Natural gas, NGL's and crude: supply, demand and price forecasts

International Nuclear Information System (INIS)

Stauft, T.L.

2003-01-01

This paper presents an overview of the major issues to watch in the crude oil, natural gas, and natural gas liquids (NGL) markets in North America. The presentation began with background information concerning Purvin and Gertz, an employee-owned consulting firm whose employees are chemical engineers, holders of a Master of Business Administration (MBA), or economists. They specialize in providing strategic, commercial, and technical advice to the international energy industry. A closer look at each individual market was provided, looking at demand, supply, price drivers and others. The author concluded that world oil prices continue to be influenced by a war premium. Oil prices support natural gas, as well as the possibility of a supply issue. The gas processing margins have remained strong. The unknown quantities are the weather and economic recovery. figs

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

The relation of monthly spot to futures prices for natural gas

International Nuclear Information System (INIS)

Herbert, J.H.

1993-01-01

The relationship between the spot price for natural gas for a delivery month and the futures contract price for the same delivery month is examined. The estimated regression equation provides a good summary of the relationship between spot and futures prices for the time period and can also be used to obtain accurate forecasts of spot prices. It appears that the natural gas futures market is inefficient. (author)

Effect of the accuracy of price forecasting on profit in a Price Based Unit Commitment

International Nuclear Information System (INIS)

Delarue, Erik; Van Den Bosch, Pieterjan; D'haeseleer, William

2010-01-01

This paper discusses and quantifies the so-called loss of profit (i.e., the sub-optimality of profit) that can be expected in a Price Based Unit Commitment (PBUC), when incorrect price forecasts are used. For this purpose, a PBUC model has been developed and utilized, using Mixed Integer Linear Programming (MILP). Simulations are used to determine the relationship between the Mean Absolute Percentage Error (MAPE) of a certain price forecast and the loss of profit, for four different types of power plants. A Combined Cycle (CC) power plant and a pumped storage unit show highest sensitivity to incorrect forecasts. A price forecast with a MAPE of 15%, on average, yields 13.8% and 12.1% profit loss, respectively. A classic thermal power plant (coal fired) and cascade hydro unit are less affected by incorrect forecasts, with only 2.4% and 2.0% profit loss, respectively, at the same price forecast MAPE. This paper further demonstrates that if price forecasts show an average bias (upward or downward), using the MAPE as measure of the price forecast might not be sufficient to quantify profit loss properly. Profit loss in this case has been determined as a function of both shift and MAPE of the price forecast. (author)

Which way the natural gas price. An attempt to predict the direction of natural gas spot price movements using trader positions

International Nuclear Information System (INIS)

Buchanan, W.K.; Hodges, P.; Theis, J.

2001-01-01

This research provides a method of predicting direction of spot price movements in the natural gas market for the month succeeding from market participants positions in the futures market. Cumby and Modest (Cumby, R.E., Modest, D.M., 1987. Testing for market timing ability: a framework for forecast evaluation. Journal of Financial Economics 19, 169-189) provide the backdrop for analyzing the futures market positions of large hedgers and speculators to arrive at conclusions of market price movements in the spot market. This methodology is suggested as a means for municipalities entering the natural gas market to improve upon their ordering of quantities of gas for the ensuing months in order to take advantage of possibly foreseeable price trends

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

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......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...... 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...

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.

Price forecasting of day-ahead electricity markets using a hybrid forecast method

Energy Technology Data Exchange (ETDEWEB)

Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

2011-05-15

Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} 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.

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.

Gas prices and price process

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

Electricity Price Forecasting Based on AOSVR and Outlier Detection

Institute of Scientific and Technical Information of China (English)

Zhou Dianmin; Gao Lin; Gao Feng

2005-01-01

Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.

Gas analysis modeling system forecast for the Energy Modeling Forum North American Natural Gas Market Study

International Nuclear Information System (INIS)

Mariner-Volpe, B.; Trapmann, W.

1989-01-01

The Gas Analysis Modeling System is a large computer-based model for analyzing the complex US natural gas industry, including production, transportation, and consumption activities. The model was developed and first used in 1982 after the passage of the NGPA, which initiated a phased decontrol of most natural gas prices at the wellhead. The categorization of gas under the NGPA and the contractual nature of the natural gas market, which existed at the time, were primary factors in the development of the basic structure of the model. As laws and regulations concerning the natural gas market have changed, the model has evolved accordingly. Recent increases in competition in the wellhead market have also led to changes in the model. GAMS produces forecasts of natural gas production, consumption, and prices annually through 2010. It is an engineering-economic model that incorporates several different mathematical structures in order to represent the interaction of the key groups involved in the natural gas market. GAMS has separate supply and demand components that are equilibrated for each year of the forecast by means of a detailed transaction network

Forecasting prices and price volatility in the Nordic electricity market

International Nuclear Information System (INIS)

2001-01-01

We develop a stochastic model for long term price forecasting in a competitive electricity market environment. It is demonstrated both theoretically and through model simulations that non-stochastic models may give biased forecasts both with respect to price level and volatility. In the paper, the model concept is applied on the restructured Nordic electricity market. It is specially in peak load hours that a stochastic model formulation provides significantly different results than an expected value model. (author)

Boosting Learning Algorithm for Stock Price Forecasting

Science.gov (United States)

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.

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)

Apples with apples: accounting for fuel price risk in comparisons of gas-fired and renewable generation

Energy Technology Data Exchange (ETDEWEB)

Bolinger, Mark; Wiser, Ryan

2003-12-18

For better or worse, natural gas has become the fuel of choice for new power plants being built across the United States. According to the US Energy Information Administration (EIA), natural gas combined-cycle and combustion turbine power plants accounted for 96% of the total generating capacity added in the US between 1999 and 2002--138 GW out of a total of 144 GW. Looking ahead, the EIA expects that gas-fired technology will account for 61% of the 355 GW new generating capacity projected to come on-line in the US up to 2025, increasing the nationwide market share of gas-fired generation from 18% in 2002 to 22% in 2025. While the data are specific to the US, natural gas-fired generation is making similar advances in other countries as well. Regardless of the explanation for (or interpretation of) the empirical findings, however, the basic implications remain the same: one should not blindly rely on gas price forecasts when comparing fixed-price renewable with variable-price gas-fired generation contracts. If there is a cost to hedging, gas price forecasts do not capture and account for it. Alternatively, if the forecasts are at risk of being biased or out of tune with the market, then one certainly would not want to use them as the basis for resource comparisons or investment decisions if a more certain source of data (forwards) existed. Accordingly, assuming that long-term price stability is valued, the most appropriate way to compare the levelized cost of these resources in both cases would be to use forward natural gas price data--i.e. prices that can be locked in to create price certainty--as opposed to uncertain natural gas price forecasts. This article suggests that had utilities and analysts in the US done so over the sample period from November 2000 to November 2003, they would have found gas-fired generation to be at least 0.3-0.6 cents/kWh more expensive (on a levelized cost basis) than otherwise thought. With some renewable resources, in particular wind

Formation of an Integrated Stock Price Forecast Model in Lithuania

Directory of Open Access Journals (Sweden)

Audrius Dzikevičius

2016-12-01

Full Text Available Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.

Forecasting of electricity prices with neural networks

Energy Technology Data Exchange (ETDEWEB)

Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

2006-08-15

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 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 natural gas supply in China: Production peak and import trends

International Nuclear Information System (INIS)

Lin Boqiang; Wang Ting

2012-01-01

China's natural gas consumption has increased rapidly in recent years making China a net gas importer. As a nonrenewable energy, the gas resource is exhaustible. Based on the forecast of this article, China's gas production peak is likely to approach in 2022. However, China is currently in the industrialization and urbanization stage, and its natural gas consumption will persistently increase. With China's gas production peak, China will have to face a massive expansion in gas imports. As the largest developing country, China's massive imports of gas will have an effect on the international gas market. In addition, as China's natural gas price is still controlled by the government and has remained at a low level, the massive imports of higher priced gas will exert great pressure on China's gas price reform. - Highlights: ► We figured out the natural gas production peak of China. ► We predict the import trends of natural gas of China. ► We study the international and national impacts of China's increasing import of gas. ► It is important for China to accelerate price reformation of natural gas.

The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia

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.

North American natural gas liquids pricing and convergence : an energy market assessment

International Nuclear Information System (INIS)

2001-05-01

A background on natural gas liquids (NGL) pricing was presented along with a discussion regarding the impact of energy price convergence. The high energy prices in the fall of 2000 were a result of many factors, including the high price of NGLs. All NGL components such as ethane, propane and butane can be used as petrochemical feedstock. In the winter of 2000/2001 the relationship between liquids and crude oil prices collapsed when high energy prices led to a situation where, for a short while, extraction of liquids from natural gas became uneconomic since producers got more value for NGLs left in the gas stream. As a result, when the supply and demand balances for NGL tightened in many regions of North America, NGL prices were reflecting the unprecedented high natural gas prices. This paper also explained how the four major North American NGL trading hubs in Alberta, Ontario, Kansas and Texas operate. The pricing events of 2000 have impacted on the NGL industry and energy prices remain an issue since both crude oil and natural gas price are forecasted to remain strong in the near future. 5 figs

Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach

Science.gov (United States)

Hamid, Mohd Fahmi Abdul; Shabri, Ani

2017-05-01

Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.

The natural gas. Forecast for 2010-2020 (availability, constraints and dependences)

International Nuclear Information System (INIS)

Terzian, P.

1998-01-01

In this book the author deals with the supply and the demand forecast of gas for 2020 and evaluate the risks of a possible european dependence towards the importations outside of Europe OECD. The following subjects are also considered: the gas and petroleum prices evolution, the supplying reliability, the european regulation and the specific question of the natural gas liquids. (A.L.B.)

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

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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.

Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors

Directory of Open Access Journals (Sweden)

Claudio Monteiro

2018-04-01

Full Text Available This article presents original probabilistic price forecasting meta-models (PPFMCP models, by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF of a Beta distribution for the output variable (hourly price can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI and a Loss function Indicator (LI are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

A hybrid approach for probabilistic forecasting of electricity price

DEFF Research Database (Denmark)

Wan, Can; Xu, Zhao; Wang, Yelei

2014-01-01

to the nonstationarities involved in market clearing prices (MCPs), it is rather difficult to accurately predict MCPs in advance. The challenge is getting intensified as more and more renewable energy and other new technologies emerged in smart grids. Therefore transformation from traditional point forecasts...... 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...

Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning

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

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)

Natural gas pricing

International Nuclear Information System (INIS)

Freedenthal, C.

1993-01-01

Natural gas pricing is the heart and soul of the gas business. Price specifically affects every phase of the industry. Too low a price will result in short supplies as seen in the mid-1970s when natural gas was scarce and in tight supply. To fully understand the pricing of this energy commodity, it is important to understand the total energy picture. In addition, the effect and impact of world and US economies, and economics in general are crucial to understanding natural gas pricing. The purpose of this presentation will be to show the parameters going into US natural gas pricing including the influence of the many outside industry factors like crude oil and coal pricing, market drivers pushing the gas industry, supply/demand parameters, risk management for buyers and sellers, and other elements involved in pricing analysis

Improving the Forecasting Accuracy of Crude Oil Prices

Directory of Open Access Journals (Sweden)

Xuluo Yin

2018-02-01

Full Text Available Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper’s method is robust and performs well compared to random walk.

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

Natural gas pricing and contracting practices in North America

International Nuclear Information System (INIS)

Hassan, F.

1992-01-01

Over the past 5 years the natural gas industry in North America has undergone substantial change as a result of the deregulated market. A comparison is provided of the key contract parameters in gas purchase contracts utilized by local distribution companies, industrial customers, cogenerators and marketers. Issues discussed include pricing mechanisms, indexed contracts, negotiated contracts, combinations, dispute resolution, supply, government regulation, industry structures, financial considerations, perception, geological influences, demand, transmission, storage, distribution, price trends and forecasts, Order 636 in the U.S., the evolution of North American market hubs, the futures market, and 'daisy chains' of connecting pipelines. 15 refs., 7 figs., 1 tab

Forecasting Natural Rubber Price In Malaysia Using Arima

Science.gov (United States)

Zahari, Fatin Z.; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Saifullah Rusiman, Mohd; Ali, Maselan

2018-04-01

This paper contains introduction, materials and methods, results and discussions, conclusions and references. Based on the title mentioned, high volatility of the price of natural rubber nowadays will give the significant risk to the producers, traders, consumers, and others parties involved in the production of natural rubber. To help them in making decisions, forecasting is needed to predict the price of natural rubber. The main objective of the research is to forecast the upcoming price of natural rubber by using the reliable statistical method. The data are gathered from Malaysia Rubber Board which the data are from January 2000 until December 2015. In this research, average monthly price of Standard Malaysia Rubber 20 (SMR20) will be forecast by using Box-Jenkins approach. Time series plot is used to determine the pattern of the data. The data have trend pattern which indicates the data is non-stationary data and the data need to be transformed. By using the Box-Jenkins method, the best fit model for the time series data is ARIMA (1, 1, 0) which this model satisfy all the criteria needed. Hence, ARIMA (1, 1, 0) is the best fitted model and the model will be used to forecast the average monthly price of Standard Malaysia Rubber 20 (SMR20) for twelve months ahead.

Short-term electricity prices forecasting in a competitive market: A neural network approach

International Nuclear Information System (INIS)

Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.

2007-01-01

This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)

Design and implementation of ticket price forecasting system

Science.gov (United States)

Li, Yuling; Li, Zhichao

2018-05-01

With the advent of the aviation travel industry, a large number of data mining technologies have been developed to increase profits for airlines in the past two decades. The implementation of the digital optimization strategy leads to price discrimination, for example, similar seats on the same flight are purchased at different prices, depending on the time of purchase, the supplier, and so on. Price fluctuations make the prediction of ticket prices have application value. In this paper, a combination of ARMA algorithm and random forest algorithm is proposed to predict the price of air ticket. The experimental results show that the model is more reliable by comparing the forecasting results with the actual results of each price model. The model is helpful for passengers to buy tickets and to save money. Based on the proposed model, using Python language and SQL Server database, we design and implement the ticket price forecasting system.

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)

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

Price dynamics of natural gas and the regional methanol markets

International Nuclear Information System (INIS)

Masih, A. Mansur M.; Albinali, Khaled; DeMello, Lurion

2010-01-01

A 'methanol economy' based mainly on natural gas as a feedstock has a lot of potential to cope with the current and ongoing concerns for energy security along with the reduction of CO-2 emissions. It is, therefore, important to examine the price dynamics of methanol in order to ascertain whether the price of methanol is mainly natural-gas-cost driven or demand driven in the context of different regions. This paper is the first attempt to investigate the following: (1) is the natural gas price significantly related to the regional methanol prices in the Far East, United States and Europe? (2) who drives the regional methanol prices? The paper is motivated by the recent and growing debate on the lead-lag relationship between natural gas and methanol prices. Our findings, based on the most recently developed 'long-run structural modelling' and subject to the limitations of the study, tend to suggest: (1) natural gas price is cointegrated with the regional methanol prices, (2) our within-sample error-correction model results tend to indicate that natural gas was driving the methanol prices in Europe and the United States but not in the Far East. These results are consistent, during most of the period under review (1998.5-2007.3), with the surge in demand for methanol throughout the Far East, particularly in China, Taiwan and South Korea, which appears to have played a relatively more dominant role in the Far East compared to that in Europe and the United States within the framework of the dynamic interactions of input and product prices. However, during the post-sample forecast period as evidenced in our variance decompositions analysis, the emergence of natural gas as the main driver of methanol prices in all three continents is consistent with the recent surge in natural gas price fueled mainly, among others, by the strong hedging activities in the natural gas futures/options as well as refining tightness (similar to those that were happening in the crude oil markets

Hourly weather forecasts for gas turbine power generation

Directory of Open Access Journals (Sweden)

G. Giunta

2017-06-01

Full Text Available An hourly short-term weather forecast can optimize processes in Combined Cycle Gas Turbine (CCGT plants by helping to reduce imbalance charges on the national power grid. Consequently, a reliable meteorological prediction for a given power plant is crucial for obtaining competitive prices for the electric market, better planning and stock management, sales and supplies of energy sources. The paper discusses the short-term hourly temperature forecasts, at lead time day+1 and day+2, over a period of thirteen months in 2012 and 2013 for six Italian CCGT power plants of 390 MW each (260 MW from the gas turbine and 130 MW from the steam turbine. These CCGT plants are placed in three different Italian climate areas: the Po Valley, the Adriatic coast, and the North Tyrrhenian coast. The meteorological model applied in this study is the eni-Kassandra Meteo Forecast (e‑kmf™, a multi-model approach system to provide probabilistic forecasts with a Kalman filter used to improve accuracy of local temperature predictions. Performance skill scores, computed by the output data of the meteorological model, are compared with local observations, and used to evaluate forecast reliability. In the study, the approach has shown good overall scores encompassing more than 50,000 hourly temperature values. Some differences from one site to another, due to local meteorological phenomena, can affect the short-term forecast performance, with consequent impacts on gas-to-power production and related negative imbalances. For operational application of the methodology in CCGT power plant, the benefits and limits have been successfully identified.

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)

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...

Forecasting electricity spot market prices with a k-factor GIGARCH process

International Nuclear Information System (INIS)

Diongue, Abdou Ka; Guegan, Dominique; Vignal, Bertrand

2009-01-01

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)

Forecasting oil price trends using wavelets and hidden Markov models

International Nuclear Information System (INIS)

Souza e Silva, Edmundo G. de; Souza e Silva, Edmundo A. de; Legey, Luiz F.L.

2010-01-01

The crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we investigate the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements. Using an HMM, we develop a forecasting methodology that consists of, basically, three steps. First, we employ wavelet analysis to remove high frequency price movements, which can be assumed as noise. Then, the HMM is used to forecast the probability distribution of the price return accumulated over the next F days. Finally, from this distribution, we infer future price trends. Our results indicate that the proposed methodology might be a useful decision support tool for agents participating in the crude oil market. (author)

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)

Housing price forecastability: A factor analysis

DEFF Research Database (Denmark)

Møller, Stig Vinther; Bork, Lasse

2017-01-01

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...... movements in housing prices. We find that (S)PLS models systematically dominate PCA models. (S)PLS models also generate significant out-of-sample predictive power over and above the predictive power contained by the price-rent ratio, autoregressive benchmarks, and regression models based on small datasets....

Stock price forecasting based on time series analysis

Science.gov (United States)

Chi, Wan Le

2018-05-01

Using the historical stock price data to set up a sequence model to explain the intrinsic relationship of data, the future stock price can forecasted. The used models are auto-regressive model, moving-average model and autoregressive-movingaverage model. The original data sequence of unit root test was used to judge whether the original data sequence was stationary. The non-stationary original sequence as a first order difference needed further processing. Then the stability of the sequence difference was re-inspected. If it is still non-stationary, the second order differential processing of the sequence is carried out. Autocorrelation diagram and partial correlation diagram were used to evaluate the parameters of the identified ARMA model, including coefficients of the model and model order. Finally, the model was used to forecast the fitting of the shanghai composite index daily closing price with precision. Results showed that the non-stationary original data series was stationary after the second order difference. The forecast value of shanghai composite index daily closing price was closer to actual value, indicating that the ARMA model in the paper was a certain accuracy.

Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

Energy Technology Data Exchange (ETDEWEB)

Mandal, Paras; Senjyu, Tomonobu [Department of Electrical and Electronics, University of the Ryukyus, 1 Senbaru, Nagakami Nishihara, Okinawa 903-0213 (Japan); Funabashi, Toshihisa [Meidensha Corporation, Tokyo 103-8515 (Japan)

2006-09-15

In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy. (author)

Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

International Nuclear Information System (INIS)

Mandal, Paras; Senjyu, Tomonobu; Funabashi, Toshihisa

2006-01-01

In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6 h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy

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)

Why rising U.S. gas demand may not hike prices in the 90s

International Nuclear Information System (INIS)

Adelman, M.A.

1992-01-01

This paper reports that it was widely believed after the 1986 U.S. natural gas price drop that prices had to rise steeply soon because at the low prices it did not pay to replace reserves. Lack of reserves would push the price back up. This forecast raised the value of reserves in the ground. It was a mistake. Reserves were replaced because the cost had dropped so sharply that it paid to replace them. This fact was hidden by the so-called finding cost per Mcf equivalent. This is expenditures on exploration plus development, for oil and gas together, divided by the reserve-additions of oil plus gas reduced to an equivalent, usually of 6:1 but sometimes a higher ratio

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.

Advances in electric power and energy systems load and price forecasting

CERN Document Server

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...

Short-term electricity price forecast based on the improved hybrid model

International Nuclear Information System (INIS)

Dong Yao; Wang Jianzhou; Jiang He; Wu Jie

2011-01-01

Highlights: → The proposed models can detach high volatility and daily seasonality of electricity price. → The improved hybrid forecast models can make full use of the advantages of individual models. → The proposed models create commendable improvements that are relatively satisfactorily for current research. → The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

Short-term electricity price forecast based on the improved hybrid model

Energy Technology Data Exchange (ETDEWEB)

Dong Yao, E-mail: dongyao20051987@yahoo.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Wang Jianzhou, E-mail: wjz@lzu.edu.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Jiang He; Wu Jie [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China)

2011-08-15

Highlights: {yields} The proposed models can detach high volatility and daily seasonality of electricity price. {yields} The improved hybrid forecast models can make full use of the advantages of individual models. {yields} The proposed models create commendable improvements that are relatively satisfactorily for current research. {yields} The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

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.

Oil price assumptions in macroeconomic forecasts: should we follow future market expectations?

International Nuclear Information System (INIS)

Coimbra, C.; Esteves, P.S.

2004-01-01

In macroeconomic forecasting, in spite of its important role in price and activity developments, oil prices are usually taken as an exogenous variable, for which assumptions have to be made. This paper evaluates the forecasting performance of futures market prices against the other popular technical procedure, the carry-over assumption. The results suggest that there is almost no difference between opting for futures market prices or using the carry-over assumption for short-term forecasting horizons (up to 12 months), while, for longer-term horizons, they favour the use of futures market prices. However, as futures market prices reflect market expectations for world economic activity, futures oil prices should be adjusted whenever market expectations for world economic growth are different to the values underlying the macroeconomic scenarios, in order to fully ensure the internal consistency of those scenarios. (Author)

Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

OpenAIRE

Saber Talari; Miadreza Shafie-khah; Gerardo J. Osório; Fei Wang; Alireza Heidari; João P. S. Catalão

2017-01-01

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...

Forecasting the price of gold: An error correction approach

Directory of Open Access Journals (Sweden)

Kausik Gangopadhyay

2016-03-01

Full Text Available Gold prices in the Indian market may be influenced by a multitude of factors such as the value of gold in investment decisions, as an inflation hedge, and in consumption motives. We develop a model to explain and forecast gold prices in India, using a vector error correction model. We identify investment decision and inflation hedge as prime movers of the data. We also present out-of-sample forecasts of our model and the related properties.

Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

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)

AN EVALUATION OF POINT AND DENSITY FORECASTS FOR SELECTED EU FARM GATE MILK PRICES

Directory of Open Access Journals (Sweden)

Dennis Bergmann

2018-01-01

Full Text Available Fundamental changes to the common agricultural policy (CAP have led to greater market orientation which in turn has resulted in sharply increased variability of EU farm gate milk prices and thus farmers’ income. In this market environment reliable forecasts of farm gate milk prices are extremely important as farmers can make improved decisions with regards to cash flow management and budget preparation. In addition these forecasts may be used in setting fixed priced contracts between dairy farmers and processors thus providing certainty and reducing risk. In this study both point and density forecasts from various time series models for farm gate milk prices in Germany, Ireland and for an average EU price series are evaluated using a rolling window framework. Additionally forecasts of the individual models are combined using different combination schemes. The results of the out of sample evaluation show that ARIMA type models perform well on short forecast horizons (1 to 3 month while the structural time series approach performs well on longer forecast horizons (12 month. Finally combining individual forecasts of different models significantly improves the forecast performance for all forecast horizons.

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

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

Modeling and forecasting electricity price jumps in the Nord Pool power market

DEFF Research Database (Denmark)

Knapik, Oskar

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......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...

