted in the South African market for the purposes of the stochastic modelling of investment returns ...
Pricing Swaptions and Coupon Bond Options in Affine Term Structure Models
Schrager, D.F.; Pelsser, A.A.J.
2006-01-01
We propose an approach to find an approximate price of a swaption in affine term structure models. Our approach is based on the derivation of approximate swap rate dynamics in which the volatility of the forward swap rate is itself an affine function of the factors. Hence, we remain in the affine
Pricing swaptions and coupon bond options in affine term structure models
Schrager, D.F.; Pelsser, A.A.J.
2005-01-01
We propose an approach to …nd an approximate price of a swaption in Affine Term Structure Models. Our approach is based on the derivation of approximate dynamics in which the volatility of the Forward Swap Rate is itself an affine function of the factors. Hence we remain in the Affine framework and
Short- and Long-Term Earthquake Forecasts Based on Statistical Models
Console, Rodolfo; Taroni, Matteo; Murru, Maura; Falcone, Giuseppe; Marzocchi, Warner
2017-04-01
The epidemic-type aftershock sequences (ETAS) models have been experimentally used to forecast the space-time earthquake occurrence rate during the sequence that followed the 2009 L'Aquila earthquake and for the 2012 Emilia earthquake sequence. These forecasts represented the two first pioneering attempts to check the feasibility of providing operational earthquake forecasting (OEF) in Italy. After the 2009 L'Aquila earthquake the Italian Department of Civil Protection nominated an International Commission on Earthquake Forecasting (ICEF) for the development of the first official OEF in Italy that was implemented for testing purposes by the newly established "Centro di Pericolosità Sismica" (CPS, the seismic Hazard Center) at the Istituto Nazionale di Geofisica e Vulcanologia (INGV). According to the ICEF guidelines, the system is open, transparent, reproducible and testable. The scientific information delivered by OEF-Italy is shaped in different formats according to the interested stakeholders, such as scientists, national and regional authorities, and the general public. The communication to people is certainly the most challenging issue, and careful pilot tests are necessary to check the effectiveness of the communication strategy, before opening the information to the public. With regard to long-term time-dependent earthquake forecast, the application of a newly developed simulation algorithm to Calabria region provided typical features in time, space and magnitude behaviour of the seismicity, which can be compared with those of the real observations. These features include long-term pseudo-periodicity and clustering of strong earthquakes, and a realistic earthquake magnitude distribution departing from the Gutenberg-Richter distribution in the moderate and higher magnitude range.
Australia's long-term electricity demand forecasting using deep neural networks
Hamedmoghadam, Homayoun; Joorabloo, Nima; Jalili, Mahdi
2018-01-01
Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand under-estimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep neural networks to predict Australia's long-term electricity demand. A stacked autoencoder is used in...
DEFF Research Database (Denmark)
Møller Andersen, Frits; Larsen, Helge V.; Boomsma, Trine Krogh
2013-01-01
Data for aggregated hourly electricity demand shows systematic variations over the day, week, and seasons, and forecasting of aggregated hourly electricity load has been the subject of many studies. With hourly metering of individual customers, data for individual consumption profiles is available....... Using this data and analysing the case of Denmark, we show that consumption profiles for categories of customers are equally systematic but very different for distinct categories, that is, distinct categories of customers contribute differently to the aggregated electricity load profile. Therefore......, to model and forecast long-term changes in the aggregated electricity load profile, we identify profiles for different categories of customers and link these to projections of the aggregated annual consumption by categories of customers. Long-term projection of the aggregated load is important for future...
Forecasting Analysis of Shanghai Stock Index Based on ARIMA Model
Directory of Open Access Journals (Sweden)
Li Chenggang
2017-01-01
Full Text Available Prediction and analysis of the Shanghai Composite Index is conducive for investors to investing in the stock market, and providing investors with reference. This paper selects Shanghai Composite Index monthly closing price from Jan, 2005 to Oct, 2016 to construct ARIMA model. This paper carries on the forecast of the last three monthly closing price of Shanghai Stock Index that have occurred, and compared it with the actual value, which tests the accuracy and feasibility of the model in the short term Shanghai Stock Index forecast. At last, this paper uses the ARIMA model to forecast the Shanghai Composite Index closing price of the last two months in 2016.
Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.
Short term load forecasting of anomalous load using hybrid soft computing methods
Rasyid, S. A.; Abdullah, A. G.; Mulyadi, Y.
2016-04-01
Load forecast accuracy will have an impact on the generation cost is more economical. The use of electrical energy by consumers on holiday, show the tendency of the load patterns are not identical, it is different from the pattern of the load on a normal day. It is then defined as a anomalous load. In this paper, the method of hybrid ANN-Particle Swarm proposed to improve the accuracy of anomalous load forecasting that often occur on holidays. The proposed methodology has been used to forecast the half-hourly electricity demand for power systems in the Indonesia National Electricity Market in West Java region. Experiments were conducted by testing various of learning rate and learning data input. Performance of this methodology will be validated with real data from the national of electricity company. The result of observations show that the proposed formula is very effective to short-term load forecasting in the case of anomalous load. Hybrid ANN-Swarm Particle relatively simple and easy as a analysis tool by engineers.
A clustering-based fuzzy wavelet neural network model for short-term load forecasting.
Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias
2013-10-01
Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.
Directory of Open Access Journals (Sweden)
Jason Grant
2014-03-01
Full Text Available The power output capacity of a local electrical utility is dictated by its customers’ cumulative peak-demand electrical consumption. Most electrical utilities in the United States maintain peak-power generation capacity by charging for end-use peak electrical demand; thirty to seventy percent of an electric utility’s bill. To reduce peak demand, a real-time energy monitoring system was designed, developed, and implemented for a large government building. Data logging, combined with an application of artificial neural networks (ANNs, provides short-term electrical load forecasting data for controlled peak demand. The ANN model was tested against other forecasting methods including simple moving average (SMA, linear regression, and multivariate adaptive regression splines (MARSplines and was effective at forecasting peak building electrical demand in a large government building sixty minutes into the future. The ANN model presented here outperformed the other forecasting methods tested with a mean absolute percentage error (MAPE of 3.9% as compared to the SMA, linear regression, and MARSplines MAPEs of 7.7%, 17.3%, and 7.0% respectively. Additionally, the ANN model realized an absolute maximum error (AME of 8.2% as compared to the SMA, linear regression, and MARSplines AMEs of 26.2%, 45.1%, and 22.5% respectively.
Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627
Iulian Lolea
2017-01-01
This article aims to compare the performance of GARCH models in terms of volatility forecasting for two asset classes: equity and commodities. The idea behind this research was that GARCH models may perform differently depending on the asset class for which they are used. A comparison based on performance of GARCH, EGARCH and GJR-GARCH for the Romanian equity market, Polish equity market, gold market and Brent Crude Oil market has been done. The results were in line with initial e...
Refined Source Terms in Wave Watch 3 with Wave Breaking and Sea Spray Forecasts
2016-08-05
Hsiao and Shemdin, 1983; Plant , 1990), for reference. Note that the form of the Janssen (1991) growth rate parameterization has been largely followed... spectrum against the observed terms during the young wind sea growth episode in the Strait of Juan de Fuca reported by Schwendeman et al. (2014). The...suite of forecast sea state variables, for sea state conditions ranging from light to very severe. Our approach required a combination of
Probabilistic forecasts of near-term climate change based on a resampling ensemble technique
Räisänen, J.; Ruokolainen, L.
2006-01-01
Probabilistic forecasts of near-term climate change are derived by using a multimodel ensemble of climate change simulations and a simple resampling technique that increases the number of realizations for the possible combination of anthropogenic climate change and internal climate variability. The technique is based on the assumption that the probability distribution of local climate changes is only a function of the all-model mean global average warming. Although this is unlikely to be exac...
International Nuclear Information System (INIS)
Truong, Nguyen-Vu; Wang, Liuping; Wong, Peter K.C.
2008-01-01
Power demand forecasting is of vital importance to the management and planning of power system operations which include generation, transmission, distribution, as well as system's security analysis and economic pricing processes. This paper concerns the modeling and short-term forecast of daily peak power demand in the state of Victoria, Australia. In this study, a two-dimensional wavelet based state dependent parameter (SDP) modelling approach is used to produce a compact mathematical model for this complex nonlinear dynamic system. In this approach, a nonlinear system is expressed by a set of linear regressive input and output terms (state variables) multiplied by the respective state dependent parameters that carry the nonlinearities in the form of 2-D wavelet series expansions. This model is identified based on historical data, descriptively representing the relationship and interaction between various components which affect the peak power demand of a certain day. The identified model has been used to forecast daily peak power demand in the state of Victoria, Australia in the time period from the 9th of August 2007 to the 24th of August 2007. With a MAPE (mean absolute prediction error) of 1.9%, it has clearly implied the effectiveness of the identified model. (author)
Electric power systems advanced forecasting techniques and optimal generation scheduling
Catalão, João P S
2012-01-01
Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie
The Impact of the Assimilation of AIRS Radiance Measurements on Short-term Weather Forecasts
McCarty, Will; Jedlovec, Gary; Miller, Timothy L.
2009-01-01
Advanced spaceborne instruments have the ability to improve the horizontal and vertical characterization of temperature and water vapor in the atmosphere through the explicit use of hyperspectral thermal infrared radiance measurements. The incorporation of these measurements into a data assimilation system provides a means to continuously characterize a three-dimensional, instantaneous atmospheric state necessary for the time integration of numerical weather forecasts. Measurements from the National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) are incorporated into the gridpoint statistical interpolation (GSI) three-dimensional variational (3D-Var) assimilation system to provide improved initial conditions for use in a mesoscale modeling framework mimicking that of the operational North American Mesoscale (NAM) model. The methodologies for the incorporation of the measurements into the system are presented. Though the measurements have been shown to have a positive impact in global modeling systems, the measurements are further constrained in this system as the model top is physically lower than the global systems and there is no ozone characterization in the background state. For a study period, the measurements are shown to have positive impact on both the analysis state as well as subsequently spawned short-term (0-48 hr) forecasts, particularly in forecasted geopotential height and precipitation fields. At 48 hr, height anomaly correlations showed an improvement in forecast skill of 2.3 hours relative to a system without the AIRS measurements. Similarly, the equitable threat and bias scores of precipitation forecasts of 25 mm (6 hr)-1 were shown to be improved by 8% and 7%, respectively.
A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty
Ohmi, Masataro; Mori, Hiroyuki
In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.
Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm
Directory of Open Access Journals (Sweden)
Haoran Zhao
2018-03-01
Full Text Available As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N decomposition approach, the Least Square Support Vector Machine (LSSVM, and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA. For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM, and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM, it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.
Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks
Directory of Open Access Journals (Sweden)
Zhisheng Zhang
2016-01-01
Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.
Energy Technology Data Exchange (ETDEWEB)
Erath, A.; Axhausen, K. W.
2010-04-15
This comprehensive final report for the Swiss Federal Office of Energy (SFOE) examines the long-term effects of fuel price elasticity. The study analyses how mobility tool usage and ownership as well as residence location choice are affected by rising fuel costs. Based on econometric models, long-term fuel price elasticity is derived. The authors quote that the demand reactions to higher fuel prices mainly observed are the reduction of mileage and the consideration of smaller-engined and diesel-driven cars. As cars with natural gas powered engines and electric drives were hardly considered in the survey, the results of the natural gas model can, according to the authors, only serve as a trend. No stable model could be estimated for the demand and usage of electric cars. A literature overview is presented and the design of the survey is discussed, whereby socio-demographical variables and the effects of price and residence changes are discussed. Modelling of mobility tool factors and results obtained are looked at. Finally, residence choice factors are modelled and discussed. Several appendices complete the report.
Molthan, A.; Fuell, K. K.; Berndt, E.; Schultz, L. A.
2016-12-01
The NASA/SPoRT Program supports the NOAA/JPSS program through the transition of S-NPP VIIRS and CrIS/ATMS products to prepare users for the upcoming JPSS-1/-2 missions. Several multispectral (i.e. RGB) imagery products can be created from VIIRS based on internationally-accepted recipes developed by EUMETSAT. Initial transition of a Nighttime Microphysics RGB to operations revealed improved distinction between low clouds and fog compared with legacy satellite imagery, and hence, improvement in short-term aviation and public forecasts. An increased number of S-NPP passes at high latitude combined with other instruments led to a series of "microphysical" RGBs to be introduced to NWS forecasters in Alaska at both local weather offices as well as regional aviation centers. Forecasters in Alaska also applied VIIRS microphysical RGBs to identify small scale features such as valley/coastal fog, volcanic ash, and convective precipitation. Further use of a "Dust" RGB in the U.S. southwest led to changes in NWS forecast products due to improvements in detection and monitoring of dust aloft. As multispectral imagery has gained operational acceptance, additional work has begun to develop quantitative products to assist users with their interpretation of RGB imagery. For example, National Center forecasters often use an "Air Mass" RGB to differentiate between possible stratospheric /tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. Research was done to demonstrate how the NUCAPS CrIS/ATMS infrared retrieved temperature, moisture, and ozone profiles can aid Air Mass RGB imagery interpretation as well as how these quantitative values are important for anticipating tropical to extratropical transition events. In addition, an enhanced stratospheric depth product was developed to identify the dynamic tropopause from the NUCAPS retrieved ozone profiles to aid identification of stratospheric air influence. Forecasters from National Centers
Short-term load forecast using trend information and process reconstruction
Energy Technology Data Exchange (ETDEWEB)
Santos, P.J.; Pires, A.J.; Martins, J.F. [Instituto Politecnico de Setubal (Portugal). Dept. of Electrical Engineering; Martins, A.G. [University of Coimbra (Portugal). Dept. of Electrical Engineering; Mendes, R.V. [Instituto Superior Tecnico, Lisboa (Portugal). Laboratorio de Mecatronica
2005-07-01
The algorithms for short-term load forecast (STLF), especially within the next-hour horizon, belong to a group of methodologies that aim to render more effective the actions of planning, operating and controlling electric energy systems (EES). In the context of the progressive liberalization of the electricity sector, unbundling of the previous monopolistic structure emphasizes the need for load forecast, particularly at the network level. Methodologies such as artificial neural networks (ANN) have been widely used in next-hour load forecast. Designing an ANN requires the proper choice of input variables, avoiding overfitting and an unnecessarily complex input vector (IV). This may be achieved by trying to reduce the arbitrariness in the choice of endogenous variables. At a first stage, we have applied the mathematical techniques of process-reconstruction to the underlying stochastic process, using coding and block entropies to characterize the measure and memory range. At a second stage, the concept of consumption trend in homologous days of previous weeks has been used. The possibility to include weather-related variables in the IV has also been analysed, the option finally being to establish a model of the non-weather sensitive type. The paper uses a real-life case study. (author)
Short-Term Forecasts Using NU-WRF for the Winter Olympics 2018
Srikishen, Jayanthi; Case, Jonathan L.; Petersen, Walter A.; Iguchi, Takamichi; Tao, Wei-Kuo; Zavodsky, Bradley T.; Molthan, Andrew
2017-01-01
The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment -PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.