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

The Pricing of natural gas

International Nuclear Information System (INIS)

Nese, Gjermund

2004-11-01

The report focuses on the pricing of natural gas. The motivation has been the wish of the Norwegian authorities to increase the use of natural gas and that this should follow market conditions. The pricing of gas occurs at present in various ways in the different markets. The report identifies to main factors behind the pricing. 1) The type of market i.e. how far the liberalization of the gas markets has gone in the various countries. 2) The development within the regulation, climate and tax policies. The gas markets are undergoing as the energy markets in general, a liberalization process where the traditional monopoly based market structures are replaced by markets based on competition. There are great differences in the liberalization development of the various countries, which is reflected in the various pricing principles applied for the trade of gas in the countries. The analysis shows that the net-back-pricing is predominant in some countries i.e. that the price is in various ways indexed towards and follow the development of the price of alternative energy carriers so that the gas may be able to compete. The development towards trade places for gas where the pricing is based on offer and demand is already underway. As the liberalization of the European gas markets progresses it is expected that the gas price will be determined increasingly at spot markets instead of through bilateral agreements between monopolistic corporations. The development within the regulation, climate and tax policies and to what extent this may influence the gas prices in the future, are also studied. There seem to be effects that may pull in both directions but it is evident that these political variables will influence the gas pricing in the international market to a large extent and thereby also the future internal natural gas market

Price Density Forecasts in the U.S. Hog Market: Composite Procedures

NARCIS (Netherlands)

Trujillo Barrera, A.A.; Garcia, P.; Mallory, M.

2013-01-01

Abstract We develop and evaluate quarterly out-of-sample individual and composite density forecasts for U.S. hog prices using data from 1975.I to 2010.IV. Individual forecasts are generated from time series models and the implied distribution of USDA outlook forecasts. Composite density forecasts

Have oil and gas prices got separated?

International Nuclear Information System (INIS)

Erdős, Péter

2012-01-01

This paper applies vector error correction models that show that oil and natural gas prices decoupled around 2009. Before 2009, US and UK gas prices had a long-term equilibrium with crude prices to which gas prices always reverted after exogenous shocks. Both US and UK gas prices adjusted to the crude oil price individually, and departure from the equilibrium gas price on one continent resulted in a similar departure on the other. After an exogenous shock, the adjustment between US and UK gas prices took approximately 20 weeks on average, and the convergence was mediated mainly by crude oil with a necessary condition that arbitrage across the Atlantic was possible. After 2009, however, the UK gas price has remained integrated with oil price, but the US gas price decoupled from crude oil price and the European gas price, as the Atlantic arbitrage has halted. The oversupply from shale gas production has not been mitigated by North American export, as there has been no liquefying and export capacity. - Highlights: ► VEC models are applied to investigate the relationship between oil and natural gas prices. ► While natural gas prices in Europe and Asia react to oil price, US gas price decoupled from oil in 2009. ► Since 2009, the US gas price has decoupled from the European and Asian gas prices.

Separated influence of crude oil prices on regional natural gas import prices

International Nuclear Information System (INIS)

Ji, Qiang; Geng, Jiang-Bo; Fan, Ying

2014-01-01

This paper analyses the impact of global economic activity and international crude oil prices on natural gas import prices in three major natural gas markets using the panel cointegration model. It also investigates the shock impacts of the volatility and the increase and decrease of oil prices on regional natural gas import prices. The results show that both global economic activity and international crude oil prices have significant long-term positive effects on regional natural gas import prices. The volatility of international crude oil prices has a negative impact on regional natural gas import prices. The shock impact is weak in North America, lags in Europe and is most significant in Asia, which is mainly determined by different regional policies for price formation. In addition, the response of natural gas import prices to increases and decreases in international crude oil prices shows an asymmetrical mechanism, of which the decrease impact is relatively stronger. - Highlights: • Impacts of world economy and oil prices on regional natural gas prices are analysed • North American natural gas prices are mainly affected by world economy • Asian and European natural gas prices are mainly affected by oil prices • The volatility of oil prices has a negative impact on regional natural gas prices • The response of natural gas import prices to oil prices up and down shows asymmetry

Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants

Directory of Open Access Journals (Sweden)

Rasika Yatigammana

2018-05-01

Full Text Available The direction of price movements are analysed under an ordered probit framework, recognising the importance of accounting for discreteness in price changes. By extending the work of Hausman et al. (1972 and Yang and Parwada (2012,This paper focuses on improving the forecast performance of the model while infusing a more practical perspective by enhancing flexibility. This is achieved by extending the existing framework to generate short term multi period ahead forecasts for better decision making, whilst considering the serial dependence structure. This approach enhances the flexibility and adaptability of the model to future price changes, particularly targeting risk minimisation. Empirical evidence is provided, based on seven stocks listed on the Australian Securities Exchange (ASX. The prediction success varies between 78 and 91 per cent for in-sample and out-of-sample forecasts for both the short term and long term.

Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

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.

Classical gas: Hearty prices, robust demand combine to pump breezy optimism through 2005 forecasts

International Nuclear Information System (INIS)

Lunan, D.

2005-01-01

The outlook for natural gas in 2005 is said to be a watershed year, with a lengthy list of developments that could have significant effect on the industry for many years to come. In light of continuing high demand and static supply prospects, prices will have to continue to be high in order to ensure the necessary infrastructure investments to keep gas flowing from multiple sources to the consumer. It is predicted that against the backdrop of robust prices several supply initiatives will continue to advance rapidly in 2005, such as the $7 billion Mackenzie Gas Project on which public hearings are expected to start this summer, along with regulatory clarity about the $20 billion Alaska Highway Natural Gas Pipeline Project to move North Slope gas to southern markets. Drilling of new gas wells will continue to approach or even surpass 18,000 new wells, with an increasing number of these being coal-bed methane wells. Despite high level drilling activity, supply is expected to grow only about 400 MMcf per day. Greater supply increments are expected through continued LNG terminal development, although plans for new LNG terminal development have been met with stiff resistance from local residents both in Canada and the United States. Imports of liquefied natural gas into the United States slowed dramatically in 2004 under the severe short-term downward pressure on natural gas prices, nevertheless, these imports are expected to rebound to new record highs in 2005. Capacity is expected to climb from about 2.55 Bcf per day in 2004 to as much as 6.4 Bcf per day by late 2007. At least one Canadian import facility, Anadarko's one Bcf per day Bear Head terminal on Nova Scotia's Strait of Canso, is expected to become operational by late 2007 or early 2008. 6 photos

Forecasting loads and prices in competitive power markets

International Nuclear Information System (INIS)

Bunn, D.W.

2000-01-01

This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations, and neural networks for forecasting the demand side, and strategic simulation using artificial agents for the supply side

Market fundamentals, competition and natural-gas prices

International Nuclear Information System (INIS)

Hulshof, Daan; Maat, Jan-Pieter van der; Mulder, Machiel

2016-01-01

After the liberalisation of the gas industry, trading hubs have emerged in Europe. Although these hubs appear to be liquid market places fostering gas-to-gas competition, the efficiency of the gas market remains a topic of interest as a fair share of gas is still traded through long-term contracts with prices linked to the oil price while the number of gas suppliers to the European market is limited. In order to assess the efficiency of the gas market, we analyse the day-ahead spot price at the Dutch gas hub over the period 2011–2014. We find that the oil price had a small positive impact on the gas price. Changes in the concentration on the supply side did not affect the movement in gas prices. The availability of gas in storages and the outside temperature negatively influenced the gas price. We also find that the gas price was related to the production of wind electricity. Overall, we conclude that the day-ahead gas prices are predominantly determined by gas-market fundamentals. Policies to further integrate gas markets within Europe may extend this gas-to-gas competition to a larger region. - Highlights: •We analyse the development of the day-ahead spot price at TTF over 2011–2014. •The oil price had a small impact on the gas price, while the coal price had no effect. •Changes in the concentration on the supply side did not affect the gas prices. •The gas prices are predominantly determined by weather and storage availability. •Policies to integrate gas markets foster gas-to-gas competition.

Business cycles and natural gas prices

International Nuclear Information System (INIS)

Apostolos, S.; Asghar, S.

2005-01-01

This paper investigates the basic stylised facts of natural gas price movements using data for the period that natural gas has been traded on an organised exchange and the methodology suggested by Kydland and Prescott (1990). Our results indicate that natural gas prices are procyclical and lag the cycle of industrial production. Moreover, natural gas prices are positively contemporaneously correlated with United States consumer prices and lead the cycle of consumer prices, raising the possibility that natural gas prices might be a useful guide for US monetary policy, like crude oil prices are, possibly serving as an important indicator variable. (author)

Pricing of natural gas in Kazakhstan

International Nuclear Information System (INIS)

Zhapargaliev, I.K.

1996-01-01

Two state companies are in charge of natural gas supply in Kazakhstan. They buy, transport and sell natural gas and have monopolized the industry and provoked increase of gas prices. Ministry of Oil and gas Industry proposed demonopolization. The restructuring that took place caused new distribution of tasks in the gas industry. A more competitive environment was created leading to normalization of the natural gas prices. All economic subjects were granted the right to acquire gas regardless the type of ownership. Measures implemented for reorganization of gas companies contributed to the reduction of gas transport costs and prices by 50% and to decrease of gas prices in the southern regions by 50%. Despite these measures gas prices for household sector are still unchanged and are below the import prices, the main reason being the low average household income

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)

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 ...... on explanatory variables. Bayesian inference is explored in order to obtain predictive densities. The main focus of the paper is on shorttime density forecasting in Nord Pool intraday market. We show that the proposed model outperforms several benchmark models at this task....

Electricity prices forecasting by automatic dynamic harmonic regression models

International Nuclear Information System (INIS)

Pedregal, Diego J.; Trapero, Juan R.

2007-01-01

The changes experienced by electricity markets in recent years have created the necessity for more accurate forecast tools of electricity prices, both for producers and consumers. Many methodologies have been applied to this aim, but in the view of the authors, state space models are not yet fully exploited. The present paper proposes a univariate dynamic harmonic regression model set up in a state space framework for forecasting prices in these markets. The advantages of the approach are threefold. Firstly, a fast automatic identification and estimation procedure is proposed based on the frequency domain. Secondly, the recursive algorithms applied offer adaptive predictions that compare favourably with respect to other techniques. Finally, since the method is based on unobserved components models, explicit information about trend, seasonal and irregular behaviours of the series can be extracted. This information is of great value to the electricity companies' managers in order to improve their strategies, i.e. it provides management innovations. The good forecast performance and the rapid adaptability of the model to changes in the data are illustrated with actual prices taken from the PJM interconnection in the US and for the Spanish market for the year 2002. (author)

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

Science.gov (United States)

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.

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)

Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT

International Nuclear Information System (INIS)

Anbazhagan, S.; Kumarappan, N.

2014-01-01

Highlights: • We presented DCT input featured FFNN model for forecasting in Spain market. • The key factors impacting electricity price forecasting are historical prices. • Past 42 days were trained and the next 7 days were forecasted. • The proposed approach has a simple and better NN structure. • The DCT-FFNN mode is effective and less computation time than the recent models. - Abstract: In a deregulated market, a number of factors determined the outcome of electricity price and displays a perplexed and maverick fluctuation. Both power producers and consumers needs single compact and robust price forecasting tool in order to maximize their profits and utilities. In order to achieve the helter–skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN). The proposed FFNN is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been predicted that the DCT-FFNN model is close to the state of the art can be achieved with less computation time. The proposed DCT-FFNN approach is compared with 17 other recent approaches to estimate the market clearing prices of mainland Spain. Finally, the accuracy of the price forecasting is also applied to the electricity market of New York in year 2010 that shows the effectiveness of the proposed DCT-FFNN approach

Electricity price forecasting in deregulated markets: A review and evaluation

Energy Technology Data Exchange (ETDEWEB)

Aggarwal, Sanjeev Kumar; Saini, Lalit Mohan; Kumar, Ashwani [Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana (India)

2009-01-15

The main methodologies used in electricity price forecasting have been reviewed in this paper. The following price-forecasting techniques have been covered: (i) stochastic time series, (ii) causal models, and (iii) artificial intelligence based models. The quantitative analysis of the work done by various authors has been presented based on (a) time horizon for prediction, (b) input variables, (c) output variables, (d) results, (e) data points used for analysis, (f) preprocessing technique employed, and (g) architecture of the model. The results have been presented in the form of tables for ease of comparison. Classification of various price-influencing factors used by different researchers has been done and put for reference. Application of various models as applied to different electricity markets is also presented for consideration. (author)

Electricity price forecasting in deregulated markets: A review and evaluation

International Nuclear Information System (INIS)

Aggarwal, Sanjeev Kumar; Saini, Lalit Mohan; Kumar, Ashwani

2009-01-01

The main methodologies used in electricity price forecasting have been reviewed in this paper. The following price-forecasting techniques have been covered: (i) stochastic time series, (ii) causal models, and (iii) artificial intelligence based models. The quantitative analysis of the work done by various authors has been presented based on (a) time horizon for prediction, (b) input variables, (c) output variables, (d) results, (e) data points used for analysis, (f) preprocessing technique employed, and (g) architecture of the model. The results have been presented in the form of tables for ease of comparison. Classification of various price-influencing factors used by different researchers has been done and put for reference. Application of various models as applied to different electricity markets is also presented for consideration. (author)

Coal Price Forecasting and Structural Analysis in China

Directory of Open Access Journals (Sweden)

Xiaopeng Guo

2016-01-01

Full Text Available Coal plays an important role in China’s energy structure and its price has been continuously decreasing since the second half of 2012. Constant low price of coal affected the profits of coal enterprises and the coal use of its downstream firms; the precision of coal price provides a reference for these enterprises making their management strategy. Based on the historical data of coal price and related factors such as port stocks, sales volume, futures prices, Producer Price Index (PPI, and crude oil price rate from November 2013 to June 2016, this study aims to forecast coal price using vector autoregression (VAR model and portray the dynamic correlations between coal price and variables by the impulse response function and variance decomposition. Comparing predicted and actual values, the root mean square error (RMSE was small which indicated good precision of this model. Thus this short period prediction can help these enterprises make the right business decisions.

Decoupling the Oil and Gas Prices. Natural Gas Pricing in the Post-Financial Crisis Market

International Nuclear Information System (INIS)

Kanai, Miharu

2011-01-01

This paper looks into natural gas pricing in the post-financial crisis market and, in particular, examines the question whether the oil-linked gas pricing system has outlived its utility as global gas markets mature and converge more rapidly than expected and as large new resources of unconventional gas shift the gas terms-of-trade. Two opposing natural gas pricing systems have coexisted for the last two decades. On the one hand, there is traditional oil-linked pricing, used in pipeline gas imports by Continental European countries and in LNG imports by the countries in Far East. The other is the system led by futures exchanges in deregulated, competitive markets largely in the UK and the US. World gas markets are changing and the basis and mechanisms of price formation are changing with them. There is no reason to expect a revolution in gas pricing, but formulas designed to address the challenges of the 1970's will need to adjust to the realities of the present and expectations for the 21. century. Because such changes will imply a redistribution of costs and benefits, vested shareholders will defend the status quo. But hopefully and ultimately, appropriately regulated markets will assert themselves and shareholders along the entire value chain will have their interests served

The price of natural gas

International Nuclear Information System (INIS)

Bakhtiari, A.M.S.

2001-01-01

Natural gas used to be a relatively cheap primary energy source, always at a discount to crude oil (on a comparative British thermal unit basis). It gradually evolved into a major resource during the 20th century - reaching a 24 per cent share of global primary energy in 1999. In the year 2000, natural gas prices in the USA rose to unheard-of highs of 10/million US dollars Btu, ushering in a new era, with natural gas at a 120 per cent premium to crude oil. This clearly was a watershed for gas, somehow similar to the 1973-74 watershed for oil prices. And similarly, any return to the status quo-ante looks rather improbable, although a number of experts (alongside the International Energy Agency) still believe the 2000 price 'spike' to have been ''only transitory''. The consequences of higher gas prices (at a level equal to crude oil prices on a Btu basis) will be multifaceted and momentous, altering habits and uses in downstream industries and economic sectors, as well as providing added income for major gas-exporters, such as Russia, Canada and Algeria. Another potential consequence of the 2000 watershed might be to propel US standard prices (such as the 'Henry Hub' spot) to international status and gas price-setter, as the 'WTI spot' became an 'international benchmark' for crude oils in the post-1993 era. For the time being, the equality of gas and oil prices has become the new norm; but, in the longer term, a discount of crude oil relative to natural gas might be envisaged, as the latter is a cleaner fuel and emits less carbon dioxide when used. (author)

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......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...

Quantifying the value that energy efficiency and renewable energy provide as a hedge against volatile natural gas prices

Energy Technology Data Exchange (ETDEWEB)

Bolinger, Mark; Wiser, Ryan; Bachrach, Devra; Golove, William

2002-05-15

Advocates of energy efficiency and renewable energy have long argued that such technologies can mitigate fuel price risk within a resource portfolio. Such arguments--made with renewed vigor in the wake of unprecedented natural gas price volatility during the winter of 2000/2001--have mostly been qualitative in nature, however, with few attempts to actually quantify the price stability benefit that these sources provide. In evaluating this benefit, it is important to recognize that alternative price hedging instruments are available--in particular, gas-based financial derivatives (futures and swaps) and physical, fixed-price gas contracts. Whether energy efficiency and renewable energy can provide price stability at lower cost than these alternative means is therefore a key question for resource acquisition planners. In this paper we evaluate the cost of hedging gas price risk through financial hedging instruments. To do this, we compare the price of a 10-year natural gas swap (i.e., what it costs to lock in prices over the next 10 years) to a 10-year natural gas price forecast (i.e., what the market is expecting spot natural gas prices to be over the next 10 years). We find that over the past two years natural gas users have had to pay a premium as high as $0.76/mmBtu (0.53/242/kWh at an aggressive 7,000 Btu/kWh heat rate) over expected spot prices to lock in natural gas prices for the next 10 years. This incremental cost to hedge gas price risk exposure is potentially large enough - particularly if incorporated by policymakers and regulators into decision-making practices - to tip the scales away from new investments in variable-price, natural gas-fired generation and in favor of fixed-price investments in energy efficiency and renewable energy.

Quantifying the value that energy efficiency and renewable energy provide as a hedge against volatile natural gas prices

International Nuclear Information System (INIS)

Bolinger, Mark; Wiser, Ryan; Bachrach, Devra; Golove, William

2002-01-01

Advocates of energy efficiency and renewable energy have long argued that such technologies can mitigate fuel price risk within a resource portfolio. Such arguments-made with renewed vigor in the wake of unprecedented natural gas price volatility during the winter of 2000/2001-have mostly been qualitative in nature, however, with few attempts to actually quantify the price stability benefit that these sources provide. In evaluating this benefit, it is important to recognize that alternative price hedging instruments are available-in particular, gas-based financial derivatives (futures and swaps) and physical, fixed-price gas contracts. Whether energy efficiency and renewable energy can provide price stability at lower cost than these alternative means is therefore a key question for resource acquisition planners. In this paper we evaluate the cost of hedging gas price risk through financial hedging instruments. To do this, we compare the price of a 10-year natural gas swap (i.e., what it costs to lock in prices over the next 10 years) to a 10-year natural gas price forecast (i.e., what the market is expecting spot natural gas prices to be over the next 10 years). We find that over the past two years natural gas users have had to pay a premium as high as$0.76/mmBtu (0.53/242/kWh at an aggressive 7,000 Btu/kWh heat rate) over expected spot prices to lock in natural gas prices for the next 10 years. This incremental cost to hedge gas price risk exposure is potentially large enough - particularly if incorporated by policymakers and regulators into decision-making practices - to tip the scales away from new investments in variable-price, natural gas-fired generation and in favor of fixed-price investments in energy efficiency and renewable energy

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Science.gov (United States)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

International Nuclear Information System (INIS)

Tan, Zhongfu; Zhang, Jinliang; Xu, Jun; Wang, Jianhui

2010-01-01

This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)

Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

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.

Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme

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

An electricity price model with consideration to load and gas price effects.

Science.gov (United States)

Huang, Min-xiang; Tao, Xiao-hu; Han, Zhen-xiang

2003-01-01

Some characteristics of the electricity load and prices are studied, and the relationship between electricity prices and gas (fuel) prices is analyzed in this paper. Because electricity prices are strongly dependent on load and gas prices, the authors constructed a model for electricity prices based on the effects of these two factors; and used the Geometric Mean Reversion Brownian Motion (GMRBM) model to describe the electricity load process, and a Geometric Brownian Motion(GBM) model to describe the gas prices; deduced the price stochastic process model based on the above load model and gas price model. This paper also presents methods for parameters estimation, and proposes some methods to solve the model.

Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price

Science.gov (United States)

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.

Forecasting Nord Pool day-ahead prices with an autoregressive model

International Nuclear Information System (INIS)

Kristiansen, Tarjei

2012-01-01

This paper presents a model to forecast Nord Pool hourly day-ahead prices. The model is based on but reduced in terms of estimation parameters (from 24 sets to 1) and modified to include Nordic demand and Danish wind power as exogenous variables. We model prices across all hours in the analysis period rather than across each single hour of 24 hours. By applying three model variants on Nord Pool data, we achieve a weekly mean absolute percentage error (WMAE) of around 6–7% and an hourly mean absolute percentage error (MAPE) ranging from 8% to 11%. Out of sample results yields a WMAE and an hourly MAPE of around 5%. The models enable analysts and traders to forecast hourly day-ahead prices accurately. Moreover, the models are relatively straightforward and user-friendly to implement. They can be set up in any trading organization. - Highlights: ► Forecasting Nord Pool day-ahead prices with an autoregressive model. ► The model is based on but with the set of parameters reduced from 24 to 1. ► The model includes Nordic demand and Danish wind power as exogenous variables. ► Hourly mean absolute percentage error ranges from 8% to 11%. ► Out of sample results yields a WMAE and an hourly MAPE of around 5%.

Forward curves, scarcity and price volatility in oil and natural gas markets

International Nuclear Information System (INIS)

Geman, Helyette; Ohana, Steve

2009-01-01

The role of inventory in explaining the shape of the forward curve and spot price volatility in commodity markets is central in the theory of storage developed by Kaldor [Kaldor, N. (1939) ''Speculation and Economic Stability'', The Review of Economic Studies 7, 1-27] and Working [Working, H. (1949) ''The theory of the price of storage'', American Economic Review, 39, 1254-1262] and has since been documented in a vast body of financial literature, including the reference paper by Fama and French [Fama, E.F. and K.R. French (1987) ''Commodity futures prices: some evidence on forecast power, premiums and the theory of storage'', Journal of Business 60, 55-73] on metals. The goal of this paper is twofold: 1. validate in the case of oil and natural gas the use of the slope of the forward curve as a proxy for inventory (the slope being defined in a way that filters out seasonality); 2. analyze directly for these two major commodities the relationship between inventory and price volatility. In agreement with the theory of storage, we find that: 1. the negative correlation between price volatility and inventory is globally significant for crude oil; 2. this negative correlation prevails only during those periods of scarcity when the inventory is below the historical average and increases importantly during the winter periods for natural gas. Our results are illustrated by the analysis of a 15 year-database of US oil and natural gas prices and inventory. (author)

Gas Price Formation, Structure and Dynamics

Energy Technology Data Exchange (ETDEWEB)

Davoust, R.

2008-07-01

Our study, focused on gas prices in importing economies, describes wholesale prices and retail prices, their evolution for the last one or two decades, the economic mechanisms of price formation. While an international market for oil has developed thanks to moderate storage and transportation charges, these costs are much higher in the case of natural gas, which involves that this energy is still traded inside continental markets. There are three regional gas markets around the world: North America (the United States, importing mainly from Canada and Mexico), Europe (importing mainly from Russia, Algeria and Norway) and Asia (Japan, Korea, Taiwan, China and India, importing mainly from Indonesia, Malaysia and Australia). A market for gas has also developed in South America, but it will not be covered by our paper. In Europe and the US, due to large domestic resources and strong grids, natural gas is purchased mostly through pipelines. In Northeast Asia, there is a lack of such infrastructures, so imported gas takes mainly the form of Liquefied Natural Gas (LNG), shipped on maritime tankers. Currently, the LNG market is divided into two zones: the Atlantic Basin (Europe and US) and the Pacific Basin (Asia and the Western Coast of America). For the past few years, the Middle East and Africa have tended to be crucial suppliers for both LNG zones. Gas price formation varies deeply between regional markets, depending on several structural factors (regulation, contracting practises, existence of a spot market, liquidity, share of imports). Empirically, the degree of market opening (which corresponds to the seniority in the liberalization process) seems to be the primary determinant of pricing patterns. North America has the most liberalized and well-performing natural gas industry in the world. Gas pricing is highly competitive and is based on supply/demand balances. Spot and futures markets are developed. The British gas sector is also deregulated and thus follows a

Gas Price Formation, Structure and Dynamics

International Nuclear Information System (INIS)

Davoust, R.