Application of Interval Type-2 Fuzzy Logic System in Short Term Load Forecasting on Special Days
Directory of Open Access Journals (Sweden)
Agus Dharma
2011-05-01
Full Text Available This paper presents the application of Interval Type-2 fuzzy logic systems (Interval Type-2 FLS in short term load forecasting (STLF on special days, study case in Bali Indonesia. Type-2 FLS is characterized by a concept called footprint of uncertainty (FOU that provides the extra mathematical dimension that equips Type-2 FLS with the potential to outperform their Type-1 counterparts. While a Type-2 FLS has the capability to model more complex relationships, the output of a Type-2 fuzzy inference engine needs to be type-reduced. Type reduction is used by applying the Karnik-Mendel (KM iterative algorithm. This type reduction maps the output of Type-2 FSs into Type-1 FSs then the defuzzification with centroid method converts that Type-1 reduced FSs into a number. The proposed method was tested with the actual load data of special days using 4 days peak load before special days and at the time of special day for the year 2002-2006. There are 20 items of special days in Bali that are used to be forecasted in the year 2005 and 2006 respectively. The test results showed an accurate forecasting with the mean average percentage error of 1.0335% and 1.5683% in the year 2005 and 2006 respectively.
Directory of Open Access Journals (Sweden)
Shuping Cai
2018-03-01
Full Text Available Weather information is an important factor in short-term load forecasting (STLF. However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introduction and variable selection in STLF. Fisher information computation for one-dimensional and multidimensional weather variables is first described, and then the introduction of meteorological factors and variables selection for STLF models are discussed in detail. On this basis, different forecasting models with the proposed methodology are established. The proposed methodology is implemented on real data obtained from Electric Power Utility of Zhenjiang, Jiangsu Province, in southeast China. The results show the advantages of the proposed methodology in comparison with other traditional ones regarding prediction accuracy, and it has very good practical significance. Therefore, it can be used as a unified method for introducing weather variables into STLF models, and selecting their features.
Performance of fuzzy approach in Malaysia short-term electricity load forecasting
Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee
2014-12-01
Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in forecasting electricity load when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.
International Nuclear Information System (INIS)
Voituriez, T.
2009-01-01
We review in this study the different factors which have been presented by the scientific community as possible explanations of the sudden upsurge in commodity prices between 2006 and 2008. We examine whether scientific evidence validates any causal relationship, and particularly emphasize the role of explanatory variables underpinning the co-movement of energy and food price rises. Our aim is to provide an up-to-date understanding of food and energy market relationships, so as to better anticipate the possible changes in the evolution of prices in the coming years. (author)
Directory of Open Access Journals (Sweden)
Jaime Buitrago
2017-01-01
Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.
Short-term forecasting of thunderstorms at Kennedy Space Center, based on the surface wind field
Watson, Andrew I.; Lopez, Raul E.; Holle, Ronald L.; Daugherty, John R.; Ortiz, Robert
1989-01-01
Techniques incorporating wind convergence that can be used for the short-term prediction of thunderstorm development are described. With these techniques, the convergence signal is sensed by the wind network array 15 to 90 min before actual storm development. Particular attention is given to the convergence cell technique (which has been applied at the Kennedy Space Center) where each convective region is analyzed independently. It is noted that, while the monitoring of areal and cellular convergence can be used to help locate the seeds of developing thunderstorms and pinpoint the lightning threat areas, this forecasting aid cannot be used in isolation.
Short-term droughts forecast using Markov chain model in Victoria, Australia
Rahmat, Siti Nazahiyah; Jayasuriya, Niranjali; Bhuiyan, Muhammed A.
2017-07-01
A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.
Price-signals and long term equilibrium. Reconsidering the organisation of electricity markets
International Nuclear Information System (INIS)
Finon, Dominique; Defeuilley, Christophe; Marty, Frederic
2009-11-01
The purpose of this article is to show that the reform of the electricity sector, based on a framework of interpretation in which the short-term/long-term articulation is made by the market price, does not result in an efficient result in terms of investment. After a presentation of a bibliographical review on investment in an uncertain context, the authors present a model of decentralised electricity markets which backs reforms. They highlight issues related to production investment which remain unresolved, and which may result in socially inefficient choices on the long term. They report an analysis of two solutions of industrial organisations, long term contracts and vertical and horizontal integration, which could solve these problems
Energy Technology Data Exchange (ETDEWEB)
Orwig, Kirsten D. [National Renewable Energy Laboratory (NREL), Golden, CO (United States). Transmission Grid Integration; Benjamin, Stan; Wilczak, James; Marquis, Melinda [National Oceanic and Atmospheric Administration, Boulder, CO (United States). Earth System Research Lab.; Stern, Andrew [National Oceanic and Atmospheric Administration, Silver Spring, MD (United States); Clark, Charlton; Cline, Joel [U.S. Department of Energy, Washington, DC (United States). Wind and Water Power Program; Finley, Catherine [WindLogics, Grand Rapids, MN (United States); Freedman, Jeffrey [AWS Truepower, Albany, NY (United States)
2012-07-01
The current state-of-the-art wind power forecasting in the 0- to 6-h timeframe has levels of uncertainty that are adding increased costs and risks to the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: (1) a one-year field measurement campaign within two regions; (2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and (3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provide an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis. (orig.)
CORRECTION OF FORECASTS OF INTERRELATED CURRENCY PAIRS IN TERMS OF SYSTEMS OF BALANCE RATIOS
Gertsekovich D. A.
2015-01-01
In this paper the problem of exchange rates forecast is logically considered a) traditionally as a task of forecast on the base of «stand-alone» equations of autoregression for each currency pair and b) as a result of forecast correction of autoregression equations system on the base of boundary conditions of balance ratios systems. As a criterion for quality of forecast constructed with empirical models we take the sum of deficiency quadrates of forecasts estimated for deductive currency pai...
Two-part pricing structure in long-term gas sales contracts
International Nuclear Information System (INIS)
Slocum, J.C.; Lee, S.Y.
1992-01-01
Although the incremental electricity generation market has the potential to be a major growth area for natural gas demand in the U.S., it may never live up to such promise unless gas suppliers are more willing to enter into long-term gas sales agreements necessary to nurture this segment of the industry. The authors submit that producer reluctance to enter into such long-term sales agreements can be traced, at least in part to the differing contract price requirements between gas producers and buyers. This paper will address an evolving solution to this contracting dilemma - the development of a two-part pricing structure for the gas commodity. A two-part pricing structure includes a usage or throughput charge established in a way to yield a marginal gas cost competitive with electric utility avoided costs, and a reservation charge established to guarantee a minimum cash flow to the producer. Moreover, the combined effect of the two charges may yield total revenues that better reflect the producer's replacement cost of the reserves committed under the contract. 2 tabs
Wu, Ming-Chang; Lin, Gwo-Fong; Feng, Lei; Hwang, Gong-Do
2017-04-01
In Taiwan, heavy rainfall brought by typhoons often causes serious disasters and leads to loss of life and property. In order to reduce the impact of these disasters, accurate rainfall forecasts are always important for civil protection authorities to prepare proper measures in advance. In this study, a methodology is proposed for providing very short-term (1- to 6-h ahead) rainfall forecasts in a basin-scale area. The proposed methodology is developed based on the use of analogy reasoning approach to effectively integrate the ensemble precipitation forecasts from a numerical weather prediction system in Taiwan. To demonstrate the potential of the proposed methodology, an application to a basin-scale area (the Choshui River basin located in west-central Taiwan) during five typhoons is conducted. The results indicate that the proposed methodology yields more accurate hourly rainfall forecasts, especially the forecasts with a lead time of 1 to 3 hours. On average, improvement of the Nash-Sutcliffe efficiency coefficient is about 14% due to the effective use of the ensemble forecasts through the proposed methodology. The proposed methodology is expected to be useful for providing accurate very short-term rainfall forecasts during typhoons.
Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.
2018-03-01
Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.
Jiang, GJ
1998-01-01
This paper develops a nonparametric model of interest rate term structure dynamics based an a spot rate process that permits only positive interest rates and a market price of interest rate risk that precludes arbitrage opportunities. Both the spot rate process and the market price of interest rate
Energy Technology Data Exchange (ETDEWEB)
Bolinger, Mark; Wiser, Ryan; Golove, William
2004-07-17
Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then--contrary to common practice--any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000-2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation.
International Nuclear Information System (INIS)
Bolinger, Mark; Wiser, Ryan; Golove, William
2006-01-01
Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then-contrary to common practice-any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000 to 2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation. (author)
Zavodsky, Bradley; McCarty, Will; Chou, Shih-Hung; Jedlovec, Gary
2009-01-01
The Atmospheric Infrared Sounder (AIRS) is acting as a heritage and risk reduction instrument for the Cross-track lnfrared Sounder (CrIS) to be flown aboard the NPP and NPOESS satellites. The hyperspectral nature of AIRS and CrIS provides high-quality soundings that, along with their asynoptic observation time over North America, make them attractive sources to fill the spatial and temporal data voids in upper air temperature and moisture measurements for use in data assimilation and numerical weather prediction. Observations from AlRS can be assimilated either as direct radiances or retrieved thermodynamic profiles, and the Short-Term Prediction Research and Transition (SPORT) Center at NASA's Marshall Space Flight Center has used both data types to improve short-term (0-48h), regional forecasts. The purpose of this paper is to share SPORT'S experiences using AlRS radiances and retrieved profiles in regional data assimilation activities by showing that proper handling of issues-including cloud contamination and land emissivity characterization-are necessary to produce optimal analyses and forecasts.
Solar radiation forecasting in the short- and medium-term under all sky conditions
International Nuclear Information System (INIS)
Alonso-Montesinos, J.; Batlles, F.J.
2015-01-01
Meteorological conditions are decisive in solar plant management and electricity generation. Any increases or decreases in solar radiation mean a plant has to adapt its operation method to the climatological phenomena. An unexpected atmospheric change can provoke a range of problems related to various solar plant components affecting the electricity generation system and, in consequence, causing alterations in the electricity grid. Therefore, predicting atmospheric features is key to managing solar plants and is therefore necessary for correct electrical grid management. Accordingly, a solar radiation forecast model is presented, where the three solar components (beam, diffuse and global) are predicted over the short- and medium-term (up to three hours) for all sky conditions, demonstrating its potential as a useful application in decision-making processes at solar power plants. - Highlights: • A solar radiation forecasting has been proposed over the short- and medium-term. • The three radiation components have been predicted under all sky conditions. • Cloud motion and the Heliosat-2 model are combined for predicting solar radiation. • Results have been presented for cloudless, partially-cloudy and overcast conditions. • For beam and global radiation, the nRMSE value is lower than 10% under clear skies
2018-01-01
NEW YORK – The outlook for international business travel is generally optimistic, according to the Global Business Travel Forecast 2018 published by American Express Global Business Travel (GBT). Demand is being driven by a steadily improving global economy and growing confidence among the business and investor communities. Hotel performance is expected to improve globally 29 January 2018
Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts
AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.
2014-12-01
Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using
Online short-term heat load forecasting for single family houses
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
2013-01-01
This paper presents a method for forecasting the load for heating in a single-family house. Both space and hot tap water heating are forecasted. The forecasting model is built using data from sixteen houses in Sønderborg, Denmark, combined with local climate measurements and weather forecasts....... Every hour the hourly heat load for each house the following two days is forecasted. The forecast models are adaptive linear time-series models and the climate inputs used are: ambient temperature, global radiation, and wind speed. A computationally efficient recursive least squares scheme is used...
Testing the data assimilation technique for short-term wind forecast in the PBL: a case study
Avolio, E.; Federico, S.; Sempreviva, A. M.; Calidonna, C. R.; Courtney, M.
2012-04-01
In this contribution we show the results of using a data assimilation technique to improve the short-term wind forecast at a site in northern Europe. The assimilation technique is a simple four-dimensional nudging and, for this purpose, we set-up a version of the Regional Atmospheric Modelling System. The nudging technique consists of adding an extra-tendency term, to the prognostic equations of the zonal and meridional wind components, which forces the variable toward the observations. dφm- (φobs -φm-) dt = τ f(r) (1) where φmis model variable (zonal or meridional wind component), φobs is the observation, τ is relaxation time scale (900 s), f(r) is a Gaussian function f(r) = e0-(r/r)Λ2 , and r0=50 km. The method was applied in Denmark where suitable observations were available at the Danish National Test Station for Large Wind Turbines, located at Høvsøre (Western Jutland, Denmark), and refer to the measurements of vertical wind profiles; the instrument is the WINDCUBE Doppler LIDAR. Data were available every 10 minutes at the following levels: 40 m, 60 m, 80 m, 100 m, 116 m, 130 m, 160 m, 200 m, 250 m and 300 m. The data represent the average of the measurement for the previous 10 minutes. Only data available at the 00 minutes of each hour were considered in this study. The RAMS model is set-up with four nested grids. The fourth grid has 1 km horizontal resolution and is centred over the site. Model levels do not coincide with the measurement levels, and, to assimilate and to verify the forecast, the observations were linearly interpolated to the model levels. The physical configuration of the model is the one adopted for operational forecast over the Calabria Region in South Italy. In order to show the potential impact of the nudging technique, we run the model in two different configurations: (a) a simple forecast and (b) an analysis-forecast run. The runs duration is twenty-four hours for both configurations. For each configuration, simulations were
Directory of Open Access Journals (Sweden)
Yuyang Gao
2016-09-01
Full Text Available With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.
International Nuclear Information System (INIS)
Ardakani, F.J.; Ardehali, M.M.
2014-01-01
Highlights: • Novel effects of DSM data on electricity consumption forecasting is examined. • Optimal ANN models based on IPSO and SFL algorithms are developed. • Addition of DSM data to socio-economic indicators data reduces MAPE by 36%. - Abstract: Worldwide implementation of demand side management (DSM) programs has had positive impacts on electrical energy consumption (EEC) and the examination of their effects on long-term forecasting is warranted. The objective of this study is to investigate the effects of historical DSM data on accuracy of EEC modeling and long-term forecasting. To achieve the objective, optimal artificial neural network (ANN) models based on improved particle swarm optimization (IPSO) and shuffled frog-leaping (SFL) algorithms are developed for EEC forecasting. For long-term EEC modeling and forecasting for the U.S. for 2010–2030, two historical data types used in conjunction with developed models include (i) EEC and (ii) socio-economic indicators, namely, gross domestic product, energy imports, energy exports, and population for 1967–2009 period. Simulation results from IPSO-ANN and SFL-ANN models show that using socio-economic indicators as input data achieves lower mean absolute percentage error (MAPE) for long-term EEC forecasting, as compared with EEC data. Based on IPSO-ANN, it is found that, for the U.S. EEC long-term forecasting, the addition of DSM data to socio-economic indicators data reduces MAPE by 36% and results in the estimated difference of 3592.8 MBOE (5849.9 TW h) in EEC for 2010–2030
F. Ravazzolo (Francesco); C. Zhou (Chen); C. Huurman
2007-01-01
textabstractIn the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been
Natural gas pricing policies in Southeast Asia
International Nuclear Information System (INIS)
Pacudan, R.B.
1998-01-01
The very dynamic economies of Southeast Asia have recently been experiencing a rapid increase in energy demand. Parallel to this development, there has been an increase in the utilization of indigenous natural gas resources. This article reviews gas-pricing policies in the region, which partly explain the rise in gas utilization. Although diverse, energy pricing policies in Southeast Asia address the common objective of enhancing domestic gas production and utilization. The article concludes that a more rational gas-pricing policy framework is emerging in the region. In global terms, gas pricing in the region tends to converge in a market-related framework, despite the many different pricing objectives of individual countries, and the predominance of non-economic pricing objectives in certain countries (especially gas-rich nations). Specifically, governments have been flexible enough to follow global trends and initiate changes in contractual agreements (pricing and profit-sharing), giving oil companies more favourable terms, and encouraging continued private investment in gas development. At the same time, promotional pricing has also been used to increase utilization of gas, through set prices and adjusted taxes achieving a lower price level compared to substitute fuels. For an efficient gas-pricing mechanism, refinements in the pricing framework should be undertaken, as demand for gas approaches existing and/or forecast production capacities. (author)
Directory of Open Access Journals (Sweden)
Hongshan Zhao
2012-05-01
Full Text Available Short-term solar irradiance forecasting (STSIF is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV, and the Levenberg-Marquardt algorithm (LMA is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS, and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.
Directory of Open Access Journals (Sweden)
Alexei I. Podberezkin
2016-01-01
Full Text Available The article is the form of scientific report on the results of three year long project on methodology of long term forecasting the development of the system of international relations. The methodology is based on the following assumptions: input information is accurate and complete; international relations constitute a system, scenarios for different levels of international relations development are hierarchically interdependent; the speed of development is different on various levels of international relations; various national capabilities affect the development; elites affect international relations; civil society affect international relations. Based on this assumption the author builds the most probable scenario of intercivilizational relations which is military coercive interaction. The role of soft power will increase its share in the toolkit of the confrontational politics. To win in this confrontation it is necessary to review the current practices of strategic forecasting and planning and to rebuild the entire military organization of the Russian army. The principal condition for the victory is development of national human capital, as well as the formation of the national ideology.
Energy Technology Data Exchange (ETDEWEB)
Cadren, M
1998-06-23
The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It`s filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter`s. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach`s and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author) 153 refs.
Nonlinear Dynamical Modes as a Basis for Short-Term Forecast of Climate Variability
Feigin, A. M.; Mukhin, D.; Gavrilov, A.; Seleznev, A.; Loskutov, E.
2017-12-01
We study abilities of data-driven stochastic models constructed by nonlinear dynamical decomposition of spatially distributed data to quantitative (short-term) forecast of climate characteristics. We compare two data processing techniques: (i) widely used empirical orthogonal function approach, and (ii) nonlinear dynamical modes (NDMs) framework [1,2]. We also make comparison of two kinds of the prognostic models: (i) traditional autoregression (linear) model and (ii) model in the form of random ("stochastic") nonlinear dynamical system [3]. We apply all combinations of the above-mentioned data mining techniques and kinds of models to short-term forecasts of climate indices based on sea surface temperature (SST) data. We use NOAA_ERSST_V4 dataset (monthly SST with space resolution 20 × 20) covering the tropical belt and starting from the year 1960. We demonstrate that NDM-based nonlinear model shows better prediction skill versus EOF-based linear and nonlinear models. Finally we discuss capability of NDM-based nonlinear model for long-term (decadal) prediction of climate variability. [1] D. Mukhin, A. Gavrilov, E. Loskutov , A.Feigin, J.Kurths, 2015: Principal nonlinear dynamical modes of climate variability, Scientific Reports, rep. 5, 15510; doi: 10.1038/srep15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J., 2016: Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. [3] Ya. Molkov, D. Mukhin, E. Loskutov, A. Feigin, 2012: Random dynamical models from time series. Phys. Rev. E, Vol. 85, n.3.
DEFF Research Database (Denmark)
Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat
2013-01-01
A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... In this study, a MOS based on multiple linear regression is proposed: the model screens the most relevant NWP forecast variables and selects the best predictors to fit a regression equation that minimizes the forecast errors, utilizing wind farm power output measurements as input. The performance of the method...... is evaluated in two wind farms, located in different topographical areas and with different NWP grid spacing. Because of the high seasonal variability of NWP forecasts, it was considered appropriate to implement monthly stratified MOS. In both wind farms, the first predictors were always wind speeds (at...
Directory of Open Access Journals (Sweden)
Danladi Ali
2018-03-01
Full Text Available Long-term load forecasting provides vital information about future load and it helps the power industries to make decision regarding electrical energy generation and delivery. In this work, fuzzy – neuro model is developed to forecast a year ahead load in relation to weather parameter (temperature and humidity in Mubi, Adamawa State. It is observed that: electrical load increased with increase in temperature and relative humidity does not show notable effect on electrical load. The accuracy of the prediction is obtained at 98.78% with the corresponding mean absolute percentage error (MAPE of 1.22%. This confirms that fuzzy – neuro is a good tool for load forecasting. Keywords: Electrical load, Load forecasting, Fuzzy logic, Back propagation, Neuro-fuzzy, Weather parameter
Near-term probabilistic forecast of significant wildfire events for the Western United States
Haiganoush K. Preisler; Karin L. Riley; Crystal S. Stonesifer; Dave E. Calkin; Matt Jolly
2016-01-01
Fire danger and potential for large fires in the United States (US) is currently indicated via several forecasted qualitative indices. However, landscape-level quantitative forecasts of the probability of a large fire are currently lacking. In this study, we present a framework for forecasting large fire occurrence - an extreme value event - and evaluating...
AN ELM FOR BI-CLASSIFICATION OF VERTICALLY BUNDLED ELECTRICITY MARKET PRICES
Directory of Open Access Journals (Sweden)
S Anbazhagan
2018-10-01
Full Text Available Electricity price forecasting is a challenging problem owing to the very great volatility of price which depends on many factors. This is especially prominent for both producers and consumers where a versatile price forecasting is crucial. This paper contributes an extreme learning machine (ELM to classify the prices. These price classifications are essential since all market players do not know the precise value of future prices in their deciding procedure. In this paper, bi-classification model is proposed for prices utilizing the pre-specified price threshold. Three alternative classification models based on neural networks (NNs are also proposed in bi-classification of prices. The performance of the proposed models is compared in terms of classification error and accuracy. The simulation results show that the ELM classification model is superior compared to three other classification models based on NNs. The performances of our models are evaluated using real data from vertically unbundled mainland Spain power system market.
The long-term forecast of Taiwan's energy supply and demand: LEAP model application
Energy Technology Data Exchange (ETDEWEB)
Huang, Yophy, E-mail: yohuanghaka@gmail.com [Deptartment of Public Finance and Tax Administration, National Taipei College of Business, Taipei Taiwan, 10051 (China); Bor, Yunchang Jeffrey [Deptartment of Economics, Chinese Culture University, Yang-Ming-Shan, Taipei, 11114, Taiwan (China); Peng, Chieh-Yu [Statistics Department, Taoyuan District Court, No. 1 Fazhi Road, Taoyuan City 33053, Taiwan (China)
2011-11-15
The long-term forecasting of energy supply and demand is an extremely important topic of fundamental research in Taiwan due to Taiwan's lack of natural resources, dependence on energy imports, and the nation's pursuit of sustainable development. In this article, we provide an overview of energy supply and demand in Taiwan, and a summary of the historical evolution and current status of its energy policies, as background to a description of the preparation and application of a Long-range Energy Alternatives Planning System (LEAP) model of Taiwan's energy sector. The Taiwan LEAP model is used to compare future energy demand and supply patterns, as well as greenhouse gas emissions, for several alternative scenarios of energy policy and energy sector evolution. Results of scenarios featuring 'business-as-usual' policies, aggressive energy-efficiency improvement policies, and on-schedule retirement of Taiwan's three existing nuclear plants are provided and compared, along with sensitivity cases exploring the impacts of lower economic growth assumptions. A concluding section provides an interpretation of the implications of model results for future energy and climate policies in Taiwan. - Research Highlights: > The LEAP model is useful for international energy policy comparison. > Nuclear power plants have significant, positive impacts on CO{sub 2} emission. > The most effective energy policy is to adopt demand-side management. > Reasonable energy pricing provides incentives for energy efficiency and conservation. > Financial crisis has less impact on energy demand than aggressive energy policy.
Long-Term Probabilistic Forecast for M ≥ 5.0 Earthquakes in Iran
Talebi, Mohammad; Zare, Mehdi; Peresan, Antonella; Ansari, Anooshiravan
2017-04-01
In this study, a long-term forecasting model is proposed to evaluate the probabilities of forthcoming M ≥ 5.0 earthquakes on a 0.2° grid for an area including the Iranian plateau. The model is built basically from smoothing the locations of preceding events, assuming a spatially heterogeneous and temporally homogeneous Poisson point process for seismicity. In order to calculate the expectations, the space distribution, from adaptively smoothed seismicity, has been scaled in time and magnitude by average number of events over a 5-year forecasting horizon and a tapered magnitude distribution, respectively. The model has been adjusted and applied considering two earthquake datasets: a regional unified catalog (MB14) and a global catalog (ISC). Only the events with M ≥ 4.5 have been retained from the datasets, based on preliminary completeness data analysis. A set of experiments has been carried out, testing different options in the model application, and the average probability gains for target earthquakes have been estimated. By optimizing the model parameters, which leads to increase of the predictive power of the model, it is shown that a declustered catalog has an advantage over a non-declustered one, and a low-magnitude threshold of a learning catalog can be preferred to a larger one. In order to examine the significance of the model results at 95% confidence level, a set of retrospective tests, namely, the L test, the N test, the R test, and the error diagram test, has been performed considering 13 target time windows. The error diagram test shows that the forecast results, obtained for both the two input catalogs, mostly fall outside the 5% critical region that is related to results from a random guess. The L test and the N test could not reject the model for most of the time intervals (i.e. 85 and 62% of times for the ISC and MB14 forecasts, respectively). Furthermore, after backwards extending the time span of the learning catalogs and repeating the L
Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory
DEFF Research Database (Denmark)
López, Erick; Allende, Héctor; Gil, Esteban
2018-01-01
Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately...... predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables...... involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed...
An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting
Directory of Open Access Journals (Sweden)
Qiang Ni
2017-10-01
Full Text Available High quality photovoltaic (PV power prediction intervals (PIs are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.
Short-term Probabilistic Load Forecasting with the Consideration of Human Body Amenity
Directory of Open Access Journals (Sweden)
Ning Lu
2013-02-01
Full Text Available Load forecasting is the basis of power system planning and design. It is important for the economic operation and reliability assurance of power system. However, the results of load forecasting given by most existing methods are deterministic. This study aims at probabilistic load forecasting. First, the support vector machine regression is used to acquire the deterministic results of load forecasting with the consideration of human body amenity. Then the probabilistic load forecasting at a certain confidence level is given after the analysis of error distribution law corresponding to certain heat index interval. The final simulation shows that this probabilistic forecasting method is easy to implement and can provide more information than the deterministic forecasting results, and thus is helpful for decision-makers to make reasonable decisions.
Swinand, Gregory P; O'Mahoney, Amy
2014-01-01
This paper studies the impact of wind generation on system costs and prices in Ireland. The need to mitigate climate change, achieve renewables energy targets, and use renewable sources of energy means that many countries are considering greater levels of wind generation in their power generation mix. The overall impact of wind generation on system costs and performance has only been studied recently, and often with limited actual data from power systems with increased wind penetration. The p...
Directory of Open Access Journals (Sweden)
Alexander G. Kerl
2011-04-01
Full Text Available This study analyzes the accuracy of forecasted target prices within analysts’ reports. We compute a measure for target price forecast accuracy that evaluates the ability of analysts to exactly forecast the ex-ante (unknown 12-month stock price. Furthermore, we determine factors that explain this accuracy. Target price accuracy is negatively related to analyst-specific optimism and stock-specific risk (measured by volatility and price-to-book ratio. However, target price accuracy is positively related to the level of detail of each report, company size and the reputation of the investment bank. The potential conflicts of interests between an analyst and a covered company do not bias forecast accuracy.
An empirical model of daily highs and lows of West Texas Intermediate crude oil prices
International Nuclear Information System (INIS)
He, Angela W.W.; Wan, Alan T.K.; Kwok, Jerry T.K.
2010-01-01
There is a large collection of literature on energy price forecasting, but most studies typically use monthly average or close-to-close daily price data. In practice, the daily price range constructed from the daily high and low also contains useful information on price volatility and is used frequently in technical analysis. The interaction between the daily high and low and the associated daily range has been examined in several recent studies on stock price and exchange rate forecasts. The present paper adopts a similar approach to analyze the behaviour of the West Texas Intermediate (WTI) crude oil price over a ten-year period. We find that daily highs and lows of the WTI oil price are cointegrated, with the error correction term being closely approximated by the daily price range. Two forecasting models, one based on a vector error correction mechanism and the other based on a transfer function framework with the range taken as a driver variable, are presented for forecasting the daily highs and lows. The results show that both of these models offer significant advantages over the naive random walk and univariate ARIMA models in terms of out-of-sample forecast accuracy. A trading strategy that makes use of the daily high and low forecasts is further developed. It is found that this strategy generally yields very reasonable trading returns over an evaluation period of about two years. (author)
Energy systems scenario modelling and long term forecasting of hourly electricity demand
Directory of Open Access Journals (Sweden)
Poul Alberg Østergaard
2015-06-01
Full Text Available The Danish energy system is undergoing a transition from a system based on storable fossil fuels to a system based on fluctuating renewable energy sources. At the same time, more of and more of the energy system is becoming electrified; transportation, heating and fuel usage in industry and elsewhere. This article investigates the development of the Danish energy system in a medium year 2030 situation as well as in a long-term year 2050 situation. The analyses are based on scenario development by the Danish Climate Commission. In the short term, it is investigated what the effects will be of having flexible or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model. The results show that even with a limited short-term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrated wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long-term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps and electric vehicles in the long-term future overshadows any effects of changes in hourly demand curve profiles.
DEFF Research Database (Denmark)
Christensen, Bent Jesper; van der Wel, Michel
We develop a new empirical approach to term structure analysis that allows testing for time-varying risk premia and for the absence of arbitrage opportunities based on the drift restriction within the Heath, Jarrow and Morton (1992) framework. As in the equity case, a zero intercept condition...... of the risk premium is associated with the slope factor, and individual risk prices depend on own past values, factor realizations, and past values of other risk prices, and are significantly related to the output gap, consumption, and the equity risk price. The absence of arbitrage opportunities is strongly...