2008-01-01

Our study, focused on gas prices in importing economies, describes wholesale prices and retail prices, their evolution for the last one or two decades, the economic mechanisms of price formation. While an international market for oil has developed thanks to moderate storage and transportation charges, these costs are much higher in the case of natural gas, which involves that this energy is still traded inside continental markets. There are three regional gas markets around the world: North America (the United States, importing mainly from Canada and Mexico), Europe (importing mainly from Russia, Algeria and Norway) and Asia (Japan, Korea, Taiwan, China and India, importing mainly from Indonesia, Malaysia and Australia). A market for gas has also developed in South America, but it will not be covered by our paper. In Europe and the US, due to large domestic resources and strong grids, natural gas is purchased mostly through pipelines. In Northeast Asia, there is a lack of such infrastructures, so imported gas takes mainly the form of Liquefied Natural Gas (LNG), shipped on maritime tankers. Currently, the LNG market is divided into two zones: the Atlantic Basin (Europe and US) and the Pacific Basin (Asia and the Western Coast of America). For the past few years, the Middle East and Africa have tended to be crucial suppliers for both LNG zones. Gas price formation varies deeply between regional markets, depending on several structural factors (regulation, contracting practises, existence of a spot market, liquidity, share of imports). Empirically, the degree of market opening (which corresponds to the seniority in the liberalization process) seems to be the primary determinant of pricing patterns. North America has the most liberalized and well-performing natural gas industry in the world. Gas pricing is highly competitive and is based on supply/demand balances. Spot and futures markets are developed. The British gas sector is also deregulated and thus follows a

Practical Results of Forecasting for the Natural Gas Market

OpenAIRE

Potocnik, Primoz; Govekar, Edvard

2010-01-01

Natural gas consumption forecasting is required to balance the supply and consumption of natural gas. Companies and natural gas distributors are motivated to forecast their consumption by the economic incentive model that dictates the cash flow rules corresponding to the forecasting accuracy. The rules are quite challenging but enable the company to gain positive cash flow by forecasting accurately their short-term natural gas consumption. In this chapter, some practical forecasting results f...

The mirage of higher petroleum prices

International Nuclear Information System (INIS)

Lynch, M.C.

1996-01-01

Most petroleum industry price forecasters do not possess a record of which they can be proud. Long-term petroleum market forecasting has been so inaccurate that it has often been described as virtually impossible. To avoid criticism of their performance, many organizations no longer circulate their forecasts. Why have the forecasts been so wrong? Because of failure to predict supply. This paper reviews the erroneous methods used to predict price trends in the oil and gas industry and identifies methods to correct the problem

Density Forecasts of Crude-Oil Prices Using Option-Implied and ARCH-Type Models

DEFF Research Database (Denmark)

Tsiaras, Leonidas; Høg, Esben

  The predictive accuracy of competing crude-oil price forecast densities is investigated for the 1994-2006 period. Moving beyond standard ARCH models that rely exclusively on past returns, we examine the benefits of utilizing the forward-looking information that is embedded in the prices...... 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...

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

Alberta producers' gas export prices slip

International Nuclear Information System (INIS)

Chandrasekharaiah, M.N.; Dubben, G.; Kolster, B.H.

1992-01-01

This paper reports that Alberta gas producers have approved a new contract with California buyers that includes slightly lower wellhead prices and more flexible pricing terms. The 1 year agreement, will apply a flexible price formula to gas sales. A basic volume of 212 MMcfd will receive $1.52 (U.S.)/Mcf. A and S also will buy 200 MMcfd at prices paid for other Alberta gas in the California market. It will have the right to buy added volumes at prices indexed to gas sold into California from the U.S. Southwest. Ballots cast by producers were to be verified by regulatory agencies in Alberta and British Columbia. The more flexible price terms in the new contract are seen as a positive development for negotiations in a dispute over long term contracts

Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system

International Nuclear Information System (INIS)

Arciniegas, Alvaro I.; Arciniegas Rueda, Ismael E.

2008-01-01

The Ontario Electricity Market (OEM), which opened in May 2002, is relatively new and is still under change. In addition, the bidding strategies of the participants are such that the relationships between price and fundamentals are non-linear and dynamic. The lack of market maturity and high complexity hinders the use of traditional statistical methodologies (e.g., regression analysis) for price forecasting. Therefore, a flexible model is needed to achieve good forecasting in OEM. This paper uses a Takagi-Sugeno-Kang (TSK) fuzzy inference system in forecasting the one-day-ahead real-time peak price of the OEM. The forecasting results of TSK are compared with those obtained by traditional statistical and neural network based forecasting. The comparison suggests that TSK has considerable value in forecasting one-day-ahead peak price in OEM. (author)

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

Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

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.

Multi-step ahead forecasts for electricity prices using NARX: A new approach, a critical analysis of one-step ahead forecasts

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

Forward curves, scarcity and price volatility in oil and natural gas markets

Energy Technology Data Exchange (ETDEWEB)

Geman, Helyette [Birkbeck, University of London (United Kingdom); ESCP-EAP (France); Ohana, Steve [ESCP-EAP (France)

2009-07-15

The role of inventory in explaining the shape of the forward curve and spot price volatility in commodity markets is central in the theory of storage developed by Kaldor [Kaldor, N. (1939) ''Speculation and Economic Stability'', The Review of Economic Studies 7, 1-27] and Working [Working, H. (1949) ''The theory of the price of storage'', American Economic Review, 39, 1254-1262] and has since been documented in a vast body of financial literature, including the reference paper by Fama and French [Fama, E.F. and K.R. French (1987) ''Commodity futures prices: some evidence on forecast power, premiums and the theory of storage'', Journal of Business 60, 55-73] on metals. The goal of this paper is twofold: 1. validate in the case of oil and natural gas the use of the slope of the forward curve as a proxy for inventory (the slope being defined in a way that filters out seasonality); 2. analyze directly for these two major commodities the relationship between inventory and price volatility. In agreement with the theory of storage, we find that: 1. the negative correlation between price volatility and inventory is globally significant for crude oil; 2. this negative correlation prevails only during those periods of scarcity when the inventory is below the historical average and increases importantly during the winter periods for natural gas. Our results are illustrated by the analysis of a 15 year-database of US oil and natural gas prices and inventory. (author)

Time series analysis applied to construct US natural gas price functions for groups of states

International Nuclear Information System (INIS)

Kalashnikov, V.V.; Matis, T.I.; Perez-Valdes, G.A.

2010-01-01

The study of natural gas markets took a considerably new direction after the liberalization of the natural gas markets during the early 1990s. As a result, several problems and research opportunities arose for those studying the natural gas supply chain, particularly the marketing operations. Consequently, various studies have been undertaken about the econometrics of natural gas. Several models have been developed and used for different purposes, from descriptive analysis to practical applications such as price and consumption forecasting. In this work, we address the problem of finding a pooled regression formula relating the monthly figures of price and consumption volumes for each state of the United States during the last twenty years. The model thus obtained is used as the basis for the development of two methods aimed at classifying the states into groups sharing a similar price/consumption relationship: a dendrogram application, and an heuristic algorithm. The details and further applications of these grouping techniques are discussed, along with the ultimate purpose of using this pooled regression model to validate data employed in the stochastic optimization problem studied by the authors.

Forecasting short-run crude oil price using high- and low-inventory variables

International Nuclear Information System (INIS)

Ye, Michael; Zyren, John; Shore, Joanne

2006-01-01

Since inventories have a lower bound or a minimum operating level, economic literature suggests a nonlinear relationship between inventory level and commodity prices. This was found to be the case in the short-run crude oil market. In order to explore this inventory-price relationship, two nonlinear inventory variables are defined and derived from the monthly normal level and relative level of OECD crude oil inventories from post 1991 Gulf War to October 2003: one for the low inventory state and another for the high inventory state of the crude oil market. Incorporation of low- and high-inventory variables in a single equation model to forecast short-run WTI crude oil prices enhances the model fit and forecast ability

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

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.

Do changes in natural gas futures prices influence changes in natural gas spot prices?

International Nuclear Information System (INIS)

Herbert, J.H.

1993-01-01

Data on natural gas futures and spot markets are examined to determine if variability in price on futures markets influences variability in price on spot markets. Using econometric techniques, it is found that changes in futures contract prices do not precede changes in spot market prices. (Author)

Northeast U.S. update: price and demand issues

International Nuclear Information System (INIS)

Lucy, M.S.

1997-01-01

The issues affecting natural gas prices in the northeast United States were discussed. The supply of natural gas is high because of new pipeline projects and new market entrants. The demand for natural gas is also high because of nuclear plant closings, new electric plants, the clean air act, and economic growth. The supply of natural gas is expected to grow in the Northeast by 27 per cent by the year 2000. Future pipeline projects from Western Canada to Chicago, New York and Boston were examined and their effect on supply and pricing were analyzed. As another variable that affects the pricing of natural gas, a list of the nuclear plants that have closed and which may soon close in the northeast United States was provided, along with a list of new gas fired plants. Other factors affecting winter market prices in the northeast United States for 1997-1998 include El Nino, warm weather forecasts, NYMEX forecasts, natural gas demand, and low oil prices, were also reviewed. Cultivating long term comprehensive relationships, and focusing on customer service were considered the key to successful Canadian export growth. 7 tabs., 5 figs

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.

Canadian natural gas market: dynamics and pricing

International Nuclear Information System (INIS)

2000-01-01

This publication by the National Energy Board is part of a continuing program of assessing applications for long-term natural gas export licences. The market-based procedure used by the Board is based on the premise that the marketplace will generally operate in a way that will ensure that Canadian requirements for natural gas will be met at fair market prices. The market--based procedure consists of a public hearing and a monitoring component. The monitoring component involves the on-going assessment of Canadian energy markets to provide analyses of major energy commodities on either an individual or integrated commodity basis. This report is the result of the most recent assessment . It identifies factors that affect natural gas prices and describes the functioning of regional markets in Canada. It provides an overview of the energy demand, including recent trends, reviews the North American gas supply and markets, the natural gas pricing dynamics in Canada, and a regional analysis of markets, prices and dynamics in British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec and the Atlantic provinces. In general, demand growth outstripped growth in supply, but natural gas producers throughout North America have been responding to the current high price environment with aggressive drilling programs. The Board anticipates that in time, there will be a supply and demand response and accompanying relief in natural gas prices. A review of the annual weighted average border price paid for Alberta gas indicates that domestic gas users paid less than export customers until 1998, at which point the two prices converged, suggesting that Canadians have had access to natural gas at prices no less favourable than export customers. The influence of electronic trading systems such as NYMEX and AECO-C/NIT have had significant impact on the pricing of natural gas. These systems, by providing timely information to market participants. enables them to manage price

Rising prices squeeze gas marketer

Energy Technology Data Exchange (ETDEWEB)

Lunan, D.

2000-06-19

Apollo Gas, a Toronto-based gas marketer, is considering options to enhance unit holder value, including sale of its 21,000 gas supply contracts, just weeks after it was forced out of the Alberta market by rising gas prices. Although the company had reported first quarter revenues of more than $15 million and earnings through that period of about $2.1 million, increases of 33 per cent and 38 per cent respectively over the same period in 1999, the company is resigned to the fact that such performance markers are not likely to be reached again in the foreseeable future, hence the decision to sell. About 95 per cent of Apollo's current transportation service volumes are matched to existing fixed-price supply contract which are due to expire in November 2000. After that, it is about 75 per cent matched for the balance of the term of its customer contracts (mostly five years). This means that the company is exposed to market prices that are likely to continue to increase. If this prediction holds true, Apollo would be forced to purchase the unhedged volumes of gas it needs to service its customers in the spot market at prices higher than prices the company is charging to its customers.

Rising prices squeeze gas marketer

International Nuclear Information System (INIS)

Lunan, D.

2000-01-01

Apollo Gas, a Toronto-based gas marketer, is considering options to enhance unit holder value, including sale of its 21,000 gas supply contracts, just weeks after it was forced out of the Alberta market by rising gas prices. Although the company had reported first quarter revenues of more than $15 million and earnings through that period of about $2.1 million, increases of 33 per cent and 38 per cent respectively over the same period in 1999, the company is resigned to the fact that such performance markers are not likely to be reached again in the foreseeable future, hence the decision to sell. About 95 per cent of Apollo's current transportation service volumes are matched to existing fixed-price supply contract which are due to expire in November 2000. After that, it is about 75 per cent matched for the balance of the term of its customer contracts (mostly five years). This means that the company is exposed to market prices that are likely to continue to increase. If this prediction holds true, Apollo would be forced to purchase the unhedged volumes of gas it needs to service its customers in the spot market at prices higher than prices the company is charging to its customers

Natural gas pricing: concepts and international overview

Energy Technology Data Exchange (ETDEWEB)

Gorodicht, Daniel Monnerat [Gas Energy, Rio de Janeiro, RJ (Brazil); Veloso, Luciano de Gusmao; Fidelis, Marco Antonio Barbosa; Mathias, Melissa Cristina Pinto Pires [Agencia Nacional do Petroleo, Gas Natural e Biocombustiveis (ANP), Rio de Janeiro, RJ (Brazil)

2012-07-01

The core of this article is a critical analysis of different forms of pricing of natural gas existing in the world today. This paper is to describe the various scenarios of natural gas price formation models. Along the paper, the context is emphasized by considering their cases of applications and their results. Today, basically, there are three main groups of models for natural gas pricing: i) competition gas-on-gas, i.e., a liberalized natural gas market, II) gas indexed to oil prices or its products and III) bilateral monopolies and regulated prices. All the three groups of models have relevant application worldwide. Moreover, those are under dynamic influence of economic, technological and sociopolitical factors which bring complexity to the many existing scenarios. However, at first this paper builds a critical analysis of the international current situation of natural gas today and its economic relevance. (author)

Forecasting inflation based on the consumer price index, taking into account the impact of seasonal factors

Directory of Open Access Journals (Sweden)

A. K. Sapova

2017-01-01

Full Text Available The consumer price index is a key indicator of the inflation level in Russia. It is important for the Central Bank and Government in decision-making process. There is a strong need for high-quality analysis and accurate forecast of this index. Modelling and forecasting of consumer price index as a key indicator of inflation are relevant issues in current macroeconomic conditions. The article is dedicated to development of quality short-term forecast of consumer inflation level, with the impact of seasonal factor. Two classes of models (vector autoregression and time series models are considered. It was shown that vector autoregression model of the dependency between consumer price index and nominal effective exchange rate is worse for the proposes of inflation forecast then non-linear model with structural components and conventional heteroscedasticity. The practical significance of this work is that the developed approach to the forecasting of the consumer price index adjusted of seasonal factor can be very helpful for the purpose of proper assessment and regulation of inflation.

Energy markets and price relations

International Nuclear Information System (INIS)

Bergendahl, P.A.

1986-10-01

The aim of the report is to elucidate the way and extent of the dependence of the price of different energy species of one another and particularly of crude oil prices. Oil, coal and natural gas can substitute each other at many applications. The prices are dependent on mining, processing and transporting. Forecasting of prices and future trends are discussed

Price management mechanisms and the gas contract

International Nuclear Information System (INIS)

Dickson, D.J.

1996-01-01

Pricing objectives and risk management strategies that can be achieved through the proper use of the standard gas contract, were discussed. Main topics of discussion were: (1) gas sales contract and convertible pricing, (2) gas contract and imbedded hedging, gas contracts and exchange traded instruments, (4) gas contracts fixed for floating swaps, and OTC options and exotics, (5) options and exotic price structures, and (6) advantages and disadvantages of using the gas contract versus the swap agreement

Forecasting ability of the investor sentiment endurance index: The case of oil service stock returns and crude oil prices

International Nuclear Information System (INIS)

He, Ling T.; Casey, K.M.

2015-01-01

Using a binomial probability distribution model this paper creates an endurance index of oil service investor sentiment. The index reflects the probability of the high or low stock price being the close price for the PHLX Oil Service Sector Index. Results of this study reveal the substantial forecasting ability of the sentiment endurance index. Monthly and quarterly rolling forecasts of returns of oil service stocks have an overall accuracy as high as 52% to 57%. In addition, the index shows decent forecasting ability on changes in crude oil prices, especially, WTI prices. The accuracy of 6-quarter rolling forecasts is 55%. The sentiment endurance index, along with the procedure of true forecasting and accuracy ratio, applied in this study provides investors and analysts of oil service sector stocks and crude oil prices as well as energy policy-makers with effective analytical tools

Average regional end-use energy price projections to the year 2030

International Nuclear Information System (INIS)

1991-01-01

The energy prices shown in this report cover the period from 1991 through 2030. These prices reflect sector/fuel price projections from the Annual Energy Outlook 1991 (AEO) base case, developed using the Energy Information Administration's (EIA) Intermediate Future Forecasting System (IFFS) forecasting model. Projections through 2010 are AEO base case forecasts. Projections for the period from 2011 through 2030 were developed separately from the AEO for this report, and the basis for these projections is described in Chapter 3. Projections in this report include average energy prices for each of four Census Regions for the residential, commercial, industrial, and transportation end-use sectors. Energy sources include electricity, distillate fuel oil, liquefied petroleum gas, motor gasoline, residual fuel oil, natural gas, and steam coal. (VC)

Forecasting world natural gas supply

International Nuclear Information System (INIS)

Al-Fattah, S. M.; Startzman, R. A.

2000-01-01

Using the multi-cyclic Hubert approach, a 53 country-specific gas supply model was developed which enables production forecasts for virtually all of the world's gas. Supply models for some organizations such as OPEC, non-OPEC and OECD were also developed and analyzed. Results of the modeling study indicate that the world's supply of natural gas will peak in 2014, followed by an annual decline at the rate of one per cent per year. North American gas production is reported to be currently at its peak with 29 Tcf/yr; Western Europe will reach its peak supply in 2002 with 12 Tcf. According to this forecast the main sources of natural gas supply in the future will be the countries of the former Soviet Union and the Middle East. Between them, they possess about 62 per cent of the world's ultimate recoverable natural gas (4,880 Tcf). It should be noted that these estimates do not include unconventional gas resulting from tight gas reservoirs, coalbed methane, gas shales and gas hydrates. These unconventional sources will undoubtedly play an important role in the gas supply in countries such as the United States and Canada. 18 refs., 2 tabs., 18 figs

Price discovery in European natural gas markets

International Nuclear Information System (INIS)

Schultz, Emma; Swieringa, John

2013-01-01

We provide the first high-frequency investigation of price discovery within the physical and financial layers of Europe's natural gas markets. Testing not only looks at short-term return dynamics, but also considers each security's contribution to price equilibrium in the longer-term. Results show that UK natural gas futures traded on the Intercontinental Exchange display greater price discovery than physical trading at various hubs throughout Europe. - Highlights: • We use intraday data to gauge price discovery in European natural gas markets. • We explore short and long-term dynamics in physical and financial market layers. • Results show ICE's UK natural gas futures are the main venue for price discovery

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change

OpenAIRE

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...

North American natural gas price outlook

International Nuclear Information System (INIS)

Denhardt, R.

1998-01-01

Issues regarding future natural gas prices for North America were discussed. Various aspects of the issue including the relationship between storage, weather and prices, received attention. It was noted that strong demand-growth will be needed to support near-term Canadian export increases without price declines. The issue of Gulf Coast production was also discussed. Power generation using natural gas as fuel is expected to support strong growth in the demand for natural gas. tabs., figs

Canadian natural gas price debate

International Nuclear Information System (INIS)

Wight, G.

1998-01-01

Sunoco Inc. is a subsidiary of Suncor Energy, one of Canada's largest integrated energy companies having total assets of $2.8 billion. As one of the major energy suppliers in the country, Sunoco Inc has a substantial stake in the emerging trends in the natural gas industry, including the Canadian natural gas price debate. Traditionally, natural gas prices have been determined by the number of pipeline expansions, weather, energy supply and demand, and storage levels. In addition to all these traditional factors which still apply today, the present day natural gas industry also has to deal with deregulation, open competition and the global energy situation, all of which also have an impact on prices. How to face up to these challenges is the subject of this discourse. tabs., figs

A Note on Forecasting the Rate of Change of the Price of Oil: Asymmetric Loss and Forecast Rationality

Directory of Open Access Journals (Sweden)

Christian Pierdzioch

2013-03-01

Full Text Available We study whether forecasts of the rate of change of the price of oil are rational. To this end, we consider a model that allows the shape of forecasters’ loss function to be studied. The shape of forecasters’ loss function may be consistent with a symmetric or an asymmetric loss function. We find that an asymmetric loss function often (but not always makes forecasts look rational, and we also report that forecast rationality may have changed over time.

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)

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...

Oil price induced gas acquisition contracts. Immune to price changes; Oelpreisindizierte Gasbezugsvertraege. Immun gegen Preisaenderungen

Energy Technology Data Exchange (ETDEWEB)

Verhoeven, Meike [Soptim AG, Aachen (Germany)

2012-10-15

The gas price continues to be linked to the oil price. Gas utilities that must buy gas in these conditions and sell it at a fixed price incur considerable financial risk. Especially with long-term buying contracts, and especially for gas from Russia, producers insist on linking to the oil price. Gas utilities, on the other hand, had to stop to sell gas at a price linked to the oil price two years ago. Utilities attempt to protect themselves, e.g. via oil swaps. Professional portfolio management is necessary to cope with the risks and the highly complex processes involved.

Time series analysis applied to construct US natural gas price functions for groups of states

Energy Technology Data Exchange (ETDEWEB)

Kalashnikov, V.V. [Departamento de Ingenieria Industrial y de Sistemas, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Col. Tecnologico, Monterrey, Nuevo Leon, 64849 (Mexico); Matis, T.I. [Deparment of Industrial Engineering, Texas Tech University, 2500 Broadway, Lubbock, TX 79409 (United States); Perez-Valdes, G.A. [Departamento de Ingenieria Industrial y de Sistemas, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Col. Tecnologico, Monterrey, Nuevo Leon, 64849 (Mexico); Deparment of Industrial Engineering, Texas Tech University, 2500 Broadway, Lubbock, TX 79409 (United States)

2010-07-15

The study of natural gas markets took a considerably new direction after the liberalization of the natural gas markets during the early 1990s. As a result, several problems and research opportunities arose for those studying the natural gas supply chain, particularly the marketing operations. Consequently, various studies have been undertaken about the econometrics of natural gas. Several models have been developed and used for different purposes, from descriptive analysis to practical applications such as price and consumption forecasting. In this work, we address the problem of finding a pooled regression formula relating the monthly figures of price and consumption volumes for each state of the United States during the last twenty years. The model thus obtained is used as the basis for the development of two methods aimed at classifying the states into groups sharing a similar price/consumption relationship: a dendrogram application, and an heuristic algorithm. The details and further applications of these grouping techniques are discussed, along with the ultimate purpose of using this pooled regression model to validate data employed in the stochastic optimization problem studied by the authors. (author)

Wavelet regression model in forecasting crude oil price

Science.gov (United States)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

The pricing of natural gas in US markets

International Nuclear Information System (INIS)

Brown, S.P.A.; Yucel, M.K.

1993-01-01

Our econometric evidence indicates that changes in natural gas prices are unequal in the long run. Nonetheless, all downstream prices change by at least as much as the average well-head price. Statistically, residential and commercial prices change as much as the city gate price. In the face of persistent shocks, however, market institutions and market dynamics can lead to lengthy periods in which the residential and commercial prices of natural gas adjust less than the wellhead or city gate prices. Electrical and industrial users of natural gas rely heavily on spot supplies and can switch fuels easily. Their ability to switch fuels may be related to the development of a spot market to serve them. Reliance on the spot market may explain why these end users have seen a greater reduction in natural gas prices than have the LDCs over the past seven years. The ability to switch fuels may account for electrical and industrial prices being the source of shocks in their relationships with the wellhead price. It also may explain why prices in these end-sue markets are quick to adjust. Commercial and residential customers cannot switch fuels easily and rely heavily on LDCs for their natural gas. The inability of these end users to switch fuels probably contributes to the reluctance of LDCs to purchase spot supplies of gas. Reliance on contract supplies may explain why the city gate price has not declined as much as electrical and industrial prices of natural gas over the past seven years. Furthermore, the LDCs administer prices in the commercial and residential markets under state regulation

Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds

Directory of Open Access Journals (Sweden)

Hankyeung Choi

2015-04-01

Full Text Available Given the emerging consensus from previous studies that crude oil and refined product (as well as crack spread prices are cointegrated, this study examines the link between the crude oil spot and crack spread derivatives markets. Specifically, the usefulness of the two crack spread derivatives products (namely, crack spread futures and the ETF crack spread for modeling and forecasting daily OPEC crude oil spot prices is evaluated. Based on the results of a structural break test, the sample is divided into pre-crisis, crisis, and post-crisis periods. We find a unidirectional relationship from the two crack spread derivatives markets to the crude oil spot market during the post-crisis period. In terms of forecasting performance, the forecasting models based on crack spread futures and the ETF crack spread outperform the Random Walk Model (RWM, both in-sample and out-of-sample. In addition, on average, the results suggest that information from the ETF crack spread market contributes more to the forecasting models than information from the crack spread futures market.