Short-term traffic flow forecasting based on feature selection with mutual information
Yuan, Zhengwu; Tu, Chuan
2017-05-01
Traffic flow forecasting is related to many traffic variables, and how to select appropriate traffic variable combination is very important to traffic flow forecasting, which can reduce the cost of calculation and improve the forecasting precision. In this paper, a feature selection technique with mutual information is proposed for this purpose. Firstly, the mutual information is used to evaluate the relevance and redundancy of variables, and feature selection is used to select the relevant variables and filter out the redundancy between the selected variables. Secondly, BP neural network is used as the forecasting engine. Finally, a numerical example of traffic flow data from Pems is used to verify the forecasting performance of the proposed method, the results indicate that the proposed method can effectively reduce the cost of calculation and also improve the model forecasting precision.
CORRECTION OF FORECASTS OF INTERRELATED CURRENCY PAIRS IN TERMS OF SYSTEMS OF BALANCE RATIOS
Directory of Open Access Journals (Sweden)
Gertsekovich D. A.
2015-03-01
Full Text Available In this paper the problem of exchange rates forecast is logically considered a traditionally as a task of forecast on the base of «stand-alone» equations of autoregression for each currency pair and b as a result of forecast correction of autoregression equations system on the base of boundary conditions of balance ratios systems. As a criterion for quality of forecast constructed with empirical models we take the sum of deficiency quadrates of forecasts estimated for deductive currency pairs. Practical approval confirmed that deductive models meet common requirements, provide accepted precision, show resistance to initial data and are free from series of deficiency of one index. However, extreme forecast errors tell that practical application of the approach offered needs further improvement.
Radar Refractivity Retrieval: Validation and Application to Short-Term Forecasting.
Weckwerth, Tammy M.; Pettet, Crystalyne R.; Fabry, Frédéric; Park, Shinju; Lemone, Margaret A.; Wilson, James W.
2005-03-01
This study will validate the S-band dual-polarization Doppler radar (S-Pol) radar refractivity retrieval using measurements from the International H2O Project conducted in the southern Great Plains in May-June 2002. The range of refractivity measurements during this project extended out to 40-60 km from the radar. Comparisons between the radar refractivity field and fixed and mobile mesonet refractivity values within the S-Pol refractivity domain show a strong correlation. Comparisons between the radar refractivity field and low-flying aircraft also show high correlations. Thus, the radar refractivity retrieval provides a good representation of low-level atmospheric refractivity. Numerous instruments that profile the temperature and moisture are also compared with the refractivity field. Radiosonde measurements, Atmospheric Emitted Radiance Interferometers, and a vertical-pointing Raman lidar show good agreement, especially at low levels. Under most daytime summertime conditions, radar refractivity measurements are representative of an 250-m-deep layer. Analyses are also performed on the utility of refractivity for short-term forecasting applications. It is found that the refractivity field may detect low-level boundaries prior to the more traditional radar reflectivity and Doppler velocity fields showing their existence. Data from two days on which convection initiated within S-Pol refractivity range suggest that the refractivity field may exhibit some potential utility in forecasting convection initiation. This study suggests that unprecedented advances in mapping near-surface water vapor and subsequent improvements in predicting convective storms could result from implementing the radar refractivity retrieval on the national network of operational radars.
Directory of Open Access Journals (Sweden)
Jaime Lloret
2013-08-01
Full Text Available Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-25
This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.
Price Changes, Resource Adjustments and Rational Expectations
DEFF Research Database (Denmark)
Hoffmann, Kira
a decrease in prices through managers that anticipated the drop in demand and proactively lower selling prices and cut resources. Moreover, this study provides evidence for the moderating effect of managerial forecast accuracy on the relationship between demand uncertainty and cost elasticity. Findings show......This study investigates the relationship between the accuracy of managerial demand expectations, resource adjustment decisions and selling price changes. In line with rational expectation theory, it is argued that managers adjust resources and selling prices differently in response to expected...... compared to unexpected demand shocks. The association is tested using the empirical concept of cost stickiness. Cost stickiness arises as a consequence of asymmetric resource or price adjustments. Resource and price adjustments are termed asymmetric if the magnitude of change is different for increases...
Directory of Open Access Journals (Sweden)
F. Anctil
2009-11-01
Full Text Available Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap.
In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS, especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.
Sankarasubramanian, A.; Lall, Upmanu; Souza Filho, Francisco Assis; Sharma, Ashish
2009-11-01
Probabilistic, seasonal to interannual streamflow forecasts are becoming increasingly available as the ability to model climate teleconnections is improving. However, water managers and practitioners have been slow to adopt such products, citing concerns with forecast skill. Essentially, a management risk is perceived in "gambling" with operations using a probabilistic forecast, while a system failure upon following existing operating policies is "protected" by the official rules or guidebook. In the presence of a prescribed system of prior allocation of releases under different storage or water availability conditions, the manager has little incentive to change. Innovation in allocation and operation is hence key to improved risk management using such forecasts. A participatory water allocation process that can effectively use probabilistic forecasts as part of an adaptive management strategy is introduced here. Users can express their demand for water through statements that cover the quantity needed at a particular reliability, the temporal distribution of the "allocation," the associated willingness to pay, and compensation in the event of contract nonperformance. The water manager then assesses feasible allocations using the probabilistic forecast that try to meet these criteria across all users. An iterative process between users and water manager could be used to formalize a set of short-term contracts that represent the resulting prioritized water allocation strategy over the operating period for which the forecast was issued. These contracts can be used to allocate water each year/season beyond long-term contracts that may have precedence. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser optimization model that can support such an allocation process is presented. The application of this conceptual model is explored using data for the Jaguaribe Metropolitan Hydro System in Ceara, Brazil. The performance
OPTIMIZING HOTEL DYNAMIC PRICES
Directory of Open Access Journals (Sweden)
A. M. Bandalouski
2016-01-01
Full Text Available An approach to solvе a problem of determining optimal dynamic prices for hotel rooms is suggested. It includes selection of input parameters for the succeeding mathematical analysis, disaggregation of the demand into several categories, demand forecasting, simulation of demand- price relations, and a mathematical programming model for price optimization.
Mean Reversion in Stock Prices: Implications for Long-Term Investors
Spierdijk, L.; Bikker, J.A.
2012-01-01
This paper discusses the implications of mean reversion in stock prices for longterm investors such as pension funds. We start with a general definition of a meanreverting price process and explain how mean reversion in stock prices is related to mean reversion in stock returns. Subsequently, we
International Nuclear Information System (INIS)
Wu, Jie; Wang, Jianzhou; Lu, Haiyan; Dong, Yao; Lu, Xiaoxiao
2013-01-01
Highlights: ► The seasonal and trend items of the data series are forecasted separately. ► Seasonal item in the data series is verified by the Kendall τ correlation testing. ► Different regression models are applied to the trend item forecasting. ► We examine the superiority of the combined models by the quartile value comparison. ► Paired-sample T test is utilized to confirm the superiority of the combined models. - Abstract: For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall τ correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests
Directory of Open Access Journals (Sweden)
KAMPOUROPOULOS, K.
2014-02-01
Full Text Available This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS and Genetic Algorithms (GA. The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.
Energy Technology Data Exchange (ETDEWEB)
Freedman, Jeffrey M. [AWS Truepower, LLC, Albany, NY (United States); Manobianco, John [MESO, Inc., Troy, NY (United States); Schroeder, John [Texas Tech Univ., Lubbock, TX (United States). National Wind Inst.; Ancell, Brian [Texas Tech Univ., Lubbock, TX (United States). Atmospheric Science Group; Brewster, Keith [Univ. of Oklahoma, Norman, OK (United States). Center for Analysis and Prediction of Storms; Basu, Sukanta [North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences; Banunarayanan, Venkat [ICF International (United States); Hodge, Bri-Mathias [National Renewable Energy Lab. (NREL), Golden, CO (United States); Flores, Isabel [Electricity Reliability Council of Texas (United States)
2014-04-30
This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.
From probabilistic forecasts to statistical scenarios of short-term wind power production
DEFF Research Database (Denmark)
Pinson, Pierre; Papaefthymiou, George; Klockl, Bernd
2009-01-01
on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time-dependent and multistage decision-making problems, e.g. optimal operation of combined wind-storage systems or multiple-market trading with different gate closures...
Emergency preparedness: community-based short-term eruption forecasting at Campi Flegrei
Selva, Jacopo; Marzocchi, Warner; Civetta, Lucia; Del Pezzo, Edoardo; Papale, Paolo
2010-05-01
A key element in emergency preparedness is to define advance tools to assist decision makers and emergency management groups during crises. Such tools must be prepared in advance, accounting for all of expertise and scientific knowledge accumulated through time. During a pre-eruptive phase, the key for sound short-term eruption forecasting is the analysis of the monitoring signals. This involves the capability (i) to recognize anomalous signals and to relate single or combined anomalies to physical processes, assigning them probability values, and (ii) to quickly provide an answer to the observed phenomena even when unexpected. Here we present a > 4 years long process devoted to define the pre-eruptive Event Tree (ET) for Campi Flegrei. A community of about 40 experts in volcanology and volcano monitoring participating to two Italian Projects on Campi Flegrei funded by the Italian Civil Protection, has been constituted and trained during periodic meetings on the statistical methods and the model BET_EF (Marzocchi et al., 2008) that forms the statistical package tool for ET definition. Model calibration has been carried out through public elicitation sessions, preceded and followed by devoted meetings and web forum discussion on the monitoring parameters, their accuracy and relevance, and their potential meanings. The calibrated ET allows anomalies in the monitored parameters to be recognized and interpreted, assigning probability values to each set of data. This process de-personalizes the difficult task of interpreting multi-parametric sets of data during on-going emergencies, and provides a view of the observed variations that accounts for the averaged, weighted opinion of the scientific community. An additional positive outcome of the described ET calibration process is that of providing a picture of the degree of confidence by the expert community on the capability of the many different monitored quantities of recognizing significant variations in the state of
Markov Chain Modelling for Short-Term NDVI Time Series Forecasting
Directory of Open Access Journals (Sweden)
Stepčenko Artūrs
2016-12-01
Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.
DEFF Research Database (Denmark)
Thorndahl, Søren Liedtke; Rasmussen, Michael R.
2013-01-01
Model based short-term forecasting of urban storm water runoff can be applied in realtime control of drainage systems in order to optimize system capacity during rain and minimize combined sewer overflows, improve wastewater treatment or activate alarms if local flooding is impending. A novel...... online system, which forecasts flows and water levels in real-time with inputs from extrapolated radar rainfall data, has been developed. The fully distributed urban drainage model includes auto-calibration using online in-sewer measurements which is seen to improve forecast skills significantly....... The radar rainfall extrapolation (nowcast) limits the lead time of the system to two hours. In this paper, the model set-up is tested on a small urban catchment for a period of 1.5 years. The 50 largest events are presented....
Energy Technology Data Exchange (ETDEWEB)
Faria, Sergio Nilo Gomes
1993-07-01
A proposal for a forecasting model of the electricity market which, in methodological terms, is based on classic econometric formulations - evaluation of income and price elasticities. The electricity demand for some industrial sectors is dealt with in a desegregated way, in order to capture its dependence on the economic activity of these sectors is presented. The proposal of this thesis differs from the usual methodology as far as evaluating the impacts of the energy demand forecast, conformed to well defined macroeconomics and tariff policy assumptions, on the expansion of the power system as a whole, and, particularly, on the financial situation of the power sector. The motivation for the study was the need for a new methodological tool, broad, but streamlined enough to allow widespread assessments of alternative development scenarios associated to different economic and politic contexts, taking into account the main uncertainties present in the several planning stages. (author)
Yao, Yibin; Shan, Lulu; Zhao, Qingzhi
2017-09-29
Global Navigation Satellite System (GNSS) can effectively retrieve precipitable water vapor (PWV) with high precision and high-temporal resolution. GNSS-derived PWV can be used to reflect water vapor variation in the process of strong convection weather. By studying the relationship between time-varying PWV and rainfall, it can be found that PWV contents increase sharply before raining. Therefore, a short-term rainfall forecasting method is proposed based on GNSS-derived PWV. Then the method is validated using hourly GNSS-PWV data from Zhejiang Continuously Operating Reference Station (CORS) network of the period 1 September 2014 to 31 August 2015 and its corresponding hourly rainfall information. The results show that the forecasted correct rate can reach about 80%, while the false alarm rate is about 66%. Compared with results of the previous studies, the correct rate is improved by about 7%, and the false alarm rate is comparable. The method is also applied to other three actual rainfall events of different regions, different durations, and different types. The results show that the method has good applicability and high accuracy, which can be used for rainfall forecasting, and in the future study, it can be assimilated with traditional weather forecasting techniques to improve the forecasted accuracy.
Forecast of electric power market to short-term: a time series approcah
International Nuclear Information System (INIS)
Costa, Roberio Neves Pelinca da.
1994-01-01
Three different time series approaches are analysed by this dissertation in the Brazilian electricity markert context. The aim is to compare the predictive performance of these approaches from a simulated exercise using the main series of the Brazilian consumption of electricity: Total Consumption, Industrial Consumption, Residencial Consumption and Commercial Consumption. One concludes that these appraches offer an enormous potentiality to the short-term planning system of the Electric Sector. Among the univariate models, the results for the analysed period point out that the forecast produced by Holt-Winter's models are more accurate than those produced by ARIMA and structural models. When explanatory variables are introduced in the last models, one can notice, in general, an improvement in the predictive performance of the models, although there is no sufficient evidence to consider that they are superior to Holt-Winter's models. The models with explanatory variables can be particularly useful, however, when one intends either to build scenarios or to study the effects of some variables on the consumption of electricity. (author). 73 refs., 19 figs., 13 tabs
Directory of Open Access Journals (Sweden)
Zhifeng Zhong
2017-01-01
Full Text Available Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.
Wind Turbine Waste Heat Recovery—A Short-Term Heat Loss Forecasting Approach
Directory of Open Access Journals (Sweden)
George Xydis
2015-07-01
Full Text Available The transition from the era of massive renewable energy deployment to the era of cheaper energy needed has made scientists and developers more careful with respect to energy planning compared with a few years ago. The focus is—and will be—placed on retrofitting and on extracting the maximum amount of locally generated energy. The question is not only how much energy can be generated, but also what kind of energy and how it can be utilized efficiently. The waste heat coming from wind farms (WFs when in operation—which until now was wasted—was thoroughly studied. A short-term forecasting methodology that can provide the operator with a better view of the expected heat losses is presented. The majority of mechanical (due to friction and electro-thermal (i.e., generator losses takes place at the nacelle while a smaller part of this thermal source is located near the foundation of the wind turbine (WT where the power electronics and the transformers are usually located. That thermal load can be easily collected via a working fluid and then be transported to the nearest local community or nearby agricultural or small scale industrial units using the necessary piping.