On the importance of the long-term seasonal component in day-ahead electricity price forecasting

International Nuclear Information System (INIS)

Nowotarski, Jakub; Weron, Rafał

2016-01-01

In day-ahead electricity price forecasting (EPF) the daily and weekly seasonalities are always taken into account, but the long-term seasonal component (LTSC) is believed to add unnecessary complexity to the already parameter-rich models and is generally ignored. Conducting an extensive empirical study involving state-of-the-art time series models we show that (i) decomposing a series of electricity prices into a LTSC and a stochastic component, (ii) modeling them independently and (iii) combining their forecasts can bring – contrary to a common belief – an accuracy gain compared to an approach in which a given time series model is calibrated to the prices themselves. - Highlights: • A new class of Seasonal Component AutoRegressive (SCAR) models is introduced. • Electricity prices are decomposed into a trend-seasonal and a stochastic component. • Both components are modeled independently, their forecasts are combined. • Significant accuracy gains can be achieved compared to commonly used approaches.

Weighted Average Cost of Retail Gas (WACORG) highlights pricing effects in the US gas value chain: Do we need wellhead price-floor regulation to bail out the unconventional gas industry?

International Nuclear Information System (INIS)

Weijermars, Ruud

2011-01-01

The total annual revenue stream in the US natural gas value chain over the past decade is analyzed. Growth of total revenues has been driven by higher wellhead prices, which peaked in 2008. The emergence of the unconventional gas business was made possible in part by the pre-recessional rise in global energy prices. The general rise in natural gas prices between 1998 and 2008 did not lower overall US gas consumption, but shifts have occurred during the past decade in the consumption levels of individual consumer groups. Industry's gas consumption has decreased, while power stations increased their gas consumption. Commercial and residential consumers maintained flat gas consumption patterns. This study introduces the Weighted Average Cost of Retail Gas (WACORG) as a tool to calculate and monitor an average retail price based on the different natural gas prices charged to the traditional consumer groups. The WACORG also provides insight in wellhead revenues and may be used as an instrument for calibrating retail prices in support of wellhead price-floor regulation. Such price-floor regulation is advocated here as a possible mitigation measure against excessive volatility in US wellhead gas prices to improve the security of gas supply. - Highlights: → This study introduces an average retail price, WACORG. → WACORG can monitor price differentials for the traditional US gas consumer groups. → WACORG also provides insight in US wellhead revenues. → WACORG can calibrate retail prices in support of wellhead price-floor regulation. → Gas price-floor can improve security of gas supply by reducing price volatility.

Gas markets and pricing in Asia

International Nuclear Information System (INIS)

Mashayekhi, A.; Law, P.L.

1992-01-01

The issues of natural gas market development and pricing are reviewed within the context of specific Asian countries where gas plays an important role. Within Southeast Asia, Malaysia's Penninsular Gas Utilization project signals a new era in pipeline gas trade with an agreement to supply Singapore. There is now also an opportunity to extend Malaysian pipeline supplies to Thailand, which is actively seeking natural gas from neighboring countries. The prospects for LNG are dominated by the high growth markets of Japan, South Korea, and Taiwan. LNG trade has tended to bind the region together through close economic ties. Due to the increasing damand within the supplier countries themselves and their close neighbors, it is likely that LNG consumers will increasingly need to look beyond their traditional Southeast Asian suppliers in the future, perhaps to higher cost LNG schemes outside the region. In Southeast Asia, reduction of the high volumes of associated gas currently flared from the Bombay High Field in India will not only make big contribution to meeting the country's future gas demand, but will also prove environmentally beneficial. Pakistan, in order to control its developing gas markets, has raised gas prices to consumers substantially, with beneficial effects on supply and demand. In Bangladesh, economic pricing has been important in allocating gas resources efficiently. At both the regional and global level, the link between gas use and the environment is becoming stronger, raising the question of relating gas and energy prices to environmental costs and benefits

Canadian natural gas market dynamics and pricing : an update

International Nuclear Information System (INIS)

2002-10-01

This energy market assessment (EMA) report discusses natural gas price formation and describes the current functioning of regional gas markets in Canada. This EMA also describes the factors affecting the price of natural gas in Canada and examines natural gas markets on a region-by region basis. It is shown that as part of an integrated North American market, prices of natural gas in Canada reflect supply and demand factors in both Canada and the United States. During the low oil price period of 1997/1998, high demand for natural gas outpaced the supply because of low drilling and production activity by producers. In response to the increased demand and lower levels of supply, the price of natural gas increased significantly in 1999 and 2000. This was followed by a period of market adjustment. The importance of electronic trading systems for enhancing price discovery was also discussed with reference to how spot and futures markets allow market participants to manage price volatility. It was determined that Canadians have had access to natural gas on terms and conditions equal to export customers, and at equal pricing. In early November 2000, natural gas prices in North American began to rise due to low levels of natural gas in storage. The price shocks were felt unevenly across the North American market. In response to the high prices, consumers conserved energy use, and many industrial users switched to cheaper fuels. By the spring 2001, demand continued to decrease at a time when production was high. These factors contributed to the downward pressure on gas prices. This EMA discusses the structure of market transactions and market adjustment mechanisms. It is presented in the context of the approaching 2002/2003 winter season where the tightening between natural gas supply and demand is expected to result in price volatility. 28 figs

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

Price impact on Russian gas production and export

International Nuclear Information System (INIS)

Kononov, Y.D.

2003-01-01

The paper examines the prospects for Russian gas output and export under different price development. Growth of gas production and transportation costs, following an increase of gas export and production, is estimated. An attempt is made to determine the relation of efficient (from the point of view of gas companies) gas export volumes to prices on external energy markets. The paper presents a quantitative estimate of the possible impact of domestic gas price policy on gas output in Western Siberia. (author)

Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost

International Nuclear Information System (INIS)

Kim, Sung Ki; Ko, Won Il; Nam, Hyoon; Kim, Chul Min; Chung, Yang Hon; Bang, Sung Sig

2017-01-01

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

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.

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

Gas pricing in Europe. Pt. 1. Wholesale markets

International Nuclear Information System (INIS)

Donath, R.

1996-01-01

The article investigates gas pricing in the European procurement market and the wholesale markets of the most important EU consumer markets. It demonstrates that value-oriented pricing principles override cost-oriented pricing principles. For one thing, and independently of pricing principles, two- or three-part demand price systems or basic price systems are common. For another, the frequently encountered opportunities for the differentiation of prices show that as long as there is merely substitution competition instead of direct competition, gas suppliers have a certain degree of freedom in fixing their prices. By contrast, the introduction of direct competition in Great Britain has reduced suppliers' individual price fixing margins, because short-term supply and demand variations in the now created spot market are decisive for gas pricing. (orig.) [de

Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

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.

Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices

International Nuclear Information System (INIS)

Atil, Ahmed; Lahiani, Amine; Nguyen, Duc Khuong

2014-01-01

In this article, we use the recently developed nonlinear autoregressive distributed lags (NARDL) model to examine the pass-through of crude oil prices into gasoline and natural gas prices. Our approach allows us to simultaneously test the short- and long-run nonlinearities through positive and negative partial sum decompositions of the predetermined explanatory variables. It also offers the possibility to quantify the respective responses of gasoline and natural gas prices to positive and negative oil price shocks from the asymmetric dynamic multipliers. The obtained results indicate that oil prices affect gasoline prices and natural gas prices in an asymmetric and nonlinear manner, but the price transmission mechanism is not the same. Important policy implications can be learned from the empirical findings. - Highlights: • The pass-through of crude oil prices into gasoline and natural gas prices is examined. • We use a NARDL model to test for the long-run and short-run asymmetric reactions. • Both gasoline and natural gas prices significantly adjust to changes in the price of oil. • Negative oil shocks have greater effects than positive oil shocks. • Policy implications are discussed

Deterministic Echo State Networks Based Stock Price Forecasting

Directory of Open Access Journals (Sweden)

Jingpei Dan

2014-01-01

Full Text Available Echo state networks (ESNs, as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500 demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

Natural gas market review 2008 - optimising investments and ensuring security in a high-priced environment

Energy Technology Data Exchange (ETDEWEB)

NONE

2008-09-18

Over the last 18 months, natural gas prices have continued to rise steadily in all IEA markets. What are the causes of this steady upward trend? Unprecedented oil and coal prices which have encouraged power generators to switch to gas, together with tight supplies, demand for gas in new markets and delayed investments all played a role. Investment uncertainties, cost increases and delays remain major concerns in most gas markets and are continuing to constitute a threat to long-term security of supply. A massive expansion in LNG production is expected in the short term to 2012, but the lag in LNG investment beyond 2012 is a concern for all gas users in both IEA and non-IEA markets. Despite this tight market context, regional markets continue on their way to globalisation. This tendency seems irreversible, and it impacts even the most independent markets. Price linkages and other interactions between markets are becoming more pronounced. This publication addresses these major developments, assessing investment in natural gas projects (LNG, pipelines, upstream), escalating costs, the activities of international oil and gas companies, and gas demand in the power sector. In addition, the publication includes data and forecasts on OECD and non-OECD regions to 2015 and in-depth reviews of five OECD countries and regions including the European Union. It also provides analysis of 34 non-OECD countries in South America, the Middle East, Africa, and Asia, including a detailed assessment of the outlook for gas in Russia, as well as insights on new technologies to deliver gas to markets.

Integrating climate forecasts and natural gas supply information into a natural gas purchasing decision

Science.gov (United States)

Changnon, David; Ritsche, Michael; Elyea, Karen; Shelton, Steve; Schramm, Kevin

2000-09-01

This paper illustrates a key lesson related to most uses of long-range climate forecast information, namely that effective weather-related decision-making requires understanding and integration of weather information with other, often complex factors. Northern Illinois University's heating plant manager and staff meteorologist, along with a group of meteorology students, worked together to assess different types of available information that could be used in an autumn natural gas purchasing decision. Weather information assessed included the impact of ENSO events on winters in northern Illinois and the Climate Prediction Center's (CPC) long-range climate outlooks. Non-weather factors, such as the cost and available supplies of natural gas prior to the heating season, contribute to the complexity of the natural gas purchase decision. A decision tree was developed and it incorporated three parts: (a) natural gas supply levels, (b) the CPC long-lead climate outlooks for the region, and (c) an ENSO model developed for DeKalb. The results were used to decide in autumn whether to lock in a price or ride the market each winter. The decision tree was tested for the period 1995-99, and returned a cost-effective decision in three of the four winters.

Natural gas prices and the end of gradual change

International Nuclear Information System (INIS)

Osten, J.A.

1998-01-01

Natural gas price predictions for the years 1998, 1999-2001, 2000-2005 are provided. In general, prices are predicted to decrease with increase in storage. Some other factors that will influence the price of natural gas and, therefore, should receive consideration in price predictions, include growth in demand, natural gas production, deliverability, new pipelines, and the Alberta price basis. tabs., figs

Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

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

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)

Recent trends in gas pricing in economies in transition

International Nuclear Information System (INIS)

Cornot-Gandolplhe, S.

1996-01-01

This paper deals with end-user gas price movements in economies in transition since 1990 and with present problems associated with rising of gas prices levels. The first part stresses the major discrepancies existing between countries in transition with regard to their economic situation and their gas market. Historical gas price movements are shown in the second part, which analyzes the main trends observed in economies in transition and problems encountered when raising the gas prices

Gas demand forecasting by a new artificial intelligent algorithm

Science.gov (United States)

Khatibi. B, Vahid; Khatibi, Elham

2012-01-01

Energy demand forecasting is a key issue for consumers and generators in all energy markets in the world. This paper presents a new forecasting algorithm for daily gas demand prediction. This algorithm combines a wavelet transform and forecasting models such as multi-layer perceptron (MLP), linear regression or GARCH. The proposed method is applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the proposed method.

Oil prices. Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

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)

Oil prices. Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

Energy Technology Data Exchange (ETDEWEB)

Meade, Nigel [Imperial College, Business School London (United Kingdom)

2010-11-15

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)

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

Price (slump) forecast : the potential impact on pipelines, producers and marketers

International Nuclear Information System (INIS)

Duncan, J.

2002-01-01

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 evolution of gas price: gas assessment and perspectives; The evolution of gas price on the American, Asian and European markets; Assessment of the organised gas market; Assessment of gas market opening; Gas price: the point of view of consumers and providers; Tariff, the formula which cannot be found: a new stage in an endless history; The diversity of the world gas industry: the Mediterranean situation on prices

International Nuclear Information System (INIS)

Malherbe, Herve; Corbeau, Anne-Sophie; Lu, Long; Maire, Jacques; Verdier, Catherine; Bros, Thierry; Nyouki, Evariste; Astruc, Pierre; Katz, Richard; Jamme, Dominique; Rosier, Philippe; Salanson, Damien; Saniez, Thierry; Moraleda, Pedro; Le Gourrierec, M.

2012-01-01

After a brief introduction, this document contains the various contributions and interventions during round tables dealing with the evolution of gas price on American, Asian and European markets, an assessment of the organised gas market (model, references, members, and so on), an assessment of market gas opening, the point of view of consumers and providers on gas price. Then three articles address the issue of gas pricing in France, the developments of gas industry in the world (consumptions, production, perspectives for LNG) and the Mediterranean situation with respect to gas prices (trends and challenges)

The rationality of EIA forecasts under symmetric and asymmetric loss

International Nuclear Information System (INIS)

Auffhammer, Maximilian

2007-01-01

The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)

The rationality of EIA forecasts under symmetric and asymmetric loss

Energy Technology Data Exchange (ETDEWEB)

Auffhammer, Maximilian [Department of Agricultural and Resource Economics, University of California, 207 Giannini Hall 3310, Berkeley, CA 94720 (United States)

2007-05-15

The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)

Natural gas demand forecast system based on the application of artificial neural networks

International Nuclear Information System (INIS)

Sanfeliu, J.M.; Doumanian, J.E.

1997-01-01

Gas Natural BAN, as a distribution gas company since 1993 in the north and west area of Buenos Aires Argentina, with 1,000,000 customers, had to develop a gas demand forecast system which should comply with the following basic requirements: Be able to do reliable forecasts with short historical information (2 years); Distinguish demands in areas of different characteristics, i.e. mainly residential, mainly industrial; Self-learning capability. To accomplish above goals, Gas Natural BAN chose in view of its own necessities, an artificial intelligence application (neural networks). 'SANDRA', the gas demand forecast system for gas distribution used by Gas Natural BAN, has the following features: Daily gas demand forecast, Hourly gas demand forecast and Breakdown of both forecast for each of the 3 basic zones in which the distribution area of Gas Natural BAN is divided. (au)

Fuel switching? Demand destruction? Gas market responses to price spikes

International Nuclear Information System (INIS)

Lippe, D.

2004-01-01

This presentation defined fuel switching and addressed the issue regarding which consumers have the capability to switch fuels. In response to short term price aberrations, consumers with fuel switching capabilities reduce their use of one fuel and increase consumption of an alternative fuel. For example, natural gas consumption by some consumers declines in response to price spikes relative to prices of alternative fuels. This presentation also addressed the issue of differentiating between fuel switching and demand destruction. It also demonstrated how to compare gas prices versus alternative fuel prices and how to determine when consumers will likely switch fuels. Price spikes have implications for long term trends in natural gas demand, supply/demand balances and prices. The power generating sector represents a particular class of gas consumers that reduce operating rates of gas fired plants and increase operating rates of other plants. Some gas consumers even shut down plants until gas prices declines and relative economies improve. Some practical considerations for fuel switching include storage tank capacity, domestic refinery production, winter heating season, and decline in working gas storage. tabs., figs

Transparency in natural gas prices in Western Europe

International Nuclear Information System (INIS)

Vrieling, E.B.; Munksgaard, J.; Hopper, R.J.

1989-11-01

The present situation on price transparency in Western Europe and North america within the context of the European internal gas market is analyzed. In chapter one the ideas and policy proposals put forward by the European Commission are discussed. Special attention is paid to the situation of the large industrial consumers. It is argued that price transparency needs to be extended to more upstream price aspects. This includes information on city-gates prices, transmission and handling charges in addition to wellhead and import prices. In Western Europe (chapter two) two pricing principles can be distinguished at the final consumer level: pricing according to costs and prices according to market value. The first principle is applied in France, Belgium, the United Kingdom and Austria, as some cost elements are included in the tariff calculations in Italy. Countries where a market-evaluation methodology is applied are Denmark, the Netherlands, Germany, Spain and Switzerland. In North America (chapter three) price transparency is extensive and part of the necessary conditions of an open access contract carriage market. In order to integrate the aspect of price transparency in the broader framework of the internal gas market a model of an integrated natural gas market is described in chapter four. The model specifies the preconditions of a truly integrated gas market, i.e. accessible market entry at all levels of the gas sector and for all market players, equal market opportunity and a regulatory oversight system. A brief comparison between the model and the actual market situation in Western Europe showed that hardly any of these preconditions are met. The comparison points out which actions need to be taken to implement an internal gas market in Western Europe. 9 appendices

Deregulation, market structure and gas prices in the Canadian Natural Gas Industry

International Nuclear Information System (INIS)

Uhler, R.S.

1992-01-01

During the course of the development of the natural gas industry in Canada, gas purchase and sales markets have evolved from being relatively free of regulation to being highly regulated and back again. Though pipeline transport charges were regulated, the pipeline companies, or their subsidiaries, owned the gas that they transported and price and other provisions of purchase and sales contracts were freely negotiated with the producers at one end and distributing utilities or industrial users at the other end. The Western Accord of 1985 set the process of deregulation of the Canadian natural gas industry in motion. On November 1, 1986, natural gas prices in interprovincial trade were deregulated in that domestic natural gas prices were to be freely negotiated. Although not stated explicitly, government policy is to permit export prices to be freely negotiated so long as they do not fall below domestic prices. The deregulation process has dramatically changed the relationship between buyers and sellers. Of particular importance is that deregulation has permitted companies to negotiate gas purchase contracts directly with producers with the pipeline company acting solely as a gas transporter. The purpose of this paper is to examine the forces that have led to shorter term contracts and to examine the likely effect of these contract terms on reservoir development investment incentives. 5 refs., 3 figs

Rising natural gas prices : impacts on U.S. industries

International Nuclear Information System (INIS)

Henry, D.

2005-01-01

The impact of rising natural gas prices on the United States economy and domestic industries was examined in this PowerPoint presentation. Industry comments were solicited on the effects of natural gas prices on their business performance from 2000 to 2004 in order to collect data, and macroeconomic impacts were determined through the use of an inter-industry model. Results of the survey and subsequent model suggested that in 2000 and 2001, real gross domestic product (GDP) growth was depressed by 0.2 per cent because of higher natural gas prices. Between 2000 and 2004, the civilian workforce was lower by 489,000 jobs. It was determined that nitrogenous fertilizer manufacturing was the most gas intensive industry. The results indicated that higher natural gas prices were an additional burden on manufacturing industries, and that the economic performance of natural gas intensive industries was poor between 2000-2004. However, it was just as poor between 1997-2000, when gas prices were relatively low and stable. Natural gas intensive industries passed along price increases in their products to their downstream consumers. Despite job losses, wages in natural gas intensive industries were higher and grew faster than in the rest of the manufacturing industry in the 2000-2004 period. Although capital expenditures declined between 2000 to 2004, they declined more rapidly in the 1997-2000 period. There has been no evidence of a decline in international competitiveness of natural gas intensive industries. It was concluded that rising natural gas prices have had a significant impact on the growth of the economy and workforce. tabs., figs

Market value-oriented gas pricing in Germany

International Nuclear Information System (INIS)

Eimermacher, T.

1996-01-01

In Germany, natural gas faces stiff competition from other types of energy. In many applications, natural gas is capable of replacing other fuels. In addition there is a growing gas-to-gas competition in some European countries, either through pipeline construction by a competitor as in Germany or by mandatory third-party access as in UK. Competition leads to market value-oriented energy pricing, which is particularly evident in Germany. For the consumer, this competitive situation ensures that natural gas can be obtained (and remains available in the long term) at competitive prices

The impact of high oil prices on natural gas

International Nuclear Information System (INIS)

Koevoet, H.

2003-01-01

The principle of gas-to-oil (oil prices determine the price of natural gas) in the Netherlands and several other developments elsewhere (war in Iraq and a cold winter in the USA) has caused high natural gas prices. The question is whether the liberalization of the energy market can change this principle [nl

Entity’s Irregular Demand Scheduling of the Wholesale Electricity Market based on the Forecast of Hourly Price Ratios

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

The Optimal Confidence Intervals for Agricultural Products’ Price Forecasts Based on Hierarchical Historical Errors

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.

Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model

International Nuclear Information System (INIS)

Unsihuay-Vila, C.; Zambroni de Souza, A.C.; Marangon-Lima, J.W.; Balestrassi, P.P.

2010-01-01

This paper proposes a new hybrid approach based on nonlinear chaotic dynamics and evolutionary strategy to forecast electricity loads and prices. The main idea is to develop a new training or identification stage in a nonlinear chaotic dynamic based predictor. In the training stage five optimal parameters for a chaotic based predictor are searched through an optimization model based on evolutionary strategy. The objective function of the optimization model is the mismatch minimization between the multi-step-ahead forecasting of predictor and observed data such as it is done in identification problems. The first contribution of this paper is that the proposed approach is capable of capturing the complex dynamic of demand and price time series considered resulting in a more accuracy forecasting. The second contribution is that the proposed approach run on-line manner, i.e. the optimal set of parameters and prediction is executed automatically which can be used to prediction in real-time, it is an advantage in comparison with other models, where the choice of their input parameters are carried out off-line, following qualitative/experience-based recipes. A case study of load and price forecasting is presented using data from New England, Alberta, and Spain. A comparison with other methods such as autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) is shown. The results show that the proposed approach provides a more accurate and effective forecasting than ARIMA and ANN methods. (author)

Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets

OpenAIRE

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...

Another look on the relationships between oil prices and energy prices

International Nuclear Information System (INIS)

Lahiani, Amine; Miloudi, Anthony; Benkraiem, Ramzi; Shahbaz, Muhammad

2017-01-01

This paper employs the Quantile Autoregressive Distributed Lags (QARDL) model developed recently by Cho et al. (2015) to investigate the pass-through of oil prices to a set of energy prices. This approach allows analyzing simultaneously short-term connections and long-run cointegrating relationships across a range of quantiles. It also provides insights on the short-run predictive power of oil prices in predicting energy prices while accounting for the cointegration between oil prices and each of the considered energy prices in low, medium and high quantiles. Two key findings emerge from this paper. First, all considered energy prices are shown to be cointegrated with oil price across quantiles meaning that a stationaryequilibriumrelationship exists between single energy price and oil price. Second, we find evidence that oil price is a significant predictor of individual petroleum products prices and natural gas in the short run. This paper has important policy implications for forecasters, energy policy-makers and portfolio managers. - Highlights: • The pass-through of oil prices to a set of energy prices is investigated for US economy. • All considered energy prices are shown to be cointegrated with oil price across quantiles. • Oil price is a significant predictor of individual petroleum products prices in the short run. • Oil price also predicts natural gas prices in the short run.

Henry Hub and national balancing point prices: what will be the international gas price reference?

International Nuclear Information System (INIS)

Mazighi, A.E.H.

2005-01-01

One of the lessons in the history of international trade in commodities is the emergence - sooner or later - of an international price reference, most commonly known as an international marker price. In the area of oil, West Texas Intermediate (WTI) plays the role of a marker for sour crudes traded in the Atlantic basin. Brent oil fulfils this function for sweet crudes traded in Europe. Another important aspect in the area of global commodities is that the emergence of a marker price is not always necessarily related to the relative share of production of exports of the commodity, but primarily to the existence of an organized market for this commodity. Today, while international gas trade is intensifying, we still lack an international price reference for this commodity. This is due to the fact that the international trade of natural gas is still highly regionalized. It is also due to the fact that most gas markets are still regulated. Nevertheless, deregulation efforts have been implemented in both developed (the United States, the United Kingdom, continental Europe, Korea) and developing countries (Brazil, Chile) and have led to new market structures based on more competition in all segments of the gas chain, except transportation. In the meantime, price structures based on supply and demand principles are supposed to have emerged in the US and UK markets in the 1990s as a result of the implementation of deregulation measures. Today, the US gas market, which represents more than 660 billion cubic metres per year of consumption and the UK gas market, which is close to 100 bcm annually, are considered mature enough to make the principles of supply and demand operate inside these markets. In fact, the Henry Hub (HH) price, which is determined at a physical location in Louisiana, US, and the national balancing point (NBP) price, which is determined somewhere inside the national transmission system (NTS), without any precise location, are considered as potential

Strategies for Ontario gas marketers: making cents of it all

Energy Technology Data Exchange (ETDEWEB)

Walsh, P.R. [Energy Objective Ltd., London ON (Canada)

2001-07-01

The evolution of the natural gas supply, demand and pricing equilibrium is discussed. Prices for natural gas are a function of supply and demand. An analysis of supply and demand forecasting shows a consistent trend to mirror the actual outcome. In the case of price, the forecasts are less consistent and indicate the need for a different approach for choosing an appropriate strategy for price determination. Analysis of price data over the past 30 years indicate that increases in natural gas prices in North America have been strongly correlated with increase in economic growth. Increased economic growth creates an increase in energy demand and a related increase in energy prices. Conversely, decreased economic activity tends to coincide with energy price spikes and subsequent declines in energy prices. These results lead to the conclusion that a successful strategy for long term pricing requires the natural gas marketer to establish the point at which the current price is, relative to the long-term equilibrium between demand, supply and pricing. 12 figs.

Strategies for Ontario gas marketers: making cents of it all

International Nuclear Information System (INIS)

Walsh, P.R.

2001-01-01

The evolution of the natural gas supply, demand and pricing equilibrium is discussed. Prices for natural gas are a function of supply and demand. An analysis of supply and demand forecasting shows a consistent trend to mirror the actual outcome. In the case of price, the forecasts are less consistent and indicate the need for a different approach for choosing an appropriate strategy for price determination. Analysis of price data over the past 30 years indicate that increases in natural gas prices in North America have been strongly correlated with increase in economic growth. Increased economic growth creates an increase in energy demand and a related increase in energy prices. Conversely, decreased economic activity tends to coincide with energy price spikes and subsequent declines in energy prices. These results lead to the conclusion that a successful strategy for long term pricing requires the natural gas marketer to establish the point at which the current price is, relative to the long-term equilibrium between demand, supply and pricing. 12 figs

Forecasting oil price movements with crack spread futures

International Nuclear Information System (INIS)

Murat, Atilim; Tokat, Ekin

2009-01-01

In oil markets, the crack spread refers to the crude-product price relationship. Refiners are major participants in oil markets and they are primarily exposed to the crack spread. In other words, refiner activity is substantially driven by the objective of protecting the crack spread. Moreover, oil consumers are active participants in the oil hedging market and they are frequently exposed to the crack spread. From another perspective, hedge funds are heavily using crack spread to speculate in oil markets. Based on the high volume of crack spread futures trading in oil markets, the question we want to raise is whether the crack spread futures can be a good predictor of oil price movements. We investigated first whether there is a causal relationship between the crack spread futures and the spot oil markets in a vector error correction framework. We found the causal impact of crack spread futures on spot oil market both in the long- and the short-run after April 2003 where we detected a structural break in the model. To examine the forecasting performance, we use the random walk model (RWM) as a benchmark, and we also evaluate the forecasting power of crack spread futures against the crude oil futures. The results showed that (a) both the crack spread futures and the crude oil futures outperformed the RWM; and (b) the crack spread futures are almost as good as the crude oil futures in predicting the movements in spot oil markets. (author)

Natural gas pricing reform in China: Getting closer to a market system?

International Nuclear Information System (INIS)

Paltsev, Sergey; Zhang, Danwei

2015-01-01

Recent policy in China targets an increase in the contribution of natural gas to the nation's energy supply. Historically, China's natural gas prices have been highly regulated with a goal to protect consumers. The old pricing regime failed to provide enough incentives for natural gas suppliers, which often resulted in natural gas shortage. A new gas pricing reform was tested in Guangdong and Guangxi provinces in 2011, and introduced nationwide in 2013. The reform is aimed at creating a more market-based pricing mechanism. We show that a substantial progress toward a better predictability and transparency of prices has been made. The prices are now more connected with the international fuel oil and liquid petroleum gas prices. The government's approach for a temporary two-tier pricing when some volumes are still traded at old prices reduced a potential opposition during the new regime implementation. Some limitations of the natural gas pricing remain as it created biased incentives for producers and favors large natural gas suppliers. The pricing reform at its current stage falls short of establishing a complete market mechanism driven by an interaction of supply and demand of natural gas in China. - Highlights: • China's reform of natural gas pricing is in effect nationwide from 2013. • Prices are now connected to international fuel oil and liquid petroleum gas prices. • The reform benefits domestic producers and importers of natural gas. • There are still price distortions between industrial and residential sector. • The reform needs to create a system where both supply and demand are considered.

Pricing Natural Gas. The Outlook for the European Market (Summary)

International Nuclear Information System (INIS)

2008-01-01

Long-term gas supply contracts contain price formulae, in which the gas price is usually linked to the price of another commodity, or to the spot price of gas in a particular market. In continental Europe the gas price in international long-term supply contracts is predominantly linked to oil products. At the same time, spot markets for gas in which gas prices are determined by supply and demand are developing in various EU markets. This paper addresses the question of to what extent the traditional form of oil-based price indexation is sustainable and/or will be sustained by the market players. It discusses the considerations the market players may have in favour of one or the other form of indexation, the external forces that may influence the choice of indexation in the short and longer terms and the consequences of change. It argues that pricing systems are a fundamental part of a market organisation, and that a shift to different pricing structures only happens if and when the main actors are convinced that they understand and accept the consequences of such change. It concludes that there is no strong evidence that the current hybrid situation, in which both forms of gas pricing co-exist, cannot continue. There are also no overriding reasons to intervene in the market practices of price formation. Both systems have their advantages and disadvantages under different market conditions, and to some extent complement each other in the current markets. Different types of risk and the appreciation thereof by the trading parties will determine particular choices of pricing rules and contracting conditions. More importantly, in today's market, in which new supplies are slow to come forward, the choice should be left to the market parties, particularly as sellers and buyers do not seem to be in strong disagreement

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

Directory of Open Access Journals (Sweden)

Ping-Huan Kuo

2018-04-01

Full Text Available Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN and the Long Short Term Memory (LSTM. In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE and Root-Mean-Square error (RMSE evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

Gas pricing in developing countries: A case study of Pakistan

International Nuclear Information System (INIS)

Sohail, H.M.; Abid, M.S.; Ansari, A.M.

1994-01-01

Pakistan, a developing country, has gone through various phases of formulating gas pricing policies during its 40-year history of natural gas production and consumption. This paper identifies critical factors that influenced gas pricing policies in Pakistan and adverse effects experienced when any of these factors was not given proper consideration. For instance, on the producer's side, discounted pricing formulas discouraged further exploration and development, leaving high-potential areas unexplored and discovered fields dormant for more than a decade. On the consumer's side, subsidized gas prices encouraged consumption to rise steeply without new discoveries to offset additional surplus consumption. The paper also discusses various short- and long-term variables that should go into a gas pricing policy for developing countries. References to recent policies are also given, indicating how these variables were incorporated in real terms. The conclusions and recommendations, based on Pakistan's long experience with the gas industry, should be useful for other oil-importing countries rich in indigenous gas resources

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)

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

Science.gov (United States)

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

Science.gov (United States)

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

Directory of Open Access Journals (Sweden)

Ernest Kissi

2018-03-01

Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.

Helping consumers manage their exposure to volatile natural gas prices

International Nuclear Information System (INIS)

Campion, A.

2004-01-01

This presentation provided a customer's view of forward gas prices and outlined different buying behaviours in terms of characteristics of novice and seasoned buyers. It presented a portfolio overview of natural gas and described the risks facing customers in terms of fixed prices and fixed volumes. An energy smart price plan considers floating gas prices instead of a fixed market price. An automobile manufacturer was presented as an example of a gas consumer that would prefer to manage internal costs of production rather than manage gas volatility. The importance of understanding the drivers of individual businesses was emphasized. Natural Resources Canada and the Office of Energy Efficiency offer financial incentives for manufacturers for energy retrofit feasibility studies that result in energy retrofit projects in lighting, heating, boiler replacement, chiller upgrades, and heat recovery. tabs., figs

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

Science.gov (United States)

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.

Market data analysis and short-term price forecasting in the Iran electricity market with pay-as-bid payment mechanism

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)

Target Price Accuracy

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.

The world economy: Its impact on the gas processing industry

International Nuclear Information System (INIS)

Teleki, A.

1994-01-01

Gas processors are in the business of extracting C 2-7 hydrocarbons from natural gas streams and converting them to commercial grade gas liquids, valued at or slightly below oil product prices. If the margin of oil prices over gas prices is higher, the gas processing business is more profitable. An approximate index of profitability is the ratio of the price of a bbl of oil divided by the price of a million Btu of gas (the oil-gas ratio). Since the mid-1980s, by which time both the oil and gas markets had been largely deregulated, the oil-gas ratio fluctuated in the 10-12 range then peaked to over 15 in 1990-91. The recent fall in oil prices has driven the ratio to historically low levels of 6-7, which leads to gas processors curtailing ethane recovery. Various aspects of the world economy and the growth of oil consumption are discussed to forecast their effect on gas processors. It is expected that oil demand should grow at least 4% annually over 1994-98, due to factors including world economic growth and low energy prices. Oil prices are forecast to bottom out in late 1995 and rise thereafter to the mid-20 dollar range by the end of the 1990s. A close supply-demand balance could send short-term prices much higher. Some widening of the gas-oil ratio should occur, providing room for domestic natural gas prices to rise, but with a lag. 8 figs

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Directory of Open Access Journals (Sweden)

Mustafa Akpinar

2016-09-01

Full Text Available Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey’s natural gas consumption increased as well in parallel with the world‘s over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey’s Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA methods. Here, 2011–2014 monthly data were prepared and divided into two series. The first series is 2011–2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently

Modelling the Errors of EIA’s Oil Prices and Production Forecasts by the Grey Markov Model

Directory of Open Access Journals (Sweden)

Gholam Hossein Hasantash

2012-01-01

Full Text Available Grey theory is about systematic analysis of limited information. The Grey-Markov model can improve the accuracy of forecast range in the random fluctuating data sequence. In this paper, we employed this model in energy system. The average errors of Energy Information Administrations predictions for world oil price and domestic crude oil production from 1982 to 2007 and from 1985 to 2008 respectively were used as two forecasted examples. We showed that the proposed Grey-Markov model can improve the forecast accuracy of original Grey forecast model.

An overview of alternative fossil fuel price and carbon regulation scenarios

Energy Technology Data Exchange (ETDEWEB)

Wiser, Ryan; Bolinger, Mark

2004-10-01

The benefits of the Department of Energy's research and development (R&D) efforts have historically been estimated under business-as-usual market and policy conditions. In recognition of the insurance value of R&D, however, the Office of Energy Efficiency and Renewable Energy (EERE) and the Office of Fossil Energy (FE) have been exploring options for evaluating the benefits of their R&D programs under an array of alternative futures. More specifically, an FE-EERE Scenarios Working Group (the Working Group) has proposed to EERE and FE staff the application of an initial set of three scenarios for use in the Working Group's upcoming analyses: (1) a Reference Case Scenario, (2) a High Fuel Price Scenario, which includes heightened natural gas and oil prices, and (3) a Carbon Cap-and-Trade Scenario. The immediate goal is to use these scenarios to conduct a pilot analysis of the benefits of EERE and FE R&D efforts. In this report, the two alternative scenarios being considered by EERE and FE staff--carbon cap-and-trade and high fuel prices--are compared to other scenarios used by energy analysts and utility planners. The report also briefly evaluates the past accuracy of fossil fuel price forecasts. We find that the natural gas prices through 2025 proposed in the FE-EERE Scenarios Working Group's High Fuel Price Scenario appear to be reasonable based on current natural gas prices and other externally generated gas price forecasts and scenarios. If anything, an even more extreme gas price scenario might be considered. The price escalation from 2025 to 2050 within the proposed High Fuel Price Scenario is harder to evaluate, primarily because few existing forecasts or scenarios extend beyond 2025, but, at first blush, it also appears reasonable. Similarly, we find that the oil prices originally proposed by the Working Group in the High Fuel Price Scenario appear to be reasonable, if not conservative, based on: (1) the current forward market for oil, (2

Russian natural gas exports-Will Russian gas price reforms improve the European security of supply?

International Nuclear Information System (INIS)

Sagen, Eirik Lund; Tsygankova, Marina

2008-01-01

In this paper we use both theoretical and numerical tools to study potential effects on Russian gas exports from different Russian domestic gas prices and production capacities in 2015. We also investigate whether a fully competitive European gas market may provide incentives for Gazprom, the dominant Russian gas company, to change its export behaviour. Our main findings suggest that both increased domestic gas prices and sufficient production capacities are vital to maintain Gazprom's market share in Europe over the next decade. In fact, Russia may struggle to carry out its current long-term export commitments if domestic prices are sufficiently low. At the same time, if Russian prices approach European net-back levels, Gazprom may reduce exports in favour of a relatively more profitable domestic market

The Value of Renewable Energy as a Hedge Against Fuel Price Risk: Analytic Contributions from Economic and Finance Theory

Energy Technology Data Exchange (ETDEWEB)

Bolinger, Mark A; Wiser, Ryan

2008-09-15

natural gas in the United States over a relatively brief period. Perhaps of most concern is that this dramatic price increase was largely unforeseen. Figure 2 compares the EIA's natural gas wellhead price forecast from each year's Annual Energy Outlook (AEO) going back to 1985 against the average US wellhead price that actually transpired. As shown, our forecasting abilities have proven rather dismal over time, as over-forecasts made in the late 1980's eventually yielded to under-forecasts that have persisted to this day. This historical experience demonstrates that little weight should be placed on any one forecast of future natural gas prices, and that a broad range of future price conditions ought to be considered in planning and investment decisions. Against this backdrop of high, volatile, and unpredictable natural gas prices, increasing the market penetration of renewable generation such as wind, solar, and geothermal power may provide economic benefits to ratepayers by displacing gas-fired generation. These benefits may manifest themselves in several ways. First, the displacement of natural gas-fired generation by increased renewable generation reduces ratepayer exposure to natural gas price risk--i.e., the risk that future gas prices (and by extension future electricity prices) may end up markedly different than expected. Second, this displacement reduces demand for natural gas among gas-fired generators, which, all else equal, will put downward pressure on natural gas prices. Lower natural gas prices in turn benefit both electric ratepayers and other end-users of natural gas. Using analytic approaches that build upon, yet differ from, the past work of others, including Awerbuch (1993, 1994, 2003), Kahn and Stoft (1993), and Humphreys and McClain (1998), this chapter explores each of these two potential 'hedging' benefits of renewable electricity. Though we do not seek to judge whether these two specific benefits outweigh any incremental

Natural gas prices in the Maritimes : an energy market assessment

International Nuclear Information System (INIS)

2004-03-01

The National Energy Board monitors the supply and price of natural gas in the Maritimes. This report contains the results and analysis of a survey of the wholesale natural gas prices paid by Canadian buyers in the Maritimes from November 2002 to October 2003. The objective of the report is to improve the understanding of the market factors that influence wholesale natural gas prices in the Maritimes. A comparative evaluation of domestic and export prices shows that Canadian buyers have had access to gas at prices similar to the export market at St. Stephen, New Brunswick. Since the number of participants in the domestic market is low, only four large buyers have a major impact on average prices in the region. The challenge for small buyers will be to buy gas from others who can divert some of their own sales of use. However, these sellers may not want to over-commit to new firm sales in case they have to re-purchase the gas during shortages that may occur due to fluctuations in production or shipping. It was noted that a new gas supply into the region would support many buyers and sellers, and could lead to a more transparent Maritime natural gas market. The National Energy Board is satisfied that the Maritime natural gas market is currently performing as well as can be expected, given its young stage of development. 1 tab., 8 figs., 1 appendix

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Ani Shabri

2014-01-01

Full Text Available Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI, has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

World gas supply-demand

International Nuclear Information System (INIS)

Rushby, I.L.

1996-01-01

The rapid growth in demand for natural gas from a global perspective is documented in this paper. Low prices compared to other fuels and a return to normal winter temperatures is argued to be the cause of this increase in consumption. Natural gas production and prices for 1995 are discussed and forecasts made for future years, in particular the prospects for LNG in Asia. Data on energy growth and gas specific information in world markets are included. (UK)

End user prices in liberalised energy markets

Energy Technology Data Exchange (ETDEWEB)

Lijesen, M.G. [Afdeling Energie en Grondstoffen, Centraal Planbureau CPB, Den Haag (Netherlands)

2002-12-01

As European energy markets move towards deregulation, energy prices shift from classic 'cost plus' prices towards market prices. We develop a model for the retail and wholesale energy markets in Europe, based on Bertrand competition in a two part pricing structure with switching costs. We use the model to forecast end user electricity and natural gas prices and find that the introduction of competition in energy retail and wholesale markets will decrease standing charges, lowering total costs for energy users. A larger number of entrants, a cost advantage for one of the suppliers, or lower switching costs reduces standing charges further.

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)

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.

Deliverability and regional pricing in U.S. natural gas markets

International Nuclear Information System (INIS)

Brown, Stephen P.A.; Yuecel, Mine K.

2008-01-01

During the 1980s and early 90s, interstate natural gas markets in the United States made a transition away from the regulation that characterized the previous three decades. With abundant supplies and plentiful pipeline capacity, a new order emerged in which freer markets and arbitrage closely linked natural gas price movements throughout the country. After the mid-1990s, however, U.S. natural gas markets tightened and some pipelines were pushed to capacity. We look for the pricing effects of limited arbitrage through causality testing between prices at nodes on the U.S. natural gas transportation system and interchange prices at regional nodes on North American electricity grids. Our tests do reveal limited arbitrage, which is indicative of bottlenecks in the U.S. natural gas pipeline system. (author)

Research on forecast technology of mine gas emission based on fuzzy data mining (FDM)

Energy Technology Data Exchange (ETDEWEB)

Xu Chang-kai; Wang Yao-cai; Wang Jun-wei [CUMT, Xuzhou (China). School of Information and Electrical Engineering

2004-07-01

The safe production of coalmine can be further improved by forecasting the quantity of gas emission based on the real-time data and historical data which the gas monitoring system has saved. By making use of the advantages of data warehouse and data mining technology for processing large quantity of redundancy data, the method and its application of forecasting mine gas emission quantity based on FDM were studied. The constructing fuzzy resembling relation and clustering analysis were proposed, which the potential relationship inside the gas emission data may be found. The mode finds model and forecast model were presented, and the detailed approach to realize this forecast was also proposed, which have been applied to forecast the gas emission quantity efficiently.

Ontario gas prices review task force report : fairness at the pump

International Nuclear Information System (INIS)

2000-01-01

Sudden gas price increases hit Ontario consumers in July 1999, and as a result, the Gas Busters Hotline operated by the provincial government received over 4,000 complaints concerning the price of gas. World crude oil prices increased to above 34 American dollars per barrel by March 2000, and there were discrepancies by as much as 10 cents a litre in the price of gas in Ontario, depending on the community where the purchase was made. The Gas Prices Review Task Force was established in November 1999 to assist in the identification of an adequate solution to the rising price of gas. Public participation was sought, as well as input from representatives of consumer groups and industry. The Task Force was also mandated to conduct policy options research to ensure fair prices at the pump, to examine the regulatory or legislative initiatives that would work best for the protection of the consumer, in accordance with the federal Competition Act. A report was submitted to the Minister of Consumer and Commercial Relations. A total of fourteen recommendations were made to the Minister. The recommendations touched topics as varied as tax collection legislation, price monitoring, segmented earnings reports, removal of the Goods and Services Tax (GST). refs., figs

Gas prices: realities and probabilities

International Nuclear Information System (INIS)

Broadfoot, M.

2000-01-01

An assessment of price trends suggests continuing rise in 2001, with some easing of upward price movement in 2002 and 2003. Storage levels as of Nov. 1, 2000 are expected to be at 2.77 Tcf, but if the winter of 2000/2001 proves to be more severe than usual, inventory levels could sink as low as 500 Bcf by April 1, 2001. With increasing demand for natural gas for non-utility electric power generation the major challenge will be to achieve significant supply growth, which means increased developmental drilling and inventory draw-downs, as well as more exploratory drilling in deepwater and frontier regions. Absence of a significant supply response by next summer will affect both growth in demand and in price levels, and the increased demand for electric generation in the summer will create a flatter consumption profile, erasing the traditional summer/winter spread in consumption, further intensifying price volatility. Managing price fluctuations is the second biggest challenge (after potential supply problems) facing the industry

International oil and natural gas demand projections: an econometric model for 2008-2030; Projecao das demandas mundiais de petroleo e de gas natural: aplicacao de um modelo agregado para o periodo 2008-2030

Energy Technology Data Exchange (ETDEWEB)

Machado, Giovani; Aragao, Amanda; Valle, Ricardo Nascimento e Silva do [Empresa de Pesquisa Energetica (EPE), Rio de Janeiro, RJ (Brazil)

2008-07-01

This study forecasts the world oil and gas demands for 2008-2030 by applying econometric formulations. The basic variables are world GDP and Brent price. The forecast assumptions are: sound world economic growth remains, despite falling rates during the period; Brent prices continue high, but in a lower level, in 2006 constant prices, in harmony with Energy Information Administration reference scenario. Findings show that, should assumptions prove to be correct, world oil and gas demands will reach 118 million bbl/d and 5 trillion cubic meters in 2030, respectively. In other words, world oil demand will grow at 1.4% per year, while world gas demand will increase at 2.5% per year. Although such figures are similar to those from other institutions (EIA, IEA and OPEC), structural changes in oil and gas markets, catalyzed by high oil prices and energy and environmental policies, may reduce forecast strength of the specifications proposed. (author)

Forecasting spot electricity prices : Deep learning approaches and empirical comparison of traditional algorithms

NARCIS (Netherlands)

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 gas pricing in Europe and Asia: A crisis of fundamentals

International Nuclear Information System (INIS)

Stern, Jonathan

2014-01-01

In Continental Europe and LNG importing Asia, international gas prices reflect the market fundamentals of the 1970s–1990s when gas was replacing oil products and crude oil in energy balances. By the end of the 2000s, fundamentals in both these regions had changed significantly, but gas price formation mechanisms had not. This created major problems for buyers locked into long term contracts indexed to crude oil and oil product prices, which had risen to levels far above gas market fundamentals. By 2013, the transition to hub-based pricing was well advanced in Europe and dominant in the large markets in the north west of the Continent. In Asia the “crisis of fundamentals” was only just starting to be addressed with a transition to market pricing an urgent imperative, but still a distant prospect. - Highlights: • International gas prices in Europe and LNG importing Asia no longer reflect market fundamentals. • This became highly problematic in Europe post-2008 and in Japan post-Fukushima. • The result has been a significant switch to hub pricing in Europe. • In Asia, no substantial action has been taken beyond some new contracts based on Henry Hub prices

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

Science.gov (United States)

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.

Forecasting risks of natural gas consumption in Slovenia

Energy Technology Data Exchange (ETDEWEB)

Potocnik, Primoz; Govekar, Edvard; Grabec, Igor [Laboratory of Synergetics, Ljubljana (Slovenia). Faculty of Mechanical Engineering; Thaler, Marko; Poredos, Alojz [Laboratory for Refrigeration, Ljubljana (Slovenia). Faculty of Mechanical Engineering

2007-08-15

Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company. (author)

Forecasting risks of natural gas consumption in Slovenia

International Nuclear Information System (INIS)

Potocnik, Primoz; Thaler, Marko; Govekar, Edvard; Grabec, Igor; Poredos, Alojz

2007-01-01

Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company

Gas and electricity price in the European Union in 2011

International Nuclear Information System (INIS)

Martin, Jean-Philippe

2012-11-01

This document indicates and comments the evolution of gas and electricity prices in the different countries of the European Union. As far as natural gas is concerned, it outlines that taxes on gas are higher in Nordic countries, and that prices are increasing everywhere (for industry as well as for households). As far as electricity is concerned, price is rather cheap in France compared to the other countries. Graphs indicate the evolution of electricity prices between 2010 and 2011 in the different countries for industry and households. Even if a decrease has been noticed in some countries, the general trend is to an increase (between 5 and 10% in average)

Gas deliverability forecasting - why bother?

International Nuclear Information System (INIS)

Trick, M.