Energy Technology Data Exchange (ETDEWEB)
Ouvry, V
1998-01-30
The objective of this work is to shed light upon the future organization of the European gas market with an emphasis on price matters. There are nowadays few producers of gas on the market, most of whom hold long-term contracts with gas companies. Gas pricing is based on the net-back principle. The actual debate on liberalization of the gas market and the growing pressure from industrial customers to obtain lower prices addresses the problem of the future organisation of the market and the potential impact of the introduction of third party access. We first analyse the main actors of the gas market, their strategy and the actual market organization market. Two different logics are considered hereunder: a market approach: the competition theory provides efficient tools to analyse the evolution of competition depending on numerous factors. It appears that the strategy of all actors and particularly of producers will be the main determinant of the future competition. The oligopoly theory includes oligopolistic behaviours modelizations. The application of the Cournot`s model leads to prices ranging from 1,6 to 3,7 $/MBtu; a contractual approach: today, gas is essentially exchanged through long term contracts, which allow for long-term management of investments and supply security. Two operators negotiate the price, which ultimately mirrors their respective leverage. The transaction cost theory clearly shows the necessity of including transaction costs, especially when optimizing the duration of the contract. The gas prices escalation is nowadays partially obsolete and unadapted to customer needs. Escalation on coal, electricity price or inflation should soon be considered. The theories of negotiation highlight the importance of the operators` marketing power during gas price fixation Applying Nash and Harsanyi-Selten`s negotiation models results in a scale of 2,4 to 3,5 $/MBtu of the gas price at the actual supply and demand conditions. Both approaches lead to similar
Directory of Open Access Journals (Sweden)
Darong Huang
2018-01-01
Full Text Available Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme.
A hybrid approach for short-term forecasting of wind speed.
Tatinati, Sivanagaraja; Veluvolu, Kalyana C
2013-01-01
We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.
THE PROCESS OF SHORT-TERM AND LONG-TERM PRICE INTEGRATION IN THE BENIN MAIZE MARKET
LUTZ, C; VANTILBURG, A; VANDERKAMP, BJ
1995-01-01
This paper reviews the methodology used to study the price integration process in spatially separated spot markets, and applies if to the Benin maize market. An Autoregressive Distributed Lag Model is derived to take into account the sluggishness of price adjustments. Hypothesis testing concerns
International Nuclear Information System (INIS)
Wang, Bo; Tai, Neng-ling; Zhai, Hai-qing; Ye, Jian; Zhu, Jia-dong; Qi, Liang-bo
2008-01-01
In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (author)
Woldesellasse, H. T.; Marpu, P. R.; Ouarda, T.
2016-12-01
Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. A number of linear and nonlinear multivariate techniques has been used to predict the stochastic character of wind speed. A wind forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study Nonlinear Canonical Correlation Analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed and wind direction, of Abu Dhabi, UAE. The wind dataset was obtained from six ground stations. The first mode of NLCCA is capable of capturing the nonlinear mode of the climate indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE) and Mean absolute error (MAE). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behaviour of the dataset of variables than linear CCA model in terms of the correlation and root mean square error. Key words: Nonlinear Canonical Correlation Analysis (NLCCA), Canonical Correlation Analysis, Neural Network, Climate Indices, wind speed, wind direction
Cai, H.; Kessinger, C.; Rehak, N.; Pinto, J. O.; Megenhardt, D.; Albo, D.; Phillips, C.; Bankert, R.; Hawkins, J.
2012-12-01
Deep convection over the ocean poses a potentially great danger for trans-oceanic flights, as tragically demonstrated by the Air France Flight 447 accident of 2009. This paper describes a forecasting system that will produce 0-12 hr convective forecasts over the Gulf of Mexico domain using a blending technique that combines satellite-based extrapolation forecasts with Numerical Weather Prediction (NWP) model forecasts. Closely following the steps of the Federal Aviation Administration (FAA) Aviation Weather Research Program (AWRP) CoSPA development, a forecasting system is being developed to blend satellite-derived rain rate and cloud top height with their corresponding fields derived from the Global Forecasting System (GFS) NWP model. Forecasts will be computed over the 0-12 hr time frame within a domain that encompasses the greater Gulf of Mexico and parts of the continental United States. Tests of various extrapolation techniques have been completed and an optimum technique has been selected. Both the extrapolated and the GFS rain rate forecast performance statistics have been compiled. Considering the relative strength of the NWP model and the satellite-based extrapolation forecasts, a dynamical-weighting technique, similar to what is being used in CoSPA, has been tested. The weights are determined by past performance of extrapolation and model forecasts as a function of forecast lead time. A prototype blended forecasting system for oceanic convection using dynamical-weighting techniques has been developed and preliminary results of the blended forecasting system will be reported at the conference.
Short-term stream flow forecasting at Australian river sites using data-driven regression techniques
CSIR Research Space (South Africa)
Steyn, Melise
2017-09-01
Full Text Available This study proposes a computationally efficient solution to stream flow forecasting for river basins where historical time series data are available. Two data-driven modeling techniques are investigated, namely support vector regression...
Trading wind generation from short-term probabilistic forecasts of wind power
DEFF Research Database (Denmark)
Pinson, Pierre; Chevallier, Christophe; Kariniotakis, Georges
2007-01-01
Due to the fluctuating nature of the wind resource, a wind power producer participating in a liberalized electricity market is subject to penalties related to regulation costs. Accurate forecasts of wind generation are therefore paramount for reducing such penalties and thus maximizing revenue...... participation. Such strategies permit to further increase revenues and thus enhance competitiveness of wind generation compared to other forms of dispatchable generation. This paper formulates a general methodology for deriving optimal bidding strategies based on probabilistic forecasts of wind generation....... Despite the fact that increasing accuracy in spot forecasts may reduce penalties, this paper shows that, if such forecasts are accompanied with information on their uncertainty, i.e., in the form of predictive distributions, then this can be the basis for defining advanced strategies for market...
Developing long-term scenario forecasts to support electricity generation investment decisions
CSIR Research Space (South Africa)
Koen, Renée
2014-09-01
Full Text Available models to develop scenario forecasts for South African load profiles (hour-to-hour changes in the electricity demand), which can then be used to support decisions regarding the electricity generation capacity required. Although historical load profile...
Competitive Pricing by a Price Leader
Abhik Roy; Dominique M. Hanssens; Jagmohan S. Raju
1994-01-01
We examine the problem of pricing in a market where one brand acts as a price leader. We develop a procedure to estimate a leader's price rule, which is optimal given a sales target objective, and allows for the inclusion of demand forecasts. We illustrate our estimation procedure by calibrating this optimal price rule for both the leader and the follower using data on past sales and prices from the mid-size sedan segment of the U.S. automobile market. Our results suggest that a leader-follow...
Levenberg-Marquardt Recurrent Networks for Long-Term Electricity Peak Load Forecasting
Charles O.P. Marpaung; Weerakorn Ongsakul; Yusak Tanoto
2011-01-01
Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt learning algorithm is proposed to forecast annual peak load of Java-Madura-Bali interconnection for 2...
DEFF Research Database (Denmark)
Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
DEFF Research Database (Denmark)
Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
Comparison of short-term rainfall forecasts for modelbased flow prediction in urban drainage systems
DEFF Research Database (Denmark)
Thorndahl, Søren; Ahm, Malte; Nielsen, Jesper Ellerbek
2013-01-01
Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times....
Perraud, Jean-Michel; Bennett, James C.; Bridgart, Robert; Robertson, David E.
2016-04-01
Research undertaken through the Water Information Research and Development Alliance (WIRADA) has laid the foundations for continuous deterministic and ensemble short-term forecasting services. One output of this research is the software Short-term Water Information Forecasting Tools version 2 (SWIFT2). SWIFT2 is developed for use in research on short term streamflow forecasting techniques as well as operational forecasting services at the Australian Bureau of Meteorology. The variety of uses in research and operations requires a modular software system whose components can be arranged in applications that are fit for each particular purpose, without unnecessary software duplication. SWIFT2 modelling structures consist of sub-areas of hydrologic models, nodes and links with in-stream routing and reservoirs. While this modelling structure is customary, SWIFT2 is built from the ground up for computational and data intensive applications such as ensemble forecasts necessary for the estimation of the uncertainty in forecasts. Support for parallel computation on multiple processors or on a compute cluster is a primary use case. A convention is defined to store large multi-dimensional forecasting data and its metadata using the netCDF library. SWIFT2 is written in modern C++ with state of the art software engineering techniques and practices. A salient technical feature is a well-defined application programming interface (API) to facilitate access from different applications and technologies. SWIFT2 is already seamlessly accessible on Windows and Linux via packages in R, Python, Matlab and .NET languages such as C# and F#. Command line or graphical front-end applications are also feasible. This poster gives an overview of the technology stack, and illustrates the resulting features of SWIFT2 for users. Research and operational uses share the same common core C++ modelling shell for consistency, but augmented by different software modules suitable for each context. The
Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
Directory of Open Access Journals (Sweden)
Yuancheng Li
2016-11-01
Full Text Available The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays. Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.
Effects of stochastic energy prices on long-term energy-economic scenarios
International Nuclear Information System (INIS)
Krey, Volker; Martinsen, Dag; Wagner, Hermann-Josef
2007-01-01
In view of the currently observed energy prices, recent price scenarios, which have been very moderate until 2004, also tend to favor high future energy prices. Having a large impact on energy-economic scenarios, we incorporate uncertain energy prices into an energy systems model by including a stochastic risk function. Energy systems models are frequently used to aid scenario analysis in energy-related studies. The impact of uncertain energy prices on the supply structures and the interaction with measures in the demand sectors is the focus of the present paper. For the illustration of the methodological approach, scenarios for four EU countries are presented. Including the stochastic risk function, elements of high energy price scenarios can be found in scenarios with a moderate future development of energy prices. In contrast to scenarios with stochastic investment costs for a limited number of technologies, the inclusion of stochastic energy prices directly affects all parts of the energy system. Robust elements of hedging strategies include increasing utilization of domestic energy carriers, the use of CHP and district heat and the application of additional energy-saving measures in the end-use sectors. Region-specific technology portfolios, i.e., different hedging options, can cause growing energy exchange between the regions in comparison with the deterministic case
The process of short- and long-term price integration in the Benin maize market.
Lutz, C.; Tilburg, van A.; Kamp, van der B.J.
1995-01-01
This paper reviews the methodology used to study the price integration process in spatially separated spot markets, and applies it to the Benin maize market. An autoregressive distributed lag model is derived to take into account the sluggishness of price adjustments. Hypothesis testing concerns
The Effects of Price Stabilization on Short-Term Returns of IPOs
Directory of Open Access Journals (Sweden)
Douglas Beserra Pinheiro
2011-12-01
Full Text Available During the price stabilization in IPOs the underwriter repurchases part of the issue (ASC for aftermarket short covering. Such activity raises question about its real purpose: to keep price artificially high and deceive investors, or avoid price fluctuation resulting from the initial flow of information and the action of flippers. Our analysis indicates that in the post stabilization period stabilized IPOs underperform non-stabilized ones; the higher the intensity of the stabilization the lower are post-stabilization returns; IPOs for which the overallotment is fully covered in the ASC underperform non-stabilized IPOs in the post-stabilization period; the same does not happen when the ASC is only partial. Therefore, both views of the stabilization process are partially right: in some cases, stabilization is used to avoid price volatility and in other cases to keep price artificially high.
Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems
Directory of Open Access Journals (Sweden)
Luis Hernández
2014-03-01
Full Text Available The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.
An approximate method of short-term tsunami forecast and the hindcasting of some recent events
Directory of Open Access Journals (Sweden)
Yu. P. Korolev
2011-11-01
Full Text Available The paper presents a method for a short-term tsunami forecast based on sea level data from remote sites. This method is based on Green's function for the wave equation possessing the fundamental property of symmetry. This property is well known in acoustics and seismology as the reciprocity principle. Some applications of this principle on tsunami research are considered in the current study. Simple relationships and estimated transfer functions enabled us to simulate tsunami waveforms for any selected oceanic point based only on the source location and sea level data from a remote reference site. The important advantage of this method is that it is irrespective of the actual source mechanism (seismic, submarine landslide or other phenomena. The method was successfully applied to hindcast several recent tsunamis observed in the Northwest Pacific. The locations of the earthquake epicenters and the tsunami records from one of the NOAA DART sites were used as inputs for the modelling, while tsunami observations at other DART sites were used to verify the model. Tsunami waveforms for the 2006, 2007 and 2009 earthquake events near Simushir Island were simulated and found to be in good agreement with the observations. The correlation coefficients between the predicted and observed tsunami waveforms were from 0.50 to 0.85. Thus, the proposed method can be effectively used to simulate tsunami waveforms for the entire ocean and also for both regional and local tsunami warning services, assuming that they have access to the real-time sea level data from DART stations.
Directory of Open Access Journals (Sweden)
Wenlei Bai
2017-12-01
Full Text Available The deterministic methods generally used to solve DC optimal power flow (OPF do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.
Directory of Open Access Journals (Sweden)
Nantian Huang
2016-09-01
Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.
Modeling and forecasting petroleum futures volatility
International Nuclear Information System (INIS)
Sadorsky, Perry
2006-01-01
Forecasts of oil price volatility are important inputs into macroeconometric models, financial market risk assessment calculations like value at risk, and option pricing formulas for futures contracts. This paper uses several different univariate and multivariate statistical models to estimate forecasts of daily volatility in petroleum futures price returns. The out-of-sample forecasts are evaluated using forecast accuracy tests and market timing tests. The TGARCH model fits well for heating oil and natural gas volatility and the GARCH model fits well for crude oil and unleaded gasoline volatility. Simple moving average models seem to fit well in some cases provided the correct order is chosen. Despite the increased complexity, models like state space, vector autoregression and bivariate GARCH do not perform as well as the single equation GARCH model. Most models out perform a random walk and there is evidence of market timing. Parametric and non-parametric value at risk measures are calculated and compared. Non-parametric models outperform the parametric models in terms of number of exceedences in backtests. These results are useful for anyone needing forecasts of petroleum futures volatility. (author)
Valuing hydrological forecasts for a pumped storage assisted hydro facility
Zhao, Guangzhi; Davison, Matt
2009-07-01
SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.
A hybrid wavelet transform based short-term wind speed forecasting approach.
Wang, Jujie
2014-01-01
It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.
DEFF Research Database (Denmark)
Sperati, Simone; Alessandrini, Stefano; Pinson, Pierre
2015-01-01
A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting...... the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview...... and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field...
Directory of Open Access Journals (Sweden)
Simone Sperati
2015-09-01
Full Text Available A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE” with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.
Short-term pollution forecasts based on linear and nonlinear methods of time series analysis
Russo, A.; Trigo, R. M.