1996-01-01

According to the author the answer to the question is an unequivocal 'yes' because gas production forecasting is extremely useful for the management and development of a gas field. To model a gas field, one must take into account reservoir performance, sandface inflow performance, wellbore pressure losses, gathering system pressure losses, and field facility performance. The integration of all these factors in a single computer-based model that incorporates proven technology will facilitate the evaluation of various development strategies. A good computer model can help to predict the most cost effective improvement methods, determine economic viability, estimate how much gas is available, evaluate whether drilling wells or adding compression will produce the most reserves, determine optimum placement of compression, evaluate changes to the gathering system, and determine if production from existing wells can be increased by wellbore modifications

Energy prices, volatility, and the stock market. Evidence from the Eurozone

International Nuclear Information System (INIS)

Oberndorfer, Ulrich

2009-01-01

This paper constitutes a first analysis on stock returns of energy corporations from the Eurozone. It focuses on the relationship between energy market developments and the pricing of European energy stocks. According to our results, oil price hikes negatively impact on stock returns of European utilities. However, they lead to an appreciation of oil and gas stocks. Interestingly, forecastable oil market volatility negatively affects European oil and gas stocks, implying profit opportunities for strategic investors. In contrast, the gas market does not play a role for the pricing of Eurozone energy stocks. Coal price developments affect the stock returns of European utilities. However, this effect is small compared to oil price impacts, although oil is barely used for electricity generation in Europe. This suggests that for the European stock market, the oil price is the main indicator for energy price developments as a whole. (author)

Prognostication : impact on Arctic gas

International Nuclear Information System (INIS)

Duncan, J.

2003-01-01

This PowerPoint presentation featured issues facing the current gas market with reference to the possibility of a supply shortage. The lack of credit-worthy counterparties increases volatility just as dwindling liquidity raises risks. It is expected that, for the most part, Alberta will continue to supply Canadian markets. The author indicated that the Alaska and Mackenzie pipelines need to be constructed. A series of graphs were presented to better illustrate the Mexican exports, gas balance sheet, rig count versus production, etc. Forecasting requires a balance between perception and reality. Demand will depend on weather, and gas prices must go higher. Some of the factors that will impact the market are: war with Iraq, oil prices, North American terrorism, and a sluggish economy. Both the short and long term outlooks are bullish, when it comes to oil prices. The alternative supply sources are: liquefied natural gas (LNG), coalbed methane (CBM), and frontier sources. Each alternative source was examined in detail. One section of the presentation was devoted to master limited partnerships. Risk was discussed, as was technical analysis of forecasting. tabs., figs

Easing the natural gas crisis: Reducing natural gas prices through increased deployment of renewable energy and energy efficiency

Energy Technology Data Exchange (ETDEWEB)

Wiser, Ryan; Bolinger, Mark; St. Clair, Matt

2004-12-21

Heightened natural gas prices have emerged as a key energy-policy challenge for at least the early part of the 21st century. With the recent run-up in gas prices and the expected continuation of volatile and high prices in the near future, a growing number of voices are calling for increased diversification of energy supplies. Proponents of renewable energy and energy efficiency identify these clean energy sources as an important part of the solution. Increased deployment of renewable energy (RE) and energy efficiency (EE) can hedge natural gas price risk in more than one way, but this paper touches on just one potential benefit: displacement of gas-fired electricity generation, which reduces natural gas demand and thus puts downward pressure on gas prices. Many recent modeling studies of increased RE and EE deployment have demonstrated that this ''secondary'' effect of lowering natural gas prices could be significant; as a result, this effect is increasingly cited as justification for policies promoting RE and EE. This paper summarizes recent studies that have evaluated the gas-price-reduction effect of RE and EE deployment, analyzes the results of these studies in light of economic theory and other research, reviews the reasonableness of the effect as portrayed in modeling studies, and develops a simple tool that can be used to evaluate the impact of RE and EE on gas prices without relying on a complex national energy model. Key findings are summarized.

Gas industry poised for further growth

International Nuclear Information System (INIS)

Thomas, Victoria

2002-01-01

Despite the current slowdown in the US economy and an expected 4.4% decline in natural gas demand in 2002, the latest long term forecasts from the Energy Information Administration (EIA) of the Department of Energy are still robust. EIA forecasts energy demand will grow by just under a third by 2020 and gas demand by 50% over the next 20 years together with a slight increase in gas price to $3.26 per thousand ft 3 . (UK)

Models of Investor Forecasting Behavior — Experimental Evidence

Directory of Open Access Journals (Sweden)

Federico Bonetto

2017-12-01

Full Text Available Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price bubbles, price crashes, and unpredictable price cycles occur. In this paper, we report the results of behavioral experiments involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast formation. We conducted three experimental sessions with different participants in each session. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past price changes, even when they know the fundamental value of the asset exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. The forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and cycles in dynamical systems models, and price bubbles and cycles ended up occurring in all three sessions.

Weak oil prices seen hindrance to pace of increase in gas use

International Nuclear Information System (INIS)

Anon.

1994-01-01

World demand for gas is expected to rocket, yet future natural gas and liquefied natural gas projects remain threatened by the link of gas prices to crude oil prices. This is the main message that emerged from the 19th World Gas Conference in Milan last week. A number of reports predicted regional demand for gas. All foresaw a rise. International Gas Union (IGU), organizer of the conference, and said world natural gas production has continued to rise despite a significant downturn in industrial production. The paper discusses gas demand in Europe, the correlation between oil and gas prices, the natural gas industry in Indonesia, Russia, and southern Europe

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

The ties between natural gas and oil prices

International Nuclear Information System (INIS)

Maisonnier, G.

2006-01-01

On the European continent, the price of natural gas is still tied directly and to a great extent to the price of competing energies, especially heavy fuel oil and home heating oil. In other words, the gas market is linked to the oil market. Under the effect of deregulation, this model is likely to change in the future, making a shift like that which took place on the American market in the past. (author)

A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

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.

Transatlantic natural gas price and oil price relationships - an empirical analysis

International Nuclear Information System (INIS)

Vasquez Josse, C.I.; Neumann, A.

2006-09-01

Markets for natural gas in industrialized countries have witnessed profound changes in the past two decades. Trade of natural gas at spot markets in North America and Europe expanded and intensified significantly as a direct result of liberalization efforts. We test the relationships of weekly prices for crude oil and natural gas on either side of the Atlantic Basin between 1999 and 2005. Applying co-integration methodology we identify a move toward integration of historically and geographically separated markets for the homogeneous commodity natural gas. (authors)

Black-Scholes finite difference modeling in forecasting of call warrant prices in Bursa Malaysia

Science.gov (United States)

Mansor, Nur Jariah; Jaffar, Maheran Mohd

2014-07-01

Call warrant is a type of structured warrant in Bursa Malaysia. It gives the holder the right to buy the underlying share at a specified price within a limited period of time. The issuer of the structured warrants usually uses European style to exercise the call warrant on the maturity date. Warrant is very similar to an option. Usually, practitioners of the financial field use Black-Scholes model to value the option. The Black-Scholes equation is hard to solve analytically. Therefore the finite difference approach is applied to approximate the value of the call warrant prices. The central in time and central in space scheme is produced to approximate the value of the call warrant prices. It allows the warrant holder to forecast the value of the call warrant prices before the expiry date.

Canadian natural gas liquids : market outlook 2000 - 2010

International Nuclear Information System (INIS)

Gill, L.; Mortensen, P.

2001-01-01

This study provides a comprehensive analysis of the availability of Canadian natural gas liquids. The analysis was developed from production profiles and gas compositions for individual gas pools and takes into account the effects of market factors. On the demand side, the effects of new infrastructure and changes in corporate structures have been evaluated. The study was initiated at a time when energy prices were stable and the major concern was to see how the addition of the Alliance pipeline, the Aux Sable gas processing plant, the Empress V straddle plant and the Nova/UCC E3 ethylene plant would affect the Canadian liquids business. The study was complicated by the advent of unexpected factors affecting the supply and demand of natural gas liquids (NGLs). These included extremely high prices for natural gas, an apparent inability of the supply basin to respond to the high gas prices with increased supply, and the very high electricity costs in Alberta. The weak supply of NGLs coincides with the increase in ethane demand from the start-up of Alberta's fourth ethylene facility and the addition of the high vapour pressure Alliance pipeline. This weak supply suggests there will be an ethane shortage for at least the next few years. The longer term outlook, however, is less certain and will require an analysis of the outlook for gas production, gas composition and NGL extraction capacity. This study developed two forecasts for natural gas prices. Both presume rising gas demand across North America driven by increased gas use for power generation. The Low Case assumes modest growth in domestic Canadian gas demand and the High case predicts strong growth in domestic demand as higher levels of exports to the United States, resulting in a doubling in growth for Canadian gas production from 2000-2015 compared to the Low Case. Both High and Low Case scenarios suggest that prices will decline from current levels so that Alberta plant gate prices fall by 2005 and will then

Canadian natural gas liquids : market outlook 2000 - 2010

Energy Technology Data Exchange (ETDEWEB)

Gill, L.; Mortensen, P.

2001-04-01

This study provides a comprehensive analysis of the availability of Canadian natural gas liquids. The analysis was developed from production profiles and gas compositions for individual gas pools and takes into account the effects of market factors. On the demand side, the effects of new infrastructure and changes in corporate structures have been evaluated. The study was initiated at a time when energy prices were stable and the major concern was to see how the addition of the Alliance pipeline, the Aux Sable gas processing plant, the Empress V straddle plant and the Nova/UCC E3 ethylene plant would affect the Canadian liquids business. The study was complicated by the advent of unexpected factors affecting the supply and demand of natural gas liquids (NGLs). These included extremely high prices for natural gas, an apparent inability of the supply basin to respond to the high gas prices with increased supply, and the very high electricity costs in Alberta. The weak supply of NGLs coincides with the increase in ethane demand from the start-up of Alberta's fourth ethylene facility and the addition of the high vapour pressure Alliance pipeline. This weak supply suggests there will be an ethane shortage for at least the next few years. The longer term outlook, however, is less certain and will require an analysis of the outlook for gas production, gas composition and NGL extraction capacity. This study developed two forecasts for natural gas prices. Both presume rising gas demand across North America driven by increased gas use for power generation. The Low Case assumes modest growth in domestic Canadian gas demand and the High case predicts strong growth in domestic demand as higher levels of exports to the United States, resulting in a doubling in growth for Canadian gas production from 2000-2015 compared to the Low Case. Both High and Low Case scenarios suggest that prices will decline from current levels so that Alberta plant gate prices fall by 2005 and will

Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

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.

Gas prices in the UK: markets and insecurity supply

International Nuclear Information System (INIS)

Wright, P.

2006-01-01

In this article, Professor P. Wright argues that the high and volatile gas price experienced by UK consumers over the last 3 years are the result of the extend of liberalization in the UK - which has made UK prices much more sensitive to insecurities of supply. Businesses pay the cost of this, straightaway, while the strategies which gas companies have used to respond to heightened price risks means domestic consumers also bear the cost of higher supply-markups. The prospect of high levels of demand in bad winters then just adds to price risk and its associated costs. The implication of this analysis is that it is illogical for the UK's regulator and government to blame the UK's high prices on the slow progress of liberalization in the rest of Europe - greater liberalization in Europe might simply replicate the UK's price difficulties throughout Europe

Combined natural gas and electricity network pricing

Energy Technology Data Exchange (ETDEWEB)

Morais, M.S.; Marangon Lima, J.W. [Universidade Federal de Itajuba, Rua Dr. Daniel de Carvalho, no. 296, Passa Quatro, Minas Gerais, CEP 37460-000 (Brazil)

2007-04-15

The introduction of competition to electricity generation and commercialization has been the main focus of many restructuring experiences around the world. The open access to the transmission network and a fair regulated tariff have been the keystones for the development of the electricity market. Parallel to the electricity industry, the natural gas business has great interaction with the electricity market in terms of fuel consumption and energy conversion. Given that the transmission and distribution monopolistic activities are very similar to the natural gas transportation through pipelines, economic regulation related to the natural gas network should be coherent with the transmission counterpart. This paper shows the application of the main wheeling charge methods, such as MW/gas-mile, invested related asset cost (IRAC) and Aumman-Shapley allocation, to both transmission and gas network. Stead-state equations are developed to adequate the various pricing methods. Some examples clarify the results, in terms of investments for thermal generation plants and end consumers, when combined pricing methods are used for transmission and gas networks. The paper also shows that the synergies between gas and electricity industry should be adequately considered, otherwise wrong economic signals are sent to the market players. (author)

A petroleum discovery-rate forecast revisited-The problem of field growth

Science.gov (United States)

Drew, L.J.; Schuenemeyer, J.H.

1992-01-01

A forecast of the future rates of discovery of crude oil and natural gas for the 123,027-km2 Miocene/Pliocene trend in the Gulf of Mexico was made in 1980. This forecast was evaluated in 1988 by comparing two sets of data: (1) the actual versus the forecasted number of fields discovered, and (2) the actual versus the forecasted volumes of crude oil and natural gas discovered with the drilling of 1,820 wildcat wells along the trend between January 1, 1977, and December 31, 1985. The forecast specified that this level of drilling would result in the discovery of 217 fields containing 1.78 billion barrels of oil equivalent; however, 238 fields containing 3.57 billion barrels of oil equivalent were actually discovered. This underestimation is attributed to biases introduced by field growth and, to a lesser degree, the artificially low, pre-1970's price of natural gas that prevented many smaller gas fields from being brought into production at the time of their discovery; most of these fields contained less than 50 billion cubic feet of producible natural gas. ?? 1992 Oxford University Press.

Forecasting of palm oil price in Malaysia using linear and nonlinear methods

Science.gov (United States)

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

Science.gov (United States)

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.

North American natural gas storage, market and price outlook

International Nuclear Information System (INIS)

George, R.

1999-01-01

A series of overhead viewgraphs accompanied this presentation which dealt with the fundamental factors and short-term considerations that will impact Canadian and U.S. natural gas pricing. The short-term pricing outlook and some transportation issues were also highlighted. The major transportation issues for 1999/2000 are: (1) Nova tolling, (2) incentive tolling and negotiations, (3) decontracting, (4) pipeline project schedules, and (5) land use and environmental considerations. The major supply issues are: (1) impact of oil prices on gas drilling and production, (2) impact of merger and acquisition activity, and (3) land use and environmental considerations. The major demand issues for the same time period are: (1) greenhouse gas emissions, (2) electricity restructuring, and (3) new end-use technologies. 3 tabs., 21 figs

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)

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

Canadian natural gas market: dynamics and pricing -- an energy market assessment

International Nuclear Information System (INIS)

2000-11-01

This publication is part of the Energy Market Assessment Program of the National Energy Board. It focuses on identifying factors that affect natural gas prices and describe the current functioning of domestic regional markets in British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec and in the Atlantic provinces.The report emphasizes the growth in demand for natural gas throughout North America, and the aggressive response by producers to the current high price environment with increased drilling programs. The report also predicts a supply and demand adjustment over time, and an accompanying relief in natural gas prices, although the Board is not able to predict with certainty any movements in commodity markets. The Board's findings indicate that domestic users of natural gas paid less than export customers until 1998, at which point the two prices have converged. The end result of the convergence was that Canadians have had access to natural gas under terms and conditions which were no less favourable than those in effect for export customers. The influence of electronic trading systems is reviewed, noting that spot markets and futures markets such as the NYMEX and AECO-C/NIT have had a significant impact on the pricing of natural gas, mostly by allowing market participants to manage price volatility by forward contracting. 1 tab., 42 figs., 1 glossary

Forecasting Day-Ahead Electricity Prices : Utilizing Hourly Prices

NARCIS (Netherlands)

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

OpenAIRE

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 ...

Natural gas prices

International Nuclear Information System (INIS)

Johnson, W.A.

1990-01-01

Since the 1970s, many electric utilities and industrial boiler fuel users have invested in dual fuel use capability which has allowed them to choose between natural gas, residual fuel oil, and in some instances, coal as boiler fuels. The immediate reason for this investment was the need for security of supply. Wellhead regulation of natural gas prices had resulted in shortages during the 1970s. Because many industrial users were given lowest priority in pipeline curtailments, these shortages affected most severely boiler fuel consumption of natural gas. In addition, foreign supply disruptions during the 1970s called into question the ready availability of oil. Many boiler fuel users of oil responded by increasing their ability to diversify to other sources of energy. Even though widespread investment in dual fuel use capability by boiler fuel users was initially motivated by a need for security of supply, perhaps the most important consequence of this investment was greater substitutability between natural gas and resid and a more competitive boiler fuel market. By the early 1980s, most boiler fuel users were able to switch from one fuel to another and often did for savings measured in pennies per MMBtu. Boiler fuel consumption became the marginal use of both natural gas and resid, with coal a looming threat on the horizon to both fuels

The challenge for gas: get price-competitive with coal-fired electricity

International Nuclear Information System (INIS)

Gill, Len

1999-01-01

The challenge for the gas industry is to become price competitive with coal-fired electricity if it wants a larger share of the energy market. Returning to the issue of greater use of gas for electricity generation, the author points out that although electricity prices were rising they were still below the point where gas-fired electricity generation was viable. Copyright (1999) The Australian Gas Journal

European natural gas

International Nuclear Information System (INIS)

Thackeray, Fred

1999-11-01

Contains Executive Summary and Chapters on: Main issues; Natural gas consumption and supply: statistics and key features of individual countries; Sectoral natural gas consumption; Indigenous production; Imports; Prices and taxes; The spot market: The interconnector; Forecasts of production and consumption and contracted imports; Progress of markets liberalisation; Effects of environmentalist developments; Transmission networks and storage; Some principal players. (Author)

An Improved Approach for Forecasting Ecological Impacts from Future Drilling in Unconventional Shale Oil and Gas Plays.

Science.gov (United States)

Wolaver, Brad D; Pierre, Jon Paul; Ikonnikova, Svetlana A; Andrews, John R; McDaid, Guinevere; Ryberg, Wade A; Hibbitts, Toby J; Duran, Charles M; Labay, Benjamin J; LaDuc, Travis J

2018-04-13

Directional well drilling and hydraulic fracturing has enabled energy production from previously inaccessible resources, but caused vegetation conversion and landscape fragmentation, often in relatively undisturbed habitats. We improve forecasts of future ecological impacts from unconventional oil and gas play developments using a new, more spatially-explicit approach. We applied an energy production outlook model, which used geologic and economic data from thousands of wells and three oil price scenarios, to map future drilling patterns and evaluate the spatial distribution of vegetation conversion and habitat impacts. We forecast where future well pad construction may be most intense, illustrating with an example from the Eagle Ford Shale Play of Texas. We also illustrate the ecological utility of this approach using the Spot-tailed Earless Lizard (Holbrookia lacerata) as the focal species, which historically occupied much of the Eagle Ford and awaits a federal decision for possible Endangered Species Act protection. We found that ~17,000-45,500 wells would be drilled 2017‒2045 resulting in vegetation conversion of ~26,485-70,623 ha (0.73-1.96% of pre-development vegetation), depending on price scenario ($40-$80/barrel). Grasslands and row crop habitats were most affected (2.30 and 2.82% areal vegetation reduction). Our approach improves forecasts of where and to what extent future energy development in unconventional plays may change land-use and ecosystem services, enabling natural resource managers to anticipate and direct on-the-ground conservation actions to places where they will most effectively mitigate ecological impacts of well pads and associated infrastructure.

Natural gas consumption and economic growth: Are we ready to natural gas price liberalization in Iran?

International Nuclear Information System (INIS)

Heidari, Hassan; Katircioglu, Salih Turan; Saeidpour, Lesyan

2013-01-01

This paper examines the relationship between natural gas consumption and economic growth in Iran within a multivariate production model. We also investigate the effects of natural gas price on its consumption and economic growth using a demand side model. The paper employs bounds test approach to level relationship over the period of 1972–007. We find evidence of bidirectional positive relationship between natural gas consumption and economic growth in short-run and long-run, based on the production model. The findings also suggest that real GDP growth and natural gas have positive and negative impacts on gross fixed capital formation, respectively. Employment, however, was found to have negative but insignificant impact on gross fixed capital formation. Moreover, the estimation results of demand side model suggest that natural gas price has negative and significant impact on natural gas consumption only in the long-run, though there is insignificant impact on economic growth. These results imply that the Iranian government's decision for natural gas price liberalization has the adverse effects on economic growth and policy makers should be cautious in doing this policy. - Highlights: • Iran has been considered as a major natural gas producer in the world. • This paper examines the relationship between gas consumption and growth in Iran. • Positive impact of gas consumption on growth has been obtained. • The paper finds that gas consumption and income reinforce each other in Iran. • Natural gas price has also negative and significant impact on natural gas consumption in Iran

A Study on the Current Oil and Gas Price Formula and Its Improvement

Energy Technology Data Exchange (ETDEWEB)

Park, Chang Won; Lee, Young Koo [Korea Energy Economics Institute, Euiwang (Korea)

2000-12-01

The object of this study is to suggest some improvements on current price formulas on oil and gas which have been pivotal roles in the process of Korean economic growth. This study first examines basic frames and transition of oil and gas pricing in Korea and then finds some suggestions on them by scrutinizing their theoretical backgrounds. This study finds several problems on oil and gas pricing formulas. (a) In a model that is now studied to evaluate the current domestic oil price, the costs associated with oil security such as oil stockpile are fully penetrated into oil price without their fair evaluations. There is no evaluation principle on the costs occurred in oil supply security. (b) The Rate Of Equity(ROE), a crucial factor in town-gas pricing which is strictly controlled, is directly connected to the average interest rate on saving accounts of domestic commercial banks. Some arguments may have rise about inclusion a risk factor on ROE in order to compensate the uncertainty of town-gas business. (c) New demand for natural gas which is generated by new technologies or machinery and tools can help reduce the costs occurred from seasonal imbalance between power sector and gas sector. So it is also important to decide how to include the beneficiary of cost reduction in town-gas pricing. In order to evaluate the proper price levels, this study tests energy supply security by adopting methodologies such as Herfindahl Index and Portfolio Variance Risk. They can help develop the method to effectively improve the energy security and include the proper energy security costs into energy prices. This study also provides some suggestions for betterment of current ROE decision rule in town-gas business and for improvement of current town-gas policy that government subsidizes newly developed demand for strengthening price competitiveness in the early stage. (author). 145 refs., 16 figs., 49 tabs.

Deriving inflation forecasts from government bond prices

Directory of Open Access Journals (Sweden)

Kožul Nataša

2014-01-01

Full Text Available In financial research and practice, it is widely accepted that nominal interest rates derived from the prices of various financial products of different maturities comprise of corresponding real interest rates and inflation. While extensive research has been conducted on the relationship between these three variables, estimation of their levels is still largely based on the industry surveys and market data. As this information only indicates the current expectations of interest rate and inflation movements over time, a number of caveats should be noted when interpreting such measures. In the US and the UK, where the government bond markets are the largest and most active, a comparative analysis between conventional government bonds and those whose yield is linked to inflation provides a measure of inflation expectations. However, as such analyses implicitly assume that investment in government bonds is virtually risk free, it is questionable whether the derived estimates are of any value in current economic conditions. Moreover, this approach cannot be generalized to other countries, where number of traded products from which any relationship between interest rates and inflation can be determined is limited and different economic conditions prevail. Thus, this paper aims to present an overview of the methodologies used to forecast inflation rates from government bond prices, drawing attention to the key assumptions and limitations of these approaches. The goal is to ascertain their accuracy, and thus their value in determining the real yields of various interest rate-linked products.

Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

CSIR Research Space (South Africa)

Das, Sonali

2010-01-01

Full Text Available and Gertler, 1995), but also because changes in house prices tend to have important wealth effects on consumption (International Monetary Fund, 2000) and investment (Topel and Rosen, 1988), the importance of forecasting house price infl ation is vital, since... sections, respectively, lay out the DFM and outline the basics of the VAR, the Minnesota-type BVARs, and the SBVARs based on the fi rst-order spatial contiguity (FOSC) and the random walk averaging (RWA) priors developed by LeSage and Pan (1995) and Le...

The dynamic linkages between crude oil and natural gas markets

International Nuclear Information System (INIS)

Batten, Jonathan A.; Ciner, Cetin; Lucey, Brian M.

2017-01-01

The time varying price spillovers between natural gas and crude oil markets for the period 1994 to 2014 are investigated. Contrary to earlier research, we show that in a large part of our sample the natural gas price leads the price of crude oil with price spillover effects lasting up to two weeks. This result is robust to a battery of tests including out-of-sample forecasting exercises. However, after 2006, we detect little price dependencies between these two energy commodities. These findings arise due to a conjunction of both demand and supply-side shocks arising from both natural and economic events, including Hurricane Katrina, the Tohoku earthquake and the Global Financial Crisis, as well as infrastructure and technological improvements. The increased use of new technologies such as hydraulic fracking for the extraction of gas and oil in particular affected supply in the latter part of the study. We conclude that the long term relation present in the early part of the sample has decoupled, such that price determination of these two energy sources is now independent. - Highlights: • Contrary to earlier research we find natural gas may lead crude oil prices over a long sample. • This finding holds in forecasting out of sample. • There may be a break in the relationship between oil and gas in 2006. • We suggest that new technologies and financial conditions have led to a decoupling of these markets. • Oil and natural gas prices may now be determined independently.