2012-04-01
Urban air pollution is a complex mixture of toxic components, which may induce acute and chronic responses from sensitive groups, such as children and people with previous heart and respiratory insufficiencies. However, air pollution, presents a highly chaotic and non-linear behavior. In this work we analyzed several pollutants time series recorded in the urban area of Lisbon (Portugal) for the 2002-2006 period. Linear and nonlinear methods were applied in order to assess NO2, PM10 and O3 main trends and fluctuations and finally, to produce daily forecasts of the referred pollutants. Here we evaluate the potential of linear and non-linear neural networks (NN) to produce short-term forecasts, and also the contribution of meteorological variables (daily mean temperature, radiation, wind speed and direction, boundary layer height, humidity) to pollutants dispersion. Additionally, we assess the role of large-scale circulation patterns, usually referred as Weather types (WT) (from the ERA40/ECMWF and ECMWF SLP database) towards the occurrence of critical pollution events identified previously. The presence and importance of trends and fluctuation is addressed by means of two modelling approaches: (1) raw data modelling; (2) residuals modelling (after the removal of the trends from the original data). The relative importance of two periodic components, the weekly and the monthly cycles, is addressed. For the three pollutants, the approach based on the removal of the weekly cycle presents the best results, comparatively to the removal of the monthly cycle or to the use of the raw data. The best predictors are chosen independently for each monitoring station and pollutant through an objective procedure (backward stepwise regression). The analysis reveals that the most significant variables in predicting NO2 concentration are several NO2 measures, wind direction and speed and global radiation, while for O3 correspond to several O3 measures, O3 precursors and WT
Short-term Probabilistic Forecasting of Wind Speed Using Stochastic Differential Equations
DEFF Research Database (Denmark)
Iversen, Jan Emil Banning; Morales González, Juan Miguel; Møller, Jan Kloppenborg
2016-01-01
It is widely accepted today that probabilistic forecasts of wind power production constitute valuable information for both wind power producers and power system operators to economically exploit this form of renewable energy, while mitigating the potential adverse effects related to its variable...... and uncertain nature. In this paper, we propose a modeling framework for wind speed that is based on stochastic differential equations. We show that stochastic differential equations allow us to naturally capture the time dependence structure of wind speed prediction errors (from 1 up to 24 hours ahead) and......, most importantly, to derive point and quantile forecasts, predictive distributions, and time-path trajectories (also referred to as scenarios or ensemble forecasts), all by one single stochastic differential equation model characterized by a few parameters....
Spatio‐temporal analysis and modeling of short‐term wind power forecast errors
DEFF Research Database (Denmark)
Tastu, Julija; Pinson, Pierre; Kotwa, Ewelina
2011-01-01
for the spatio‐temporal dependencies observed in the wind generation field. However, it is intuitively expected that, owing to the inertia of meteorological forecasting systems, a forecast error made at a given point in space and time will be related to forecast errors at other points in space in the following...... of small size like western Denmark, significant correlation between the various zones is observed for time delays up to 5 h. Wind direction is shown to play a crucial role, while the effect of wind speed is more complex. Nonlinear models permitting capture of the interdependence structure of wind power...... period. The existence of such underlying correlation patterns is demonstrated and analyzed in this paper, considering the case‐study of western Denmark. The effects of prevailing wind speed and direction on autocorrelation and cross‐correlation patterns are thoroughly described. For a flat terrain region...
The Impact of Atmospheric InfraRed Sounder (AIRS) Profiles on Short-term Weather Forecasts
Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William
2007-01-01
The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced spacebased atmospheric sounding systems. The combined AlRS/AMSU system provides radiance measurements used to retrieve temperature profiles with an accuracy of 1 K over 1 km layers under both clear and partly cloudy conditions, while the accuracy of the derived humidity profiles is 15% in 2 km layers. Critical to the successful use of AIRS profiles for weather and climate studies is the use of profile quality indicators and error estimates provided with each profile Aside form monitoring changes in Earth's climate, one of the objectives of AIRS is to provide sounding information of sufficient accuracy such that the assimilation of the new observations, especially in data sparse region, will lead to an improvement in weather forecasts. The purpose of this paper is to describe a procedure to optimally assimilate highresolution AIRS profile data in a regional analysis/forecast model. The paper will focus on the impact of AIRS profiles on a rapidly developing east coast storm and will also discuss preliminary results for a 30-day forecast period, simulating a quasi-operation environment. Temperature and moisture profiles were obtained from the prototype version 5.0 EOS science team retrieval algorithm which includes explicit error information for each profile. The error profile information was used to select the highest quality temperature and moisture data for every profile location and pressure level for assimilation into the ARPS Data Analysis System (ADAS). The AIRS-enhanced analyses were used as initial fields for the Weather Research and Forecast (WRF) system used by the SPORT project for regional weather forecast studies. The ADASWRF system will be run on CONUS domain with an emphasis on the east coast. The preliminary assessment of the impact of the AIRS profiles will focus on quality control issues associated with AIRS
Directory of Open Access Journals (Sweden)
Benjamin Planque
2003-04-01
Full Text Available Empirical evidence supports the hypothesis of a general relationship between sea temperature and recruitment of cod stocks across the North Atlantic, as well as between recruitment and the size of the spawning population. In the North Sea, cod year-class strength is inversely related to sea surface temperature during the first half of the year. This stock is currently at a low level, and the future trajectory of the stock biomass will be strongly influenced by recruitment levels. In the present study we investigate the possible use of observed and modelled sea surface temperature (SST to increase the accuracy and/or time horizon of recruitment forecasts for this stock. We show that the statistical model developed for forecasting spring temperature has good skill (35% skill, with a standard error of 0.36°C when predictions are made in late January. Within the frame of the current fish stock assessment working group we incorporate SST observations and January forecasts and simulate short-term recruitment projections. The resulting model accounts for a greater fraction of the variance in recruitment (42% than that obtained without temperature (17%. In operational mode, the model allows forecasting 1.5 years in advance but the accuracy of predicted recruitment remains low. This example indicates that we have not yet reached a point where environmental information can be used with great benefit for the management of North Sea cod. However, a similar strategy may yield greater benefits if developed for other stocks for which environmental effects are better understood and/or account for a larger fraction of the variability in recruitment, for species with a shorter generation time and species for which recruitment forecast is critical to management (e.g. anchovy, and in areas where environmental prediction capabilities may be greater either in accuracy or in lead time.
Mosier, T. M.; Hill, D. F.; Sharp, K. V.
2013-12-01
High spatial resolution time-series data are critical for many hydrological and earth science studies. Multiple groups have developed historical and forecast datasets of high-resolution monthly time-series for regions of the world such as the United States (e.g. PRISM for hindcast data and MACA for long-term forecasts); however, analogous datasets have not been available for most data scarce regions. The current work fills this data need by producing and freely distributing hindcast and forecast time-series datasets of monthly precipitation and mean temperature for all global land surfaces, gridded at a 30 arc-second resolution. The hindcast data are constructed through a Delta downscaling method, using as inputs 0.5 degree monthly time-series and 30 arc-second climatology global weather datasets developed by Willmott & Matsuura and WorldClim, respectively. The forecast data are formulated using a similar downscaling method, but with an additional step to remove bias from the climate variable's probability distribution over each region of interest. The downscaling package is designed to be compatible with a number of general circulation models (GCM) (e.g. with GCMs developed for the IPCC AR4 report and CMIP5), and is presently implemented using time-series data from the NCAR CESM1 model in conjunction with 30 arc-second future decadal climatologies distributed by the Consultative Group on International Agricultural Research. The resulting downscaled datasets are 30 arc-second time-series forecasts of monthly precipitation and mean temperature available for all global land areas. As an example of these data, historical and forecast 30 arc-second monthly time-series from 1950 through 2070 are created and analyzed for the region encompassing Pakistan. For this case study, forecast datasets corresponding to the future representative concentration pathways 45 and 85 scenarios developed by the IPCC are presented and compared. This exercise highlights a range of potential
An analog ensemble for short-term probabilistic solar power forecast
International Nuclear Information System (INIS)
Alessandrini, S.; Delle Monache, L.; Sperati, S.; Cervone, G.
2015-01-01
Highlights: • A novel method for solar power probabilistic forecasting is proposed. • The forecast accuracy does not depend on the nominal power. • The impact of climatology on forecast accuracy is evaluated. - Abstract: The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the solar elevation and the solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the solar radiation within the troposphere is the main meteorological factor influencing the solar power production, as a fraction of short wave solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on solar photovoltaic power prediction. This lack of predictability makes the solar energy integration into the grid challenging. A cost-effective utilization of solar energy over a grid strongly depends on the accuracy and reliability of the power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring solar power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic solar power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model
Forecasting Housing Approvals in Australia: Do Forecasters Herd?
DEFF Research Database (Denmark)
Stadtmann, Georg; Pierdzioch; Rülke
2012-01-01
Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find...
Revisiting the Long-Term Hedge Value of Wind Power in an Era of Low Natural Gas Prices
Energy Technology Data Exchange (ETDEWEB)
Bolinger, Mark [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division
2013-03-01
Expanding production of the United States’ vast shale gas reserves in recent years has put the country on a path towards greater energy independence, enhanced economic prosperity, and (potentially) reduced emissions of greenhouse gases and other pollutants. The corresponding expansion of gas-fired generation in the power sector – driven primarily by lower natural gas prices – has also made it easier and cheaper to integrate large amounts of variable renewable generation, such as wind power, into the grid. At the same time, however, low natural gas prices have suppressed wholesale power prices across the nation, making it harder for wind and other renewable power technologies to compete on cost alone – even despite their recent cost and performance improvements. A near-term softening in policy-driven demand from state-level renewable energy mandates, coupled with a possible phase-out of a key federal tax incentive over time, may exacerbate wind’s challenge in the coming years. As wind power finds it more difficult to compete with gas-fired generation on the basis of near-term cost, it will increasingly need to rely on other attributes, such as its “portfolio” or “hedge” value, as justification for inclusion in the power mix. This article investigates the degree to which wind power can still serve as a cost-effective hedge against rising natural gas prices, given the significant reduction in gas prices in recent years, coupled with expectations that prices will remain low for many years to come. It does so by drawing upon a rich sample of long-term power purchase agreements (“PPAs”) between existing wind generators and electric utilities in the U.S., and comparing the contracted prices at which utilities will be buying wind power from these existing projects for decades to come to a variety of long-term projections of the fuel costs of gas-fired generation modeled by the Energy Information Administration (“EIA”).
Long-term infrastructure forecasting in the Gulf of Mexico: a decision- and resource-based approach
International Nuclear Information System (INIS)
Kaiser, M.J.; Mesyanzhinov, D.V.; Pulsipher, A.G.
2004-01-01
A long-term infrastructure forecast in the Gulf of Mexico is developed in a disaggregated decision- and resource-based environment. Models for the installation and removal rates of structures are performed across five water depth categories for the Western and Central Gulf of Mexico planning areas for structures grouped according to a major and nonmajor classification. Master hydrocarbon production schedules are constructed per water depth and planning area using a two-parameter decision model, where 'bundled' resources are recoverable at a given time and at a specific rate. The infrastructure requirements to support the expected production is determined by extrapolating historical data. The analytic forecasting framework allows for subjective judgement, technological change, analogy, and historical trends to be employed in a user-defined manner. Special attention to the aggregation procedures employed and the general methodological framework are highlighted, including a candid discussion of the limitations of analysis and suggestions for further research
Directory of Open Access Journals (Sweden)
Javier Moriano
2016-01-01
Full Text Available In recent years, Secondary Substations (SSs are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected.
Moriano, Javier; Rodríguez, Francisco Javier; Martín, Pedro; Jiménez, Jose Antonio; Vuksanovic, Branislav
2016-01-12
In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected.
Enhanced Short-Term Wind Power Forecasting and Value to Grid Operations: Preprint
Energy Technology Data Exchange (ETDEWEB)
Orwig, K.; Clark, C.; Cline, J.; Benjamin, S.; Wilczak, J.; Marquis, M.; Finley, C.; Stern, A.; Freedman, J.
2012-09-01
The current state of the art of wind power forecasting in the 0- to 6-hour time frame has levels of uncertainty that are adding increased costs and risk on the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: 1) a 1-year field measurement campaign within two regions; 2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and 3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provides an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis.
Short-term forecasting of Czech quarterly GDP using monthly indicators
Czech Academy of Sciences Publication Activity Database
Arnoštová, K.; Havrlant, D.; Růžička, L.; Tóth, Peter
2011-01-01
Roč. 61, č. 6 (2011), s. 566-583 ISSN 0015-1920 Institutional research plan: CEZ:MSM0021620846 Keywords : GDP forecasting * bridge models * principal components Subject RIV: AH - Economics Impact factor: 0.346, year: 2011 http://journal.fsv.cuni.cz/storage/1235_toth.pdf
Short-term load and wind power forecasting using neural network-based prediction intervals.
Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas
2014-02-01
Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.
Levenberg-Marquardt Recurrent Networks for Long-Term Electricity Peak Load Forecasting
Directory of Open Access Journals (Sweden)
Charles O.P. Marpaung
2011-08-01
Full Text Available Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt learning algorithm is proposed to forecast annual peak load of Java-Madura-Bali interconnection for 2009-2011. Actual historical regional data which consists of economic, electricity statistic and weather during 1995-2008 are applied as inputs. The networks structure is firstly justified using true historical data of 1995-2005 to forecast peak load of 2006-2008. Afterwards, peak load forecasting of 2009-2011 is conducted subsequently using actual historical data of 1995-2008. Overall, the proposed networks shown better performance compared to that obtained by Levenberg-Marquardt-Feedforward network, Double-log Multiple Regression, and with projection by PLN for 2006-2010.
Long-term flow forecasts based on climate and hydrologic modeling: Uruguay River basin
Tucci, Carlos Eduardo Morelli; Clarke, Robin Thomas; Collischonn, Walter; da Silva Dias, Pedro Leite; de Oliveira, Gilvan Sampaio
2003-07-01
This paper describes a procedure for predicting seasonal flow in the Rio Uruguay drainage basin (area 75,000 km2, lying in Brazilian territory), using sequences of future daily rainfall given by the global climate model (GCM) of the Brazilian agency for climate prediction (Centro de Previsão de Tempo e Clima, or CPTEC). Sequences of future daily rainfall given by this model were used as input to a rainfall-runoff model appropriate for large drainage basins. Forecasts of flow in the Rio Uruguay were made for the period 1995-2001 of the full record, which began in 1940. Analysis showed that GCM forecasts underestimated rainfall over almost all the basin, particularly in winter, although interannual variability in regional rainfall was reproduced relatively well. A statistical procedure was used to correct for the underestimation of rainfall. When the corrected rainfall sequences were transformed to flow by the hydrologic model, forecasts of flow in the Rio Uruguay basin were better than forecasts based on historic mean or median flows by 37% for monthly flows and by 54% for 3-monthly flows.
Pool Strategy of a Price-Maker Wind Power Producer
DEFF Research Database (Denmark)
Zugno, Marco; Morales González, Juan Miguel; Pinson, Pierre
2013-01-01
We consider the problem of a wind power producer trading energy in short-term electricity markets. The producer is a price-taker in the day-ahead market, but a price-maker in the balancing market, and aims at optimizing its expected revenues from these market floors. The problem is formulated...... or median forecast of wind power distribution. Finally, sensitivity analyses are carried out to assess the impact on the offering strategy of the producer's penetration in the market, of the correlation between wind power production and residual system deviation, and of the shape of the forecast...
Li, Yu; Giuliani, Matteo; Castelletti, Andrea
2016-04-01
Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in
Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections
Energy Technology Data Exchange (ETDEWEB)
Feldman, David [National Renewable Energy Lab. (NREL), Golden, CO (United States); Barbose, Galen [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Margolis, Robert [National Renewable Energy Lab. (NREL), Golden, CO (United States); Wiser, Ryan [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Darghouth, Naim [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Goodrich, Alan [National Renewable Energy Lab. (NREL), Golden, CO (United States)
2012-11-30
The installed capacity of global and U.S. photovoltaic (PV) systems has soared in recent years, driven by declining PV prices and government incentives. The U.S. Department of Energy’s (DOE) SunShot Initiative aims to make PV cost competitive without incentives by reducing the cost of PV-generated electricity by about 75% between 2010 and 2020. This summary report—based on research at Lawrence Berkeley National Laboratory (LBNL) and the National Renewable Energy Laboratory (NREL)—examines progress in PV price reductions to help DOE and other PV stakeholders manage the transition to a market-driven PV industry, and to provide clarity surrounding the wide variety of potentially conflicting data available about PV system prices.
SUCIU Titus
2013-01-01
In individual companies, price is one significant factor in achieving marketing success. In many purchase situations, price can be of great importance to customers. Marketers must establish pricing strategies that are compatible with the rest of the marketing mix. Management should decide whether to charge the same price to all similar buyers of identical quantities of a product (a one-price strategy) or to set different prices (a flexible price strategy). Many organizations, especially retai...
Directory of Open Access Journals (Sweden)
Chao-Rong Chen
2017-02-01
Full Text Available This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI. The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV energy. This methodology is a combination of k-nearest neighbor (k-NN algorithm modelling and artificial neural network (ANN model. The k-NN-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN-ANN model the mean absolute bias error (MABE is 42 W/m2 and the root-mean-square error (RMSE is 242 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the k-NN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy.
International Nuclear Information System (INIS)
Tang, Pingzhou; Chen, Di; Hou, Yushuo
2016-01-01
As the world’s energy problem becomes more severe day by day, photovoltaic power generation has opened a new door for us with no doubt. It will provide an effective solution for this severe energy problem and meet human’s needs for energy if we can apply photovoltaic power generation in real life, Similar to wind power generation, photovoltaic power generation is uncertain. Therefore, the forecast of photovoltaic power generation is very crucial. In this paper, entropy method and extreme learning machine (ELM) method were combined to forecast a short-term photovoltaic power generation. First, entropy method is used to process initial data, train the network through the data after unification, and then forecast electricity generation. Finally, the data results obtained through the entropy method with ELM were compared with that generated through generalized regression neural network (GRNN) and radial basis function neural network (RBF) method. We found that entropy method combining with ELM method possesses higher accuracy and the calculation is faster.
International Nuclear Information System (INIS)
Santos, P.J.; Martins, A.G.; Pires, A.J.
2007-01-01
The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector. (author)
Park, Han-Earl; Yoon, Ha Su; Yoo, Sung-Moon; Cho, Jungho
2017-04-01
Over the past decade, Global Navigation Satellite System (GNSS) was in the spotlight as a meteorological research tool. The Korea Astronomy and Space Science Institute (KASI) developed a GNSS precipitable water vapor (PWV) information management system to apply PWV to practical applications, such as very short-term weather forecast. The system consists of a DPR, DRS, and TEV, which are divided functionally. The DPR processes GNSS data using the Bernese GNSS software and then retrieves PWV from zenith total delay (ZTD) with the optimized mean temperature equation for the Korean Peninsula. The DRS collects data from eighty permanent GNSS stations in the southern part of the Korean Peninsula and provides the PWV retrieved from GNSS data to a user. The TEV is in charge of redundancy of the DPR. The whole process is performed in near real-time where the delay is ten minutes. The validity of the GNSS PWV was proved by means of a comparison with radiosonde data. In the experiment of numerical weather prediction model, the GNSS PWV was utilized as the initial value of the Weather Research & Forecasting (WRF) model for heavy rainfall event. As a result, we found that the forecasting capability of the WRF is improved by data assimilation of GNSS PWV.
Energy Technology Data Exchange (ETDEWEB)
Santos, P.J. [LabSEI-ESTSetubal-Department of Electrical Engineering at Escola Superior de Tecnologia, Polytechnic Institute of Setubal Rua Vale de Chaves Estefanilha, 2910-761 Setubal (Portugal); Martins, A.G. [Department of Electrical Engineering, FCTUC/INESC, Polo 2 University of Coimbra, Pinhal de Marrocos, 3030 Coimbra (Portugal); Pires, A.J. [LabSEI-ESTSetubal-Department of Electrical Engineering at Escola Superior de Tecnologia, Polytechnic Institute of Setubal Rua Vale de, Chaves Estefanilha, 2910-761 Setubal (Portugal)
2007-05-15
The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector. (author)
Long-term price and environmental effects in a liberalised electricity market
International Nuclear Information System (INIS)
Lise, Wietze; Kruseman, Gideon
2008-01-01
This paper studies the effects of endogenous investment decisions in a liberalised electricity market on prices and the environment in the time horizon 2000-2050. Therefore, a computational, game-theoretic, recursive dynamic model is developed. Simulations with the model indicate that perfect competition leads to lower prices and benefits the environment in the form of lower acid and smog emissions. Continued exercise of market power leads to postponed investments and more diversity in the technology portfolio, while under perfect competition there is an earlier switch to gas-based technologies. (author)
Retrospective validation of renewal-based, medium-term earthquake forecasts
Rotondi, R.
2013-10-01
In this paper, some methods for scoring the performances of an earthquake forecasting probability model are applied retrospectively for different goals. The time-dependent occurrence probabilities of a renewal process are tested against earthquakes of Mw ≥ 5.3 recorded in Italy according to decades of the past century. An aim was to check the capability of the model to reproduce the data by which the model was calibrated. The scoring procedures used can be distinguished on the basis of the requirement (or absence) of a reference model and of probability thresholds. Overall, a rank-based score, information gain, gambling scores, indices used in binary predictions and their loss functions are considered. The definition of various probability thresholds as percentages of the hazard functions allows proposals of the values associated with the best forecasting performance as alarm level in procedures for seismic risk mitigation. Some improvements are then made to the input data concerning the completeness of the historical catalogue and the consistency of the composite seismogenic sources with the hypotheses of the probability model. Another purpose of this study was thus to obtain hints on what is the most influential factor and on the suitability of adopting the consequent changes of the data sets. This is achieved by repeating the estimation procedure of the occurrence probabilities and the retrospective validation of the forecasts obtained under the new assumptions. According to the rank-based score, the completeness appears to be the most influential factor, while there are no clear indications of the usefulness of the decomposition of some composite sources, although in some cases, it has led to improvements of the forecast.
Forecasting world and regional aviation jet fuel demands to the mid-term (2025)
International Nuclear Information System (INIS)
Cheze, Benoit; Gastineau, Pascal; Chevallier, Julien
2011-01-01
This article provides jet fuel demand projections at the worldwide level and for eight geographical zones until 2025. Air traffic forecasts are performed using dynamic panel-data econometrics. Then, the conversion of air traffic projections into quantities of jet fuel is accomplished by using a complementary approach to the 'Traffic Efficiency' method developed previously by the UK Department of Trade and Industry to support the Intergovernmental Panel on Climate Change (). According to our main scenario, air traffic should increase by about 100% between 2008 and 2025 at the world level, corresponding to a yearly average growth rate of 4.7%. World jet fuel demand is expected to increase by about 38% during the same period, corresponding to a yearly average growth rate of 1.9% per year. According to these results, energy efficiency improvements allow reducing the effect of air traffic rise on the increase in jet fuel demand, but do not annihilate it. Jet fuel demand is thus unlikely to diminish unless there is a radical technological shift, or air travel demand is restricted. - Highlights: → Jet fuel demand is forecasted at the worldwide and regional level until 2025. → Regional heterogeneity must be considered when forecasting jet fuel demand. → World air traffic should increase by about 100% between 2008 and 2025. → World jet fuel demand is expected to increase by about 38% during the same period. → Technological progress will not be enough to decrease the world jet fuel demand.
Stochastic Volatility and Option Pricing in Heath-Jarrow-Morton Term Structure Analysis
DEFF Research Database (Denmark)
Christensen, Bent Jesper; Konaris, George; Nicolato, Elisa
We consider a generalized Heath-Jarrow-Morton bond market model which allows both for jumps and stochastic volatility. Specifications with affine and quadratic volatility are studied and explicit option pricing formulas (in the Heston (1993) sense) are derived and implemented....
How much are you prepared to PAY for a forecast?
Arnal, Louise; Coughlan, Erin; Ramos, Maria-Helena; Pappenberger, Florian; Wetterhall, Fredrik; Bachofen, Carina; van Andel, Schalk Jan
2015-04-01
Probabilistic hydro-meteorological forecasts are a crucial element of the decision-making chain in the field of flood prevention. The operational use of probabilistic forecasts is increasingly promoted through the development of new novel state-of-the-art forecast methods and numerical skill is continuously increasing. However, the value of such forecasts for flood early-warning systems is a topic of diverging opinions. Indeed, the word value, when applied to flood forecasting, is multifaceted. It refers, not only to the raw cost of acquiring and maintaining a probabilistic forecasting system (in terms of human and financial resources, data volume and computational time), but also and most importantly perhaps, to the use of such products. This game aims at investigating this point. It is a willingness to pay game, embedded in a risk-based decision-making experiment. Based on a ``Red Cross/Red Crescent, Climate Centre'' game, it is a contribution to the international Hydrologic Ensemble Prediction Experiment (HEPEX). A limited number of probabilistic forecasts will be auctioned to the participants; the price of these forecasts being market driven. All participants (irrespective of having bought or not a forecast set) will then be taken through a decision-making process to issue warnings for extreme rainfall. This game will promote discussions around the topic of the value of forecasts for decision-making in the field of flood prevention.
Uranium price formation. Final report
International Nuclear Information System (INIS)
1977-10-01
The modern uranium industry came into existence in 1946. Until 1966, its sole customer was the Atomic Energy Commission, whose needs for U 3 O 8 relative to industry capacity declined over the years. The development of the commercial market after 1965 coincided with a period of excess capacity and falling nominal and real prices. Gradually in 1973 and dramatically thereafter, market conditions changed and prices rose as utilities sought larger quantities of U 3 O 8 and longer term contracts. Questions about availability of long-run supplies were raised, given the known reserve base. The response of the supply of U 3 O 8 to incentives offered first by the AEC and later by the utilities in the context of new and developing market conventions is examined. The methodology used is microeconomic analysis, qualitatively applied to the history of price formation in the market. Because the study emphasizes the implications of the history of uranium price formation for forecasting supply response, the study presents many different kinds of data and evaluates their quality and appropriateness for forecasting. A simple, very-useful framework for analyzing the history of the market for U 3 O 8 was developed and used to describe supply responses in selected important periods of the industry's development. It is concluded that the response of supply of U 3 O 8 to rising prices or to expectations of demand growth has been impressively strong. The potential reserve inventory is large enough to meet the needs for nuclear power generation through the end of this century. The price necessary to induce producers to find and produce these reserves is uncertain, partly because of problems inherent in estimating long-run supply curves and partly because recent inflation has created major uncertainties about the cost of future supplies
Tangborn, Wendell V.
1980-01-01
Snowmelt runoff is forecast with a statistical model that utilizes daily values of stream discharge, gaged precipitation, and maximum and minimum observations of air temperature. Synoptic observations of these variables are made at existing low- and medium-altitude weather stations, thus eliminating the difficulties and expense of new, high-altitude installations. Four model development steps are used to demonstrate the influence on prediction accuracy of basin storage, a preforecast test season, air temperature (to estimate ablation), and a prediction based on storage. Daily ablation is determined by a technique that employs both mean temperature and a radiative index. Radiation (both long- and short-wave components) is approximated by using the range in daily temperature, which is shown to be closely related to mean cloud cover. A technique based on the relationship between prediction error and prediction season weather utilizes short-term forecasts of precipitation and temperature to improve the final prediction. Verification of the model is accomplished by a split sampling technique for the 1960–1977 period. Short- term (5–15 days) predictions of runoff throughout the main snowmelt season are demonstrated for mountain drainages in western Washington, south-central Arizona, western Montana, and central California. The coefficient of prediction (Cp) based on actual, short-term predictions for 18 years is for Thunder Creek (Washington), 0.69; for South Fork Flathead River (Montana), 0.45; for the Black River (Arizona), 0.80; and for the Kings River (California), 0.80.
Stock price prediction using geometric Brownian motion
Farida Agustini, W.; Restu Affianti, Ika; Putri, Endah RM
2018-03-01
Geometric Brownian motion is a mathematical model for predicting the future price of stock. The phase that done before stock price prediction is determine stock expected price formulation and determine the confidence level of 95%. On stock price prediction using geometric Brownian Motion model, the algorithm starts from calculating the value of return, followed by estimating value of volatility and drift, obtain the stock price forecast, calculating the forecast MAPE, calculating the stock expected price and calculating the confidence level of 95%. Based on the research, the output analysis shows that geometric Brownian motion model is the prediction technique with high rate of accuracy. It is proven with forecast MAPE value ≤ 20%.
International Nuclear Information System (INIS)
Yu, Feng; Xu, Xiaozhong
2014-01-01
Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms
Energy Technology Data Exchange (ETDEWEB)
Zack, John [AWS Truewind, LLC, Albany, NY (United States); Natenberg, Eddie [AWS Truewind, LLC, Albany, NY (United States); Young, Steve [AWS Truewind, LLC, Albany, NY (United States); Knowe, Glenn Van [AWS Truewind, LLC, Albany, NY (United States); Waight, Ken [AWS Truewind, LLC, Albany, NY (United States); Manobianco, John [AWS Truewind, LLC, Albany, NY (United States); Kamath, Chandrika [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2010-10-01
To economically and reliably balance electrical load and generation, electrical grid operators, also called Balancing Authorities (BA), need highly accurate electrical power generation forecasts in time frames ranging from a few minutes to six hours ahead. As wind power generation increases, there is a requirement to improve the accuracy of 0- to 6-hour ahead wind power forecasts. Forecasts covering this short look-ahead period have depended heavily on short-term trends obtained from the actual power production and meteorological data of a wind generation facility. Additional data are often available from Numerical Weather Prediction (NWP) models and sometimes from off-site meteorological towers near wind generation facilities.
A new approach for crude oil price prediction based on stream learning
Directory of Open Access Journals (Sweden)
Shuang Gao
2017-01-01
Full Text Available Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments and individuals. Although many methods have been developed for predicting oil prices, it remains one of the most challenging forecasting problems due to the high volatility of oil prices. In this paper, we propose a novel approach for crude oil price prediction based on a new machine learning paradigm called stream learning. The main advantage of our stream learning approach is that the prediction model can capture the changing pattern of oil prices since the model is continuously updated whenever new oil price data are available, with very small constant overhead. To evaluate the forecasting ability of our stream learning model, we compare it with three other popular oil price prediction models. The experiment results show that our stream learning model achieves the highest accuracy in terms of both mean squared prediction error and directional accuracy ratio over a variety of forecast time horizons.