Equivalent oil price, equivalent gas price and CO2 cost

International Nuclear Information System (INIS)

Bacher, P.

2008-01-01

This article assess the magnitudes of costs to replace oil (and natural gas) in their fixed (heat) or mobile (transport) uses with energy savings or non CO 2 emitting energies. The price of oil (or gas) at which such measures would be profitable at is inferred, without any tax or subsidy, as well as the resulting CO 2 costs avoided. It shows that several of the actions considered in France and Europe to protect the climate are far from being the most economically justified. (author)

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

Gas prices in the UK: markets and insecurity of supply

International Nuclear Information System (INIS)

Wright, P.

2006-01-01

In this article Professor Philip Wright argues that the high and volatile gas prices experienced by UK consumers over the last 3 years are the result of the extent of liberalization in the UK which has made UK prices much more sensitive to insecurities of supply. Businesses pay the cost of this, straightaway, while the strategies which gas companies have used to respond to heightened price risks means domestic consumers also bear the cost of higher supply-markups. The prospect of high levels of demand in bad winters then just adds to price risk and its associated costs. The implication of this analysis is that it is illogical for the UK's regulator and government to blame the UK's high prices on the slow progress of liberalization in the rest of Europe - greater liberalization in Europe might simply replicate the UK's price difficulties throughout Europe. (author)

Renewable energy as a natural gas price hedge: the case of wind

International Nuclear Information System (INIS)

Berry, David

2005-01-01

Electric utilities use natural gas to fuel many of their power plants, especially those plants which provide electricity at peak and intermediate hours. Natural gas prices are highly volatile and have shown a general upward trend. Wind energy can provide a cost-effective hedge against natural gas price volatility or price increases. This conclusion is based on analysis of the costs of marginal conventional generation given the historical probability distribution of natural gas prices, the cost of wind energy, wind integration costs, transmission costs for wind energy, the capacity value of wind, and environmental benefits of wind energy for a hypothetical utility in the Southwestern United States. The efficacy of using wind energy as a hedge at a particular utility will depend on site specific conditions

A GM (1, 1 Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

Directory of Open Access Journals (Sweden)

Ning-bo Zhao

2014-01-01

Full Text Available Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1 Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1 model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this approach, firstly, the GM (1, 1 model is used to forecast the trend by using limited data samples. Then, Markov chain model is integrated into GM (1, 1 model in order to enhance the forecast performance, which can solve the influence of random fluctuation data on forecasting accuracy and achieving an accurate estimate of the nonlinear forecast. As an example, the historical monitoring data of exhaust gas temperature from CFM56 aeroengine of China Southern is used to verify the forecast performance of the GM (1, 1 Markov chain model. The results show that the GM (1, 1 Markov chain model is able to forecast exhaust gas temperature accurately, which can effectively reflect the random fluctuation characteristics of exhaust gas temperature changes over time.

Can deployment of renewable energy put downward pressure on natural gas prices?

International Nuclear Information System (INIS)

Wiser, Ryan; Bolinger, Mark

2007-01-01

High and volatile natural gas prices have increasingly led to calls for investments in renewable energy. One line of argument is that deployment of these resources may lead to reductions in the demand for and price of natural gas. Many recent US-based modeling studies have demonstrated that this effect could provide significant consumer savings. In this article we evaluate these studies, and benchmark their findings against economic theory, other modeling results, and a limited empirical literature. We find that many uncertainties remain regarding the absolute magnitude of this effect, and that the reduction in natural gas prices may not represent an increase in aggregate economic wealth. Nonetheless, we conclude that many of the studies of the impact of renewable energy on natural gas prices appear to have represented this effect within reason, given current knowledge. These studies specifically suggest that a 1% reduction in US natural gas demand could lead to long-term average wellhead price reductions of 0.8-2%, and that each megawatt-hour of renewable energy may benefit natural gas consumers to the tune of at least $7.5-20

Can deployment of renewable energy put downward pressure on natural gas prices?

International Nuclear Information System (INIS)

Wiser, R.; Bolinger, M.

2007-01-01

High and volatile natural gas prices have increasingly led to calls for investments in renewable energy. One line of argument is that deployment of these resources may lead to reductions in the demand for and price of natural gas. Many recent US-based modeling studies have demonstrated that this effect could provide significant consumer savings. In this article we evaluate these studies, and benchmark their findings against economic theory, other modeling results, and a limited empirical literature. We find that many uncertainties remain regarding the absolute magnitude of this effect, and that the reduction in natural gas prices may not represent an increase in aggregate economic wealth. Nonetheless, we conclude that many of the studies of the impact of renewable energy on natural gas prices appear to have represented this effect within reason, given current knowledge. These studies specifically suggest that a 1% reduction in US natural gas demand could lead to long-term average wellhead price reductions of 0.8-2%, and that each megawatt-hour of renewable energy may benefit natural gas consumers to the tune of at least $7.5-20. [Author

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...

The price of oil and the future of Middle East Gas

International Nuclear Information System (INIS)

Zaki Yamani, A.

1997-01-01

Most LNG contracts relate the LNG price received by the supplier at the point of delivery to a relevant oil price. Gas and oil are thus closely connected so that when the price of landed oil decreases so dose the price of delivered LNG. With large fixed transportation and liquefaction costs, accounting for around 85% of the supply cost of delivered LNG in the case of Qatari LNG supplied to japan, you can imagine how large falls in the price paid for delivered LNG would squeeze the net back to the producer back in Qatar. However, low oil price can do some damage to the economics of existing LNG projects in the Middle East. More importantly, persistently low oil prices can prevent new LNG projects from leaving the drawing board-which will stifle the exciting export potential of Middle Eastern gas

Russian gas price reform and the EU-Russia gas relationship: Incentives, consequences and European security of supply

International Nuclear Information System (INIS)

Spanjer, Aldo

2007-01-01

In order to provide a comprehensive picture on the relationship between Russia and the EU, the focus should be on both the external energy relationship as well as Russia's internal organization. This paper sets out to do this by combining both strands of research in order to arrive at recommendations for Europe on the way to adjust its energy policy towards Russia. The emphasis is on whether or not Russia should impose unified gas pricing. Main conclusions are that the perceived advantages of unified Russian gas pricing to Russia as well as Europe are in fact overstated and that EU security of supply might worsen under unified gas prices. Three policy recommendations are that EU policy should (1) more explicitly acknowledge the interdependence between Russia and Europe; (2) not push Russia towards unified gas pricing; and (3) not take for granted any increase in Russian exports flowing to Europe

Main drivers of natural gas prices in the Czech Republic after the market liberalisation

International Nuclear Information System (INIS)

Slabá, Monika; Gapko, Petr; Klimešová, Andrea

2013-01-01

One of the goals of the European Commission in the energy sector is creating a single competitive European market. The decision to liberalise energy markets has far-reaching consequences not only for gas companies, but also for the rest of the real economy in view of the fact that natural gas is being used as an important primary energy source in several sectors of production and in the power industry. We aim to answer how liberalisation/unbundling has influenced gas pricing/prices in the Czech Republic. We investigate the individual components of end-customer gas prices according to the value chain and we define and structure the drivers of these components. We use a case study from the Czech Republic, one of the Central and Eastern European countries, which, contrary to the old Member States, is buying most of its gas from one supplier (high import dependence and low supply diversity) and where the transmission and distribution network is characterised by a sufficient contractual and physical capacity. We stress that next to basic conditions on the European gas market (import dependency on external gas producers) legal and institutional conditions and the initial market structure of each Member State are also important for the results of the liberalisation. - Highlights: ► We deal with gas pricing in the Czech Republic after liberalisation/unbundling. ► The TSO, DSO price components have increased, the SSO price component has decreased. ► Commodity price for Households started to relate to hub prices. ► Commodity price for Corporates remained oil-linked, however discounts were provided. ► Only some Corporates experienced savings in total purchasing costs of gas.

World oil prices flat to declining

International Nuclear Information System (INIS)

Adelman, M.A.

1993-01-01

A forecast is presented of the likely trends in world oil prices over the short to medium term. A historical background is presented of the OPEC cartel and its role in influencing oil prices. The incentives and disincentives for OPEC to raise prices, and the tensions within the cartel are explored. Slower demand growth and the expansion of natural gas are expected to put downward pressure on oil prices, which are currently artificially high. The impacts of high taxes on development and exploration are examined, and it is shown that state ownership poses an obstacle to improved performance. Threats of price decline are expected to continue to lead to threats of hasty, or even violent action on the part of OPEC members, as happened in 1990. Privatization and tax codes designed to skim rent are positive trends

Natural-gas futures: Bias, predictive performance, and the theory of storage

International Nuclear Information System (INIS)

Modjtahedi, Bagher; Movassagh, Nahid

2005-01-01

This study reports several empirical findings concerning natural gas futures prices. First, spot and futures prices are non-stationary and the observed trends are due to positive drifts in the random-walk components of the prices rather than possible deterministic time trends. Second, market forecast errors are stationary. Third, futures are less than expected future spot prices so that futures are backdated. Fourth, the bias in the futures prices is time varying. Fifth, futures have statistically significant market-timing ability, despite the bias in the magnitude forecasts. Finally, the data lends partial support to the cost-of-carry theory of the basis determination. (Author)

Natural-gas futures: Bias, predictive performance, and the theory of storage

Energy Technology Data Exchange (ETDEWEB)

Modjtahedi, Bagher [California Univ., Davis, CA (United States); California Franchise Tax Board, CA (United States); Movassagh, Nahid [California Energy Commission, MS22, Sacramento, CA (United States)

2005-07-01

This study reports several empirical findings concerning natural gas futures prices. First, spot and futures prices are non-stationary and the observed trends are due to positive drifts in the random-walk components of the prices rather than possible deterministic time trends. Second, market forecast errors are stationary. Third, futures are less than expected future spot prices so that futures are backdated. Fourth, the bias in the futures prices is time varying. Fifth, futures have statistically significant market-timing ability, despite the bias in the magnitude forecasts. Finally, the data lends partial support to the cost-of-carry theory of the basis determination. (Author)

The impact of wind forecast errors on the efficiency of the Ontario electricity market

International Nuclear Information System (INIS)

Ng, H.

2008-01-01

Ontario's Independent System Operator (IESO) is currently involved in a number of wind projects in the province, and has developed both a resource commitment and dispatch timeline in relation to increased wind power penetration in the Ontario electricity grid. This presentation discussed the impacts of wind forecast errors on the province's electricity market. Day-ahead planning is used to commit fossil fuels and gas resources, while 3-hours ahead planning is used to commit generation in real time. Inter-ties are committed 1 hour ahead of dispatch. Over-forecasts for wind can cause market prices to increase in real-time, or cause markets to miss opportunities to schedule cheaper imports. The inefficient scheduling caused by overforecasts can also lead to exports not being purchases at high enough prices. Under-forecasts can cause market prices to decrease, and may cause imports to be scheduled that would not have been economic at lower prices. The scheduling difficulties related to under-forecasting can cause markets to miss opportunities to schedule efficient exports. Wind facility forecast errors typically improve closer to real-time. One-hour ahead wind forecast errors can reach approximately 12 per cent. The annual costs of overforecasting are under $200,000. Underforecasting costs are usually less than $30,000. The costs of the wind forecasting inefficiencies are relatively small in the $10 billion electricity market. It was concluded that system operators will continue to track forecast errors and inefficiencies as wind power capacity in the electric power industry increases. tabs., figs

U.S. gas outlook: The price is right

International Nuclear Information System (INIS)

Parent, L.

1997-01-01

The gas business is on a roll. Prices are higher than ever since deregulation became a reality; the gas-well rig count is higher than it has been in years; pipelines are adding capacity; demand is growing; and the prospect of adding substantial gas-fired electric generating capacity is the pot of gold at the end of the rainbow. The players also recognize that competition is fierce and that they need to be cost-effective operators at the leading edge of technology, and flexible enough to manage change. Supporting these premises, the following discussion covers: (1) pricing factors--trends for a slight increase, futures, the market driver, status/effect of storage, and supply/demand; (2) gas well drilling--an increase in 1996, effects of geographics on incentive, good news/bad news of creating another surplus; (3) energy marketing trends--electric power industry restructuring, pairing of electrics and pipelines; (4) gas industry standards--new proposals for 1997 implementation; and (5) Canada/Mexico--competition along the northern US border, Mexico still getting its act together

Gas Supply, Pricing Mechanism and the Economics of Power Generation in China

Directory of Open Access Journals (Sweden)

Yuanxin Liu

2018-04-01

Full Text Available During the “13th Five-Year Plan” period, green energy is the top priority for China. China has realized that natural gas, as a low-carbon energy source, fits with the nation’s energy demand and will play a critical role in the energy transition, but the actual industry development is slower than expected. By analyzing the major gas corporations around the world, the paper finds that the key factors of the sector are supply and price of the energy resource. A comprehensive analysis on domestic and foreign imported gas reveals a trend of oversupply in China in the future. Given the critical import dependence, China has introduced a series of gas price reforms since 2013, which have led to negative impacts on important gas consumption sectors including power generation. With the levelized cost of electricity (LCOE model, we find that under the prevailing gas supply structure and price level, the economy of utility gas power generation will remain unprofitable, while combined cooling heating and power (CCHP is only commercially feasible in coastal developed regions. If continuing, such a trend will not only bring forth disastrous consequences to gas power industry, but also damage the upstream gas industry, more importantly, impede the energy transition. We conclude the paper with policy implications on pricing mechanism reform, developing domestic unconventional gas and the R&D of gas turbine.

Gas prices in Europe before oligopoly and liberalization; Il prezzo del gas in Europa tra liberalizzazione e oligopolio

Energy Technology Data Exchange (ETDEWEB)

Bianchi, A.

1998-09-01

The forthcoming incorporation of the EU Directive on the internal gas market shall set the conditions to increase competition among suppliers, thus lowering gas prices in Europe. But, in perspective, the strategic decisions taken by the majors tend to a stronger oligopoly coordination to sustain prices and share the gas price premium provided by environmental constraints and the technical dominance of combined-cycle plants. [Italiano] Con la direttiva comunitaria sul mercato interno del gas si dovrebbero creare le condizioni per una maggiore competizione tra i fornitori e, quindi, per una riduzione dei prezzi del gas in Europa. Le azioni strategiche delle major paiono invece tendere, in prospettiva, a un piu` forte coordinamento oligopolistico per sostenere i prezzi e spartirsi l`incremento di premio del gas garantito dai vincoli ambientali e dal dominio del ciclo combinato.

On the electrification of road transport - Learning rates and price forecasts for hybrid-electric and battery-electric vehicles

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.

Guidelines For Evaluation Of Natural Gas Projects

International Nuclear Information System (INIS)

Farag, H.; El Messirie, A.

2004-01-01

This paper is objected to give guidelines for natural gas projects appraisal These guidelines are summarized in modeling of natural gas demand forecast and energy pricing policies for different gas consumers mainly in the manufacturing, mining, transport, trade and agriculture sectors. Analysis of the results is made through sensitivity analysis and decision support system ( DSS )

Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique

Science.gov (United States)

Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad

2015-11-01

One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.

Effect of Energy Efficiency Standards on Natural Gas Prices

Energy Technology Data Exchange (ETDEWEB)

Carnall, Michael; Dale, Larry; Lekov, Alex

2011-07-26

A primary justification for the establishment of energy efficiency standards for home appliances is the existence of information deficiencies and externalities in the market for appliances. For example, when a long-term homeowner purchases a new gas-fired water heater, she will maximize the value of her purchase by comparing the life-cycle cost of ownership of available units, including both total installed cost - purchase price plus installation costs - and operating cost in the calculus. Choice of the appliance with the lowest life-cycle costs leads to the most economically efficient balance between capital cost and fuel cost. However, if the purchaser's expected period of ownership is shorter than the useful life of the appliance, or the purchaser does not pay for the fuel used by the appliance, as is often the case with rental property, fuel cost will be external to her costs, biasing her decision toward spending less on fuel efficiency and resulting in the purchase of an appliance with greater than optimal fuel usage. By imposing an efficiency standard on appliances, less efficient appliances are made unavailable, precluding less efficient purchases and reducing fuel usage. The reduction in fuel demanded by residential users affects the total demand for such fuels as natural gas, for example. Reduced demand implies that residential customers are willing to purchase less gas at each price level. That is, the demand curve, labeled D{sub 0} in Figure 1, shifts to the left to D{sub 1}. If there is no change in the supply function, the supply curve will intersect the demand curve at a lower price. Residential demand is only one component of the total demand for natural gas. It is possible that total demand will decline very little if demand in other sectors increases substantially in response to a decline in the price. If demand does decrease, modeling studies generally confirm the intuition that reductions in demand for natural gas will result in reductions

Heavy oil supply economics and supply response to low oil prices

International Nuclear Information System (INIS)

Fisher, L.

1999-01-01

The dynamics of the heavy oil industry are examined, including prices, market demand, supply and supply costs. Price assumptions are provided for the reference case oil price (west Texas intermediate at Cushing). Supply cost methodology is explained. Capital and operating costs for various heavy oil and synthetic sources are derived from modeling results. The range of supply costs for heavy oil and bitumen from various sources, supply costs in terms of reference case market values and in terms of 1995-1996 average market values for Bow River crude, are derived. The CERI long term supply forecast model is explained. Western Canada upstream oil and gas cash flow and capital expenditures, eastern Canada exploration and expenditures by hydrocarbon type, and Canadian heavy oil and bitumen production based on reference case prices are estimated. Based on these projections the outlook for heavy oil at reference case prices for better than average quality resources is judged to be economic. Lower quality resources will require technology gains for successful commercialization. SAGD is a likely candidate in this respect. Again based on reference prices, production is forecast to decline by 100 Kb/d over the next five years. Diluent supply is considered to be adequate throughout the forecast period. As far as thermal bitumen is concerned, the growth could, in fact, exceed the projection, but if so, more upgrading will be required. 11 figs

THE ANALYSIS OF THE COMMODITY PRICE FORECASTING SUCCESS CONSIDERING DIFFERENT LENGTHS OF THE INITIAL CONDITION DRIFT

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.

Alternative natural gas contract and pricing structures and incentives for the LNG industry

International Nuclear Information System (INIS)

Attanasi, E.D.

1991-01-01

Gas conversion to liquefied gas (LNG) and transport by LNG tankers is one option for meeting expanding gas consumption and for gas traded internationally. This paper examines the impact of the traditional gas contract provisions of indefinite pricing, market out price ceilings, and take-or-pay requirements on the profitability of LNG projects in the context of markets characterized by price and quantity uncertainty. Simulation of experiments are used to examine and calibrate the effects of those provisions. The results provide guidance to operators, host countries and purchasers in structuring such contracts. The paper also assesses prospects of future expansion of world LNG capacity. (author). 11 refs, 3 figs, 4 tabs

Price elasticity of natural gas demand in the power generation sector

International Nuclear Information System (INIS)

McArdle, P.F.

1990-01-01

Today, the demand for energy by the electric generation sector is highly competitive and price-responsive. Previous estimates of the price elasticity of natural gas demand in this sector have focused primarily on data from the 1960s and 1970s. Such estimates fail to take full account of economic, regulatory, and legislative developments that have altered the structure of the electric generation market during the 1980s. Structural changes include an increased ability of utilities to choose among generating options, the increase in non-utility generators, the amending of the Fuel Use Act, and a more competitive market for electricity. An accurate estimate of price elasticity requires a refocusing on data from the post-1983 period. The purpose of this paper is to answer two questions: how price responsive (elastic) is natural gas demand in this market; and what changes in natural gas demand elasticity have occurred over time

Estimating household fuel oil/kerosine, natural gas, and LPG prices by census region

International Nuclear Information System (INIS)

Poyer, D.A.; Teotia, A.P.S.

1994-08-01

The purpose of this research is to estimate individual fuel prices within the residential sector. The data from four US Department of Energy, Energy Information Administration, residential energy consumption surveys were used to estimate the models. For a number of important fuel types - fuel oil, natural gas, and liquefied petroleum gas - the estimation presents a problem because these fuels are not used by all households. Estimates obtained by using only data in which observed fuel prices are present would be biased. A correction for this self-selection bias is needed for estimating prices of these fuels. A literature search identified no past studies on application of the selectivity model for estimating prices of residential fuel oil/kerosine, natural gas, and liquefied petroleum gas. This report describes selectivity models that utilize the Dubin/McFadden correction method for estimating prices of residential fuel oil/kerosine, natural gas, and liquefied petroleum gas in the Northeast, Midwest, South, and West census regions. Statistically significant explanatory variables are identified and discussed in each of the models. This new application of the selectivity model should be of interest to energy policy makers, researchers, and academicians

Dynamical behavior of price forecasting in structures of group correlations

Science.gov (United States)

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.

The contribution of the DOE's R ampersand D budget in natural gas to energy price security

International Nuclear Information System (INIS)

Sutherland, R.J.

1992-01-01

The energy price volatility model suggests that some of the proposed natural gas programs can contribute to energy price stability. The sector most vulnerable to fuel price variations is, of course, the transportation sector. The most effective strategy to achieve energy pace stability is to reduce petroleum consumption in this sector. The natural gas vehicle program is therefore recommended as potentially important and worthy of further consideration. At this point, distinguishing the merits of various subprograms is not feasible. This result farther supports the conclusion that the DOE's energy R ampersand D portfolio is not efficiently balanced and an increase in oil and gas research should be a high priority. The DOE has responded favorably and has significantly increased its proposed research with the explicit objective of displacing oil in the transportation sector. The enhanced research and development program for energy security, in the NES, proposes major funding, increases in this area. To recommend the further increases proposed by the industry, a careful analysis of incremental benefits and costs is required. The proposed natural as supply program is intended to enhance the future supply of natural gas. As explained above, enhanced gas supplies can reduce the volatility of gas prices and severe the link between gas and oil prices. The gas supply program is recommended as a potentially important strategy to ensure energy price stability. The importance of this point merits restatement. Oil price volatility affects directly the transportation and industrial sectors. The residential, commercial and electric utility sectors are not highly oil dependent. However, oil prices have affected gas prices and gas is used extensively the residential, commercial, industrial and electric utility sectors. Energy price stability is enhanced in these sectors by severing, the link, between oil and gas prices

Price interactions and discovery among natural gas spot markets in North America

International Nuclear Information System (INIS)

Park, Haesun; Mjelde, James W.; Bessler, David A.

2008-01-01

Recent advances in modeling causal flows with time series analysis are used to study relationships among eight North American natural gas spot market prices. Results indicate that the Canadian and US natural gas market is a single highly integrated market. Further results indicate that price discovery tends to reflect both regions of excess demand and supply. Across North America, Malin Hub in Oregon, Chicago Hub, Illinois, Waha, Texas, and Henry Hub, Louisiana region, are the most important markets for price discovery. Opal Hub in Wyoming is an information sink in contemporaneous time, receiving price information but passing on no price information. AECO Hub in Alberta, Canada, receives price signals from several markets and passes on information to Opal and the Oklahoma region. (author)

Flexible LNG supply, storage and price formation in a global natural gas market

Science.gov (United States)

Hayes, Mark Hanley

The body of work included in this dissertation explores the interaction of the growing, flexible liquefied natural gas (LNG) trade with the fundamentals of pipeline gas supply, gas storage, and gas consumption. By nature of its uses---largely for residential heating and electric power generation---the consumption of natural gas is highly variable both seasonally and on less predictable daily and weekly timescales. Flexible LNG trade will interconnect previously isolated regional gas markets, each with non-correlated variability in gas demand, differing gas storage costs, and heterogeneous institutional structures. The dissertation employs a series of analytical models to address key issues that will affect the expansion of the LNG trade and the implications for gas prices, investment and energy policy. First, I employ an optimization model to evaluate the fundamentals of seasonal LNG swing between markets with non-correlated gas demand (the U.S. and Europe). The model provides insights about the interaction of LNG trade with gas storage and price formation in interconnected regional markets. I then explore how random (stochastic) variability in gas demand will drive spot cargo movements and covariation in regional gas prices. Finally, I analyze the different institutional structures of the gas markets in the U.S. and Europe and consider how managed gas markets in Europe---without a competitive wholesale gas market---may effectively "export" supply and price volatility to countries with more competitive gas markets, such as the U.S.

Effects of the uncertainty of energy price and water availability forecasts on the operation of Alpine hydropower reservoir systems

Science.gov (United States)

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

Visibility graph network analysis of natural gas price: The case of North American market

Science.gov (United States)

Sun, Mei; Wang, Yaqi; Gao, Cuixia

2016-11-01

Fluctuations in prices of natural gas significantly affect global economy. Therefore, the research on the characteristics of natural gas price fluctuations, turning points and its influencing cycle on the subsequent price series is of great significance. Global natural gas trade concentrates on three regional markets: the North American market, the European market and the Asia-Pacific market, with North America having the most developed natural gas financial market. In addition, perfect legal supervision and coordinated regulations make the North American market more open and more competitive. This paper focuses on the North American natural gas market specifically. The Henry Hub natural gas spot price time series is converted to a visibility graph network which provides a new direction for macro analysis of time series, and several indicators are investigated: degree and degree distribution, the average shortest path length and community structure. The internal mechanisms underlying price fluctuations are explored through the indicators. The results show that the natural gas prices visibility graph network (NGP-VGN) is of small-world and scale-free properties simultaneously. After random rearrangement of original price time series, the degree distribution of network becomes exponential distribution, different from the original ones. This means that, the original price time series is of long-range negative correlation fractal characteristic. In addition, nodes with large degree correspond to significant geopolitical or economic events. Communities correspond to time cycles in visibility graph network. The cycles of time series and the impact scope of hubs can be found by community structure partition.

Forecasting the natural gas demand in China using a self-adapting intelligent grey model

International Nuclear Information System (INIS)

Zeng, Bo; Li, Chuan

2016-01-01

Reasonably forecasting demands of natural gas in China is of significance as it could aid Chinese government in formulating energy policies and adjusting industrial structures. To this end, a self-adapting intelligent grey prediction model is proposed in this paper. Compared with conventional grey models which have the inherent drawbacks of fixed structure and poor adaptability, the proposed new model can automatically optimize model parameters according to the real data characteristics of modeling sequence. In this study, the proposed new model, discrete grey model, even difference grey model and classical grey model were employed, respectively, to simulate China's natural gas demands during 2002–2010 and forecast demands during 2011–2014. The results show the new model has the best simulative and predictive precision. Finally, the new model is used to forecast China's natural gas demand during 2015–2020. The forecast shows the demand will grow rapidly over the next six years. Therefore, in order to maintain the balance between the supplies and the demands for the natural gas in the future, Chinese government needs to take some measures, such as importing huge amounts of natural gas from abroad, increasing the domestic yield, using more alternative energy, and reducing the industrial reliance on natural gas. - Highlights: • A self-adapting intelligent grey prediction model (SIGM) is proposed in this paper. • The SIGM has the advantage of working with exponential functions and linear functions. • The SIGM solves the drawbacks of fixed structure and poor adaptability of grey models. • The demand of natural gas in China is successfully forecasted using the SIGM model. • The study findings can help Chinese government reasonably formulate energy policies.

A novel improved fuzzy support vector machine based stock price trend forecast model

OpenAIRE

Wang, Shuheng; Li, Guohao; Bao, Yifan

2018-01-01

Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has become a hot research topic in the field of machine learning, and it has been successfully applied in many fields. However, as a new technology, there are many limitations to support...

Indexing of gas prices with respect to those of petroleum products: problem and perspectives

International Nuclear Information System (INIS)

Percebois, J.; Sauvage, E.; Valette, M.; Liens, G.; Lu, L.

2009-01-01

A debate was organized by the French gas association (AFG) on December 2, 2008 around the question of: is it opportune to maintain the present day system of indexing of gas prices with respect to petroleum product prices? Even if the basic reasons justifying this indexing system have changed with time, and despite the recent hostility of the European Commission, this practice remains the standard for the huge majority of gas transactions. Does this indexing system favour the producers? In spite of their apparent interest, do the consumers really wish to replace indexed prices by market prices in a context where strong uncertainties and tensions on gas markets cannot be excluded? Is the present day status quo the result of the situation imposed by producers or is it the consequences of contradictory anticipations between sellers and buyers? Will gas prices remain indexed on petroleum prices in the future and if not, what would be the possible alternatives? These are the questions debated by the participants and reported in this paper with the questions from the audience. (J.S.)

U.S. gas supply overview

International Nuclear Information System (INIS)

George, R.

1999-01-01

The most recent outlook by Purvin and Gertz regarding the long-term supply potential of oil and gas producing basins in the United States was presented. The role that technology will play in extending their economic reach was also discussed. The focus of this paper was on regional supply, inter regional gas flows and related issues such as pricing. A series of maps depicting production (in Tcf) of various basins in North America showed that the important supply sources are in the deepwater Gulf of Mexico, Rocky Mountains, onshore Texas and Canada. Natural gas pricing from 1990 to 2020 has been forecasted to steadily increase. 1 tab., 11 figs

The lead-lag relationships between spot and futures prices of natural gas

Science.gov (United States)

Zhang, Yahui; Liu, Li

2018-01-01

The lead-lag relationships between spot and futures markets are of great interest for academics. Previous studies neglect the possibility of nonlinear behaviors which may be caused by asymmetry or persistence. To fill this gap, this paper uses the MF-DCCA method and the linear and nonlinear causality tests to explore the causal relationships between natural gas spot and futures prices in the New York Mercantile Exchange. We find that spot and futures prices are positive cross-correlated, the natural gas futures can linearly Granger cause spot price, and there are bidirectional nonlinear causality relationships between natural gas spot and futures prices. Further, we explore the sources of nonlinear causality relationships, and find that the volatility spillover can partly explain the nonlinear causality and affect their cross-correlations.

Will the supply meet the demand? The future of the natural gas liquids market in the WCSB

International Nuclear Information System (INIS)

Stauft, T.

2004-01-01

Natural Gas Liquids (NGL) price influences were reviewed in this presentation, as well as issues concerning North American propane demand and waterborne imports. A review of U.S. propane stocks was provided as well as regional temperature outlooks for 2004-2005. A cracking feedstock parity forecast was presented, as well as United States gross gas plant margins and propane prices to July 2005. Canadian propane inventories and prices were reviewed. A propane supply and demand forecast to 2020 was presented. Alberta's natural gas supply and intra-Alberta oil sand gas demand growth were discussed. Various market uncertainties include higher levels of activity; the potential of petroleum prices falling due to a reduction of geopolitical risk; the possibility of a U.S. recession; and the growth of Alberta's oil sands industry, with resulting demand for natural gas. It was concluded that the NGL market in North America will continue to be balanced, with waterborne imports becoming more critical. It was suggested that inventories are adequate for the expected winter season. It was also suggested that Canadian NGL supplies are expected to decline, and that prices are expected to soften in the spring of 2005, with falling natural gas and crude oil prices. refs., tabs., figs

Making sure natural gas gets to market

International Nuclear Information System (INIS)

Pleckaitis, A.

2004-01-01

The role of natural gas in power generation was discussed with reference to price implications and policy recommendations. New natural gas supply is not keeping pace with demand. Production is leveling out in traditional basins and industry investment is not adequate. In addition, energy deregulation is creating disconnects. This presentation included a map depicting the abundant natural gas reserves across North America. It was noted that at 2002 levels of domestic production, North America has approximately 80 years of natural gas. The AECO consensus wholesale natural gas price forecast is that natural gas prices in 2010 will be lower than today. The use of natural gas for power generation was outlined with reference to fuel switching, distributed generation, and central generation. It was emphasized that government, regulators and the energy industry must work together to address policy gaps and eliminate barriers to new investment. 13 figs

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

Energy Technology Data Exchange (ETDEWEB)

Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

2011-02-15

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)

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)

Petroleum price

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)

On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

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...

Gas pricing in Europe. Pt. 2. End-use consumption markets

International Nuclear Information System (INIS)

Donath, R.

1996-01-01

In the end-use consumption markets, gas is supplied to households, small consumers and industrial customers of retail distributors. As regards the delimitation of industrial customers receiving gas from retail distributors, there are great differences from one country to another, similarly to the market segmentation of wholesale markets.- First of all, the article points out structures and regulations in the investigated end-use consumption markets. The second part investigates cost-oriented and value-oriented pricing principles, followed by a comparison of price structures based on the Eurostat gas purchasing criteria for households and small consumers in the third part. A fourth part summarizes the results. (orig./UA) [de

The top 100: hanging on for another wild oil price ride with new survival skills and help from hired hands

International Nuclear Information System (INIS)

Harvie, W.; Jaremko, G.

1998-01-01

A forecast of industry performance in 1998 predicts that despite the heavy plunge of prices in 1997 that resulted in an increase in the TSE oil and gas index of only 2.8 per cent compared to 36.6 per cent in 1996, the Canadian oil and gas industry will be able to hang on. This is due to new technology, overhauled companies, improved management, a more diverse mix of products, better access to export outlets and new expertise in marketing, trading, hedging and risk assessment. Industry forecasts call for 12,000 new wells in 1998 which, if realized, will be the second best performance for the industry since 1986. Takeovers by the likes of Renaissance Energy, the merger by Nova Corporation and TransCanada Pipelines and the prospect of new competition to the Nova-TransCanada System by the Alliance Pipeline Project, are just some of the twists and turns that help maintain the buoyancy of the industry. Heavy oil developments are likely to remain dormant during 1998, although the long term prospects look strong. Husky Oil's $ 500 million expansion of its Lloydminster upgrader is seen as the lone bright spot in 1998. The natural gas sector has kept the industry going in 1997, but with the heating season over, the immediate prospects are not very bright. Gas prices are likely to decline during the summer, but with new pipeline capacity coming on-stream soon, the artificially low gas prices in Alberta should begin to ease later in the year. Conventional oil prices are expected to rise, provided OPEC members can keep recalcitrant members in check. If light oil prices do not rise soon, the forecast calls for several oil companies to end up on the auctioneer's block

Assessing demand when introducing a new fuel: natural gas on Java

International Nuclear Information System (INIS)

Groenendaal, W.J.H. van

1995-01-01

The Indonesian government is investing in a gas transmission system on Java. For the evaluation of this investment a forecast of the demand for natural gas by the manufacturing sector is needed. To obtain this forecast the manufacturing sector is divided into subsectors according to energy use in production processes. On the level of production processes the opportunities for natural gas are based on net present value evaluations of its future benefits in production. This results in the desired fuel mix for manufacturing subsectors, from which the gas intensity ratios per subsector for existing production and new investments are calculated. Gas demand can then be forecast by combining the gas intensity ratios with subsectoral (growth in) gross value-added. This approach leads to a flexible forecasting tool that can readily account for changes in economic structure and energy prices, as encountered by rapidly developing economies. (author)

Competitive Pricing by a Price Leader

OpenAIRE

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...

Rising natural gas and electricity prices in the Netherlands

International Nuclear Information System (INIS)

Roggen, M.

2004-01-01

In a free market, the price for electricity rises rather than falls. And as for the gas price, the consumer will be facing strong fluctuations. For that matter, it is only slightly connected with the liberalization of the market. An employee of Roland Berger Strategy Consultants has delved deeply into the matter, down to the euro [nl

Natural gas market assessment: Price convergence in North American natural gas markets

International Nuclear Information System (INIS)

1995-12-01

The extent to which Canadian and U.S. natural gas markets have become integrated in the post-deregulation era was assessed. This assessment was accomplished through a statistical analysis of the price movements in Canadian and U.S. gas markets. The analysis pointed to three broad conclusions: (1) on the whole, there has been an increasing degree of integration among North American natural gas markets since price deregulation and the introduction of open access, (2) there is somewhat of a split between eastern and western markets, (3) Alberta's links are stronger with the western U.S. natural gas market than with the market in the eastern U.S. Several factors were cited as contributing to the general increase in market integration, including: (1) increased pipeline capacity and additional pipeline interconnections, coupled with the development of market hubs, (2) improved flexibility of access to pipeline transportation services, (3) improved access to market information and greater trading flexibility which has been facilitated by growing use of electronic bulletin boards and electronic trading systems. The increased market integration was claimed to have benefited both consumers and producers, and to have increased competition in both countries.. 28 refs., 14 figs

Gas and electricity prices in France and in the European Union in 2012

International Nuclear Information System (INIS)

Martin, Jean-Philippe

2013-11-01

Graphs and tables illustrate the levels and the evolution of gas and electricity prices in France and in EU member states. It is notably outlined that gas is less expensive in central and eastern Europe, that gas prices generally increased in Europe between 2011 and 2012 (for industries as well as for households), that electricity remains cheap in France

Price trends and project viability

International Nuclear Information System (INIS)

Olsen, W.H.

1999-01-01

The paper discusses some of the remarkable changes that have occurred in the oil and gas industry over about the past 25 years. In the past ten years alone, technology and politics have brought new ways of working together and the recovery of resources once thought of as impossible. Cooperation appears to be a key word. The paper was presented under the sub-headings of the environment, global energy outlook, technological drivers and the challenge ahead. The current low price of oil will inevitably slow down exploration and field development but the author remains optimistic despite the tough challenges. The paper contains many diagrams relating to production, costs, efficiency, exploration, reserves, price forecasts and exploration technology

Price-related sensitivities of greenhouse gas intensity targets

International Nuclear Information System (INIS)

Muller, Benito; Muller-Furstenberger, Georg

2003-12-01

Greenhouse gas intensities are an appealing tool to foster abatement without imposing constraints on economic growth. This paper shows, however, that the computation of intensities is subject to some significant statistical and conceptual problems which relate to the inflation proofing of GDP growth. It is shown that the choice of price-index, the updating of quantity weights and the choice of base year prices can have a significant impact upon the commitment of intensity targets

U.S., non-U.S. outlays to rise in '98, but oil price plunge clouds spending outlook

International Nuclear Information System (INIS)

Beck, R.J.

1998-01-01

Capital spending by oil and gas companies in and outside the US will rise in 1998, but that forecast may be jeopardized by the continuing plunge in oil prices. For operations in the US, oil and gas company capital spending is expected to move up in 1998 for the fourth year in a row. If the money is spent, it will be the highest industry investment level since 1985. Strong oil and gas prices and increased volumes have boosted company cash flow and profits the last few years, fueling increased spending. However, the near-term outlook has now been clouded by economic turmoil in a number of Asian countries and the recent collapse of oil prices. The paper discusses oil and gas prices, US upstream spending, US non-exploration and production spending, capital spending in Canada, and spending outside US and Canada

Canadian natural gas price debate : TCGS view

International Nuclear Information System (INIS)

Johnson, J.

1998-01-01

Issues regarding the Alberta supply of natural gas were debated. Factors considered include pipeline expansions, storage and foreign exchange. The influence of NYMEX was also cited as an important determinant of gas pricing. Currently, the Western Canada Sedimentary Basin's (WCSB) market share is 22 per cent of the North American demand. The WCSB extends through Alberta, British Columbia, Saskatchewan, the Northwest Territories and the Yukon. The Basin's estimated reserves at the end of 1996 were 65 TCF. tabs., figs

Gas price and oil price: a new level of competition; Gaspreis und Oelpreis. Eine neue Stufe des Wettbewerbs

Energy Technology Data Exchange (ETDEWEB)

Hahn, Wolfgang; Poepperl, Claudia [Energie Consulting GmbH, Kehl (Germany)

2012-01-15

With a marked delay relative to electricity the gas market has now too come under the reign of competition. The dissociation of gas prices from oil prices was not only the result of successive deregulation but was also catalysed by the drop in demand attending the economic slump in 2008 and 2009. In response to the changing market environment the procurement processes of industrial and commercial customers have undergone lasting changes in the course of the past three years. At the same time, fierce competition has developed between the two energy carriers crude oil and natural gas.

The US Shale Gas Revolution and Its Externality on Crude Oil Prices: A Counterfactual Analysis

Directory of Open Access Journals (Sweden)

Hongxun Liu

2018-03-01

Full Text Available The expansion of shale gas production since the mid-2000s which is commonly referred to as “shale gas revolution” has had large impacts on global energy outlook. The impact is particularly substantial when it comes to the oil market because natural gas and oil are substitutes in consumption and complements and rivals in production. This paper investigates the price externality of shale gas revolution on crude oil. Applying a structural vector autoregressive model (VAR model, the effect of natural gas production on real oil price is identified in particular, and then based on the identification, counterfactuals of oil price without shale gas revolution are constructed. We find that after the expansion of shale gas production, the real West Texas Intermediate (WTI oil price is depressed by 10.22 USD/barrel on average from 2007 to 2017, and the magnitude seems to increase with time. In addition, the period before shale gas revolution is used as a “thought experiment” for placebo study. The results support the hypothesis that real WTI oil price can be reasonably reproduced by our models, and the estimated gap for oil price during 2007–2017 can be attributed to shale gas revolution. The methodology and framework can be applied to evaluate the economic impacts of other programs or policies.

Gas prices deregulation: how to play the game well?

International Nuclear Information System (INIS)

Petitot, Pauline

2014-01-01

At the instigation of the European Union and in a context of increasing deregulation, the end of regulated gas prices in France is coming soon. Suppliers and consumers concerned are actively preparing themselves to take the turn of gas market liberalization. This short paper reveals their strategies

Gas pricing in a liberalized European market; will the rent be taxed away?

International Nuclear Information System (INIS)

Austvik, O.G.

1997-01-01

The European gas market will become 'more liberal'. Depending on in which segments competition is intensified and public interference takes place, prices in the gas chain will be affected. Rent may be redistributed among firms and prices will become more volatile. If supply overshoots demand for a long period, average consumer prices may also be pushed down. Rent may also end up as tax revenues for public authorities. This article argues that an active use of gas taxes as an instrument to derive public revenues increases the probability of a politically led liberalization process. The effect of these economic and political forces and actions may, however, be less new gas to the market. (author)

Natural gas prices in Italy. Tariffs geographical distribution

International Nuclear Information System (INIS)

Marrocchelli, A.

2000-01-01

The annual report on services and activity carries at some evaluations of data concerned the natural gas market: total consumption, costs and prices in Italy and comparative evaluations with other european countries [it

Pricing, hedging and risk management : practical tips for natural gas buyers and sellers

International Nuclear Information System (INIS)

Shields, D.

1998-01-01

Risk analysis and techniques to manage risk as it pertains to the natural gas industry were discussed. Portfolio allocations for long-term, short-term fixed price and variable price contracts were described. Options were defined as a market instrument offering the benefits of a fixed price purchase or sale without the obligation of incurring financial or opportunity losses if the market goes against the option buyer. Options should be used as a defence strategy to protect portfolios from price risk in times of uncertainty and to take advantage of current floating market conditions without making a full price commitment. Options can also be used as an offensive strategy to make a directional play on the market or on volatility. Options selling was regarded as a much higher risk than options buying. The variables that affect options premiums were: (1) volatility, (2) time to expiration, (3) underlying price versus strike price, and (4) flexibility. Williams Energy's new world class energy trading floor in Tulsa, Oklahoma was also described. Williams is the largest-volume transporter of natural gas in the U.S. with more than 27,000 miles of pipelines. Williams pipelines transport 16 per cent of all the natural gas used in the U.S. and the company is one of the nation's largest natural gas gatherers and processors. tabs., figs

Natural gas supply, demand and price outlook

International Nuclear Information System (INIS)

Anon.

1992-01-01

Natural gas consumption in the US grew 15.9 percent between 1986 and 1989. Its share of total primary energy use in the US grew from 22.5 percent to 23.8 percent. Despite unusually warm weather and an economic downturn, natural gas use in the first eight months of 1990 fell only modestly from its 1989 pace - while its market share of US total primary energy use has remained stable. The American Gas Association's Total Energy Resource Analysis energy modeling system (A.G.A.-TERA) projects continued growth in natural gas demand and supply. Natural gas is projected to gain a growing share of total US primary use. Natural gas prices are projected to be sufficient to encourage growth in well completions and reserve additions, yet competitive with electricity, fuel oil and other alternative forms of energy

The Share Price and Investment: Current Footprints for Future Oil and Gas Industry Performance

Directory of Open Access Journals (Sweden)

Ionel Jianu

2018-02-01

Full Text Available The share price has become a very important indicator for shareholders, banks, and financial institutions evaluating the performance of companies. The oil and gas industry seems to be in a difficult era of development, due to the market prices for its products. Moreover, climate change and renewable energies are barriers for fossil energy. This state of affairs, and the fact that oil and gas shares are considered one of the most solid and reliable shares on the London Stock Exchange (LSE, have drawn our attention. International institutions encourage the investment in the oil and gas economic sector. This study investigates how investments of oil and gas companies in long-term assets influence the share price. Using the Ohlson share price model for a sample of 51 listed companies on the LSE proves that investments in long-term assets influence the share price in the case of companies which record losses. Investments in long-term assets are responsible for the attractiveness of the oil and gas company shares.

How does increased corn-ethanol production affect US natural gas prices?

International Nuclear Information System (INIS)

Whistance, Jarrett; Thompson, Wyatt

2010-01-01

In recent years, there has been a push to increase biofuel production in the United States. The biofuel of choice, so far, has been ethanol produced from corn. The effects of increased corn-ethanol production on the consumer prices of food and energy continue to be studied and debated. This study examines, in particular, the effects of increased corn-ethanol production on US natural gas prices. A structural model of the natural gas market is developed and estimated using two stage least squares. A baseline projection for the period 2007-2018 is determined, and two scenarios are simulated. In the first scenario, current biofuel policies including EISA mandates, tariffs, and tax credits are removed. In the second scenario, we hold ethanol production to the level required only for largely obligatory additive use. The results indicate that the increased level of corn-ethanol production occurring as a result of the current US biofuel policies may lead to natural gas prices that are as much as 0.25% higher, on average, than if no biofuel policies were in place. A similar comparison between the baseline and second scenario indicates natural gas prices could be as much as 0.5% higher, on average, for the same period.

Price war or instrumentalization of price uncertainty: which strategy for a dominant provider on the European gas market

International Nuclear Information System (INIS)

Boussena, Sadek; Locatelli, Catherine

2016-03-01

The objective of this article is to try to assess which strategy could be implemented by a European dominant provider (or a group of big providers) during the current phase of transition of the European gas market in order to keep (or increase) his market shares and maximise his revenues. The authors aim at exploring possibilities of strategic actions on the long term other than those of defence of volumes through a price war, or the possibility of a strategy similar to that of Saudi Arabia which instrumentalises uncertainty on future prices. This last type of strategy is defined for the case of natural gas. The authors show that it could be implemented on the EU gas market, provided some specific conditions. They show that Gazprom has not enough power to become a price maker, and explore which kind of strategy of uncertainty could be implemented by this actor

Modeling spot markets for electricity and pricing electricity derivatives

Science.gov (United States)

Ning, Yumei

Spot prices for electricity have been very volatile with dramatic price spikes occurring in restructured market. The task of forecasting electricity prices and managing price risk presents a new challenge for market players. The objectives of this dissertation are: (1) to develop a stochastic model of price behavior and predict price spikes; (2) to examine the effect of weather forecasts on forecasted prices; (3) to price electricity options and value generation capacity. The volatile behavior of prices can be represented by a stochastic regime-switching model. In the model, the means of the high-price and low-price regimes and the probabilities of switching from one regime to the other are specified as functions of daily peak load. The probability of switching to the high-price regime is positively related to load, but is still not high enough at the highest loads to predict price spikes accurately. An application of this model shows how the structure of the Pennsylvania-New Jersey-Maryland market changed when market-based offers were allowed, resulting in higher price spikes. An ARIMA model including temperature, seasonal, and weekly effects is estimated to forecast daily peak load. Forecasts of load under different assumptions about weather patterns are used to predict changes of price behavior given the regime-switching model of prices. Results show that the range of temperature forecasts from a normal summer to an extremely warm summer cause relatively small increases in temperature (+1.5%) and load (+3.0%). In contrast, the increases in prices are large (+20%). The conclusion is that the seasonal outlook forecasts provided by NOAA are potentially valuable for predicting prices in electricity markets. The traditional option models, based on Geometric Brownian Motion are not appropriate for electricity prices. An option model using the regime-switching framework is developed to value a European call option. The model includes volatility risk and allows changes

Price Comovement Between Biodiesel and Natural Gas

OpenAIRE

Janda, Karel; Kourilek, Jakub

2016-01-01

We study relationship between biodiesel, as a most important biofuel in the EU, relevant feedstock commodities and fossil fuels. Our main interest is to capture relationship between biodiesel and natural gas. They are both used either directly as a fuel or indirectly in form of additives in transport. Therefore, our purpose is to �nd price linkage between biofuel and natural gas to support or reject the claim that they compete as alternative fuels and potential substitutes. The estimated p...