Study of the long-term values and prices of plutonium; a simplified parametrized model
International Nuclear Information System (INIS)
Gaussens, J.; Paillot, H.
1965-01-01
The authors define the notions of use values and price of plutonium. They give a 'simplified parametrized model' simulating the equilibrium of the offer and the demand in time, concerning the plutonium and the price deriving from the relative scarcity of this metal, taking into account the technical and economic operating parameters of the various reactors confronted. This model is simple enough to allow direct computations and establish clear relations between the various parameters. The use of the linear programmes method allows on the other hand a wide extension of the model. This report includes three main parts: I - General description of the study (without detailed calculations) II - Mathematical development of the simplified parametrized model and application (the basic data and the results of the calculations are given) III - Appendices (giving the detailed computations of part II). (authors) [fr
Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models
Directory of Open Access Journals (Sweden)
B. M. Brentan
2017-01-01
Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.
International Nuclear Information System (INIS)
Egging, Ruud
2013-01-01
Buildings are responsible for almost 40% of energy consumption and CO 2 emissions in the EU (EC, 2010). Improving the energy efficiency of buildings is a vital step towards achieving the EU climate and energy objectives. Directive 2010/31/EU outlines measures specifically focused on the energy performance of buildings. Incentives are created for building operators to optimize their energy sub-systems in a more robust, energy-efficient, and cost-effective manner. The challenge is to choose efficient energy-supply portfolios accounting for technological and market deregulation and risks. Decision support tools for energy management in public buildings using future scenarios of market and technological developments would be beneficial. The aim of this paper is to discuss the drivers and uncertainties in the recent and future energy market trends and prices, including technological progress and developments in fossil-fuel markets. This discussion is relevant for researchers and policymakers in general, and in particular, as an input for scenarios used in the development of decision support systems. -- highlights: •Decision support tools for building energy management should address uncertainty. •Differences in technological progress affect new technologies competitiveness. •Fuel price projections are unreliable, even more so for natural gas than other fossil fuels. •Efficiency gains and merit order shifts can lower future electricity prices significantly. •Bandwidths for future parameters should represent the large uncertainty ranges
International Nuclear Information System (INIS)
Galli, R.; Univ. della Svizzera Italiana, Lugano
1998-01-01
This paper analyzes long-term trends in energy intensity for ten Asian emerging countries to test for a non-monotonic relationship between energy intensity and income in the author's sample. Energy demand functions are estimated during 1973--1990 using a quadratic function of log income. The long-run coefficient on squared income is found to be negative and significant, indicating a change in trend of energy intensity. The estimates are then used to evaluate a medium-term forecast of energy demand in the Asian countries, using both a log-linear and a quadratic model. It is found that in medium to high income countries the quadratic model performs better than the log-linear, with an average error of 9% against 43% in 1995. For the region as a whole, the quadratic model appears more adequate with a forecast error of 16% against 28% in 1995. These results are consistent with a process of dematerialization, which occurs as a result of a reduction of resource use per unit of GDP once an economy passes some threshold level of GDP per capita
Directory of Open Access Journals (Sweden)
Jin-peng Liu
2017-07-01
Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.
International Nuclear Information System (INIS)
Groenewegen, G.G.
1992-01-01
On a conference (Gas for Europe in the 1990's) during the Gasexpo '91 the author held a speech of which the Dutch text is presented here. Attention is paid to the current European pricing methods (prices based on the costs of buying, transporting and distributing the natural gas and prices based on the market value, which is deducted from the prices of alternative fuels), and the transparency of the prices (lack of information on the way the prices are determined). Also attention is paid to the market signal transparency and gas-gas competition, which means a more or less free market of gas distribution. The risks of gas-to-gas competition for a long term price stability, investment policies and security of supply are discussed. Opposition against the Third Party Access (TPA), which is the program to implement gas-to-gas competition, is caused by the fear of natural gas companies for lower gas prices and lower profits. Finally attention is paid to government regulation and the activities of the European Commission (EC) in this matter. 1 fig., 6 ills., 1 tab
Computer aided planning of distribution systems and connection with medium term load forecast
International Nuclear Information System (INIS)
di Salvatore, F.; Grattieri, W.; Insinga, F.; Malafarina, L.; Mazzoni, M.; Nicola, G.
1990-01-01
In order to perform planning studies on HV (40-l50 kV), MV and LV networks, ENEL (Italian Electricity Board) has developed a computation system composed of a set of integrated programs which utilize the information stored in several data bases, with the aim of: providing energy consumption forecasts for each area of the country; transferring consumption for each area to the distribution network nodes and to evaluating the electric demand by using a statistical power/energy correlation model; analyzing several network development alternatives and selecting the optimum development plan by comparing the overall costs (investments, operation, risk). In order to make its utilization by planners easier, the computation system will be operated with interactive and graphic procedures made available by the use of graphic work stations. This report describes the main objectives and basic hypotheses assumed in the preparation of the computation system, as well as, the system's general architecture
LI, Y.; Castelletti, A.; Giuliani, M.
2014-12-01
Over recent years, long-term climate forecast from global circulation models (GCMs) has been demonstrated to show increasing skills over the climatology, thanks to the advances in the modelling of coupled ocean-atmosphere dynamics. Improved information from long-term forecast is supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping time) and for more effectively coping with the adverse impacts of climate variability. Yet, evaluating how valuable this information can be is not straightforward and farmers' response must be taken into consideration. Indeed, while long-range forecast are traditionally evaluated in terms of accuracy by comparison of hindcast and observed values, in the context of agricultural systems, potentially useful forecast information should alter the stakeholders' expectation, modify their decisions and ultimately have an impact on their annual benefit. Therefore, it is more desirable to assess the value of those long-term forecasts via decision-making models so as to extract direct indication of probable decision outcomes from farmers, i.e. from an end-to-end perspective. In this work, we evaluate the operational value of thirteen state-of-the-art long-range forecast ensembles against climatology forecast and subjective prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of farmers' behavior. Collected ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in order to address the mismatch of the spatio-temporal scale between forecast data from GCMs and distributed crop simulation model. The agronomic model is first simulated using the forecast information (ex-ante), followed by a second run with actual climate (ex-post). Multi-year simulations are performed to account for climate variability and the value of the different climate forecast is evaluated against the perfect
Linear and non-linear autoregressive models for short-term wind speed forecasting
International Nuclear Information System (INIS)
Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.
2016-01-01
Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.
Short term forecasting of explosions at Ubinas volcano, Perú
Traversa, P.; Lengliné, O.; Macedo, O.; Metaxian, J. P.; Grasso, J. R.; Inza, A.; Taipe, E.
2011-11-01
Most seismic eruption forerunners are described using Volcano-Tectonic earthquakes, seismic energy release, deformation rates or seismic noise analyses. Using the seismic data recorded at Ubinas volcano (Perú) between 2006 and 2008, we explore the time evolution of the Long Period (LP) seismicity rate prior to 143 explosions. We resolve an average acceleration of the LP rate above the background level during the 2-3 hours preceding the explosion onset. Such an average pattern, which emerges when stacking over LP time series, is robust and stable over all the 2006-2008 period, for which data is available. This accelerating pattern is also recovered when conditioning the LP rate on the occurrence of an other LP event, rather than on the explosion time. It supports a common mechanism for the generation of explosions and LP events, the magma conduit pressure increase being the most probable candidate. The average LP rate acceleration toward an explosion is highly significant prior to the higher energy explosions, supposedly the ones associated with the larger pressure increases. The dramatic decay of the LP activity following explosions, still reinforce the strong relationship between these two processes. We test and we quantify the retrospective forecasting power of these LP rate patterns to predict Ubinas explosions. The prediction quality of the forecasts (e.g. for 17% of alarm time, we predict 63% of Ubinas explosions, with 58% of false alarms) is evaluated using error diagrams. The prediction results are stable and the prediction algorithm validated, i.e. its performance is better than the random guess.
Energy Technology Data Exchange (ETDEWEB)
Arango, Hector Gustavo; Torres, Germano Lambert; Silva, Alexandre P. Alves da [Escola Federal de Engenharia de Itajuba, MG (Brazil)
1996-07-01
Future scenery definition is fundamental to planning activity on the whole, and specifically to electrical energy distribution system. The long term load forecast is classically accomplish in order to show how evolves in time the different variables of respective models. However, sceneries to decisions in electric distribution, needs information as regards of how the model electrical variables be spatially located. Thus, load spatial forecasting is an basic tool to distribution system planning. In this article, is proposed an expansion model of electrical market based in genetic algorithms. The construction of market evolution process is make by means of modified genetic algorithm, where the new loads localization is resolved by a fitness function, dependent of an relevant parameters set, such as distance, load value, load pattern, voltage, etc. The modified genetic algorithm refers to the fact every localization solution corresponds to a new load's point, so that during the process is generated multiples solutions based in selection and genetic manipulation to create the new population elements, or load's points. (author)
Peterson, D. A.; Hyer, E. J.; Wang, J.
2011-12-01
In order to meet the emerging need for better estimates of biomass burning emissions in air quality and climate models, a statistical model is developed to characterize the effect of a given set of meteorological conditions on the following day's fire activity, including ignition and spread potential. Preliminary tests are conducted within several spatial domains of the North American boreal forest by investigating a wide range of meteorological information, including operational fire weather forecasting indices, such as the Canadian Forest Fire Danger Rating System (CFFDRS). However, rather than using local noon surface station data, the six components of the CFFDRS are modified to use inputs from the North America Regional Reanalysis (NARR) and the Navy's Operational Global Atmospheric Prediction System Model (NOGAPS). The Initial Spread Index (ISI) and the Fire Weather Index (FWI) are shown to be the most relevant components of the CFFDRS for short-term changes in fire activity. However, both components are found to be highly sensitive to variations in relative humidity and wind speed input data. Several variables related to fire ignition from dry lighting, such as instability and the synoptic pattern, are also incorporated. Cases of fire ignition, growth, decay, and extinction are stratified using satellite fire observations from the Geostationary Operational Environmental Satellites (GOES) and the MODerate Resolution Imaging Spectroradiometer (MODIS) and compared to the available suite of meteorological information. These comparisons reveal that combinations of meteorological variables, such as the FWI, ISI, and additional indices developed for this study, produce the greatest separability between major fire growth and decay cases, which are defined by the observed change in fire counts and fire radiative power. This information is used to derive statistical relationships affecting the short-term changes in fire activity and subsequently applied to other
A quality assessment of the MARS crop yield forecasting system for the European Union
van der Velde, Marijn; Bareuth, Bettina
2015-04-01
Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.
DEFF Research Database (Denmark)
Runge, Julian; Wagner, Stefan; Claussen, Jörg
Firms commonly run field experiments to improve their freemium pricing schemes. However, they often lack a framework for analysis that goes beyond directly measurable outcomes and focuses on longer term profit. We aim to fill this gap by structuring existing knowledge on freemium pricing...... into a stylized framework. We apply the proposed framework in the analysis of a field experiment that contrasts three variations of a freemium pricing scheme and comprises about 300,000 users of a software application. Our findings indicate that a reduction of free product features increases conversion as well...... as viral activity, but reduces usage – which is in line with the framework’s predictions. Additional back-of-the-envelope profit estimations suggest that managers were overly optimistic about positive externalities from usage and viral activity in their choice of pricing scheme, leading them to give too...
Short-Term Forecasting of Inertial Response from a Wind Power Plant: Preprint
Energy Technology Data Exchange (ETDEWEB)
Muljadi, Eduard; Gevorgian, Vahan; Hoke, Andy
2016-09-01
The total inertia stored in all rotating masses (synchronous generators, induction motors, etc.) connected to a power system grid is an essential force that keeps the system stable after disturbances. Power systems have been experiencing reduced inertia during the past few decades [1]. This trend will continue as the level of renewable generation (e.g., wind and solar) increases. Wind power plants (WPPs) and other renewable power plants with power electronic interfaces are capable of delivering frequency response (both droop and/or inertial response) by a control action; thus, the reduction in available online inertia can be compensated by designing the plant control to include frequency response. The source of energy to be delivered as inertial response is determined by the type of generation (wind, photovoltaic, concentrating solar power, etc.) and the control strategy chosen. The importance of providing ancillary services to ensure frequency control within a power system is evidenced from many recent publications with different perspectives (manufacturer, system operator, regulator, etc.) [2]-[6]. This paper is intended to provide operators with a method for the real-time assessment of the available inertia of a WPP. This is critical to managing power system stability and the reserve margin. In many states, modern WPPs are required to provide ancillary services (e.g., frequency regulation via governor response and inertial response) to the grid. This paper describes the method of estimating the available inertia and the profile of the forecasted response from a WPP.
Trend analysis and short-term forecast of incident HIV infection in Ghana.
Aboagye-Sarfo, Patrick; Cross, James; Mueller, Ute
2010-06-01
The study uses time-series modelling to determine and predict trends in incident HIV infection in Ghana among specific age groups. The HIV data for Ghana were grouped according to northern and southern spatial sectors as they exhibited slightly different data collection formats. The trend of the epidemic is modelled using moving-average smoothing techniques, and the Box-Jenkins ARIMA model is used to forecast cases of newly acquired (incident) HIV infection. Trend analysis of past growth patterns reveals an increase in new cases of HIV infection in the northern sector, with the greatest increase occurring among persons aged 30 years and over. The epidemic in the southern sector appears to have levelled off. However, incident HIV infection in the 20-39-year-old age group of females in the sector is estimated to increase in the next three years. Moreover, the estimates suggest a higher increase in incident cases than that predicted by the National AIDS Control Programme. Nevertheless, incident HIV infection among persons aged 19 and below is found to be relatively stable. Thus, if efforts are made to reduce or prevent an increase in the number of new infections in the northern sector, and for the 20-39 years age group in the southern sector, Ghana will have a brighter future with regard to its response to the HIV epidemic. These findings can assist with developing strategic-intervention policy planning for Ghana and other countries in sub-Saharan Africa.
Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond
DEFF Research Database (Denmark)
Hong, Tao; Pinson, Pierre; Fan, Shu
2016-01-01
The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged fundamenta...... competition with four tracks on load, price, wind and solar forecasting, which attracted 581 participants from 61 countries. We conclude the paper with 12 predictions for the next decade of energy forecasting.......The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged...... fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the energy forecasting literature focus on point or singlevalued forecasts, the research interest in probabilistic energy...
Energy Technology Data Exchange (ETDEWEB)
Metaxiotis, K.; Kagiannas, A.; Askounis, D.; Psarras, J. [National Technical University of Athens, Zografou (Turkey). Dept. of Electrical and Computer Engineering
2003-06-01
Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. AI-based systems are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. This paper provides an overview for the researcher of AI technologies, as well as their current use in the field of short term electric load forecasting (STELF). The history of AI in STELF is outlined, leading to a discussion of the various approaches as well as the current research directions. The paper concludes by sharing thoughts and estimations on AI future prospects in this area. This review reveals that although still regarded as a novel methodology, AI technologies are shown to have matured to the point of offering real practical benefits in many of their applications. (Author)