Spatial electric load forecasting
Willis, H Lee
2002-01-01
Spatial Electric Load Forecasting Consumer Demand for Power and ReliabilityCoincidence and Load BehaviorLoad Curve and End-Use ModelingWeather and Electric LoadWeather Design Criteria and Forecast NormalizationSpatial Load Growth BehaviorSpatial Forecast Accuracy and Error MeasuresTrending MethodsSimulation Method: Basic ConceptsA Detailed Look at the Simulation MethodBasics of Computerized SimulationAnalytical Building Blocks for Spatial SimulationAdvanced Elements of Computerized SimulationHybrid Trending-Simulation MethodsAdvanced
electrical load survey electrical load survey and forecast for a ...
eobe
1, 2NATIONAL CENTRE FOR HYDROPOWER RESEARCH AND DEV., UNIV. OF ILORIN ... be integrated to meet the present and future energy needs of this are ... paper reports the results of electrical load demand and forecast for Elebu rural community located in Kwara State, ... geothermal and ocean energies [5].
Forecasting Electrical Load Using a Multi-time-scale Approach
RINGWOOD John; Murray, F.T.
1999-01-01
This paper describes the application of a multi-time-scale technique to the modelling and forecasting of short-term electrical load. The multi-time-scale technique is based on adjusting the underlying short sampling period forecast time series with specific target points and possible aggregated demand. This allows not only improvement of the short sampling period forecast, but also focuses on weighting the accuracy of the forecast at certain critical points e.g. the ov...
101 Modelling and Forecasting Periodic Electric Load for a ...
User
2012-01-24
Jan 24, 2012 ... In this work, three models are used to analyze the electric load capacity of a ..... Forecasting electricity prices for a day-ahead pool-based electric energy market. ... Control, Operation and Management, Hong Kong pgs.782–7.
Load Forecasting in Electric Utility Integrated Resource Planning
Carvallo, Juan Pablo [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Larsen, Peter H. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sanstad, Alan H [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Goldman, Charles A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2017-07-19
Integrated resource planning (IRP) is a process used by many vertically-integrated U.S. electric utilities to determine least-cost/risk supply and demand-side resources that meet government policy objectives and future obligations to customers and, in many cases, shareholders. Forecasts of energy and peak demand are a critical component of the IRP process. There have been few, if any, quantitative studies of IRP long-run (planning horizons of two decades) load forecast performance and its relationship to resource planning and actual procurement decisions. In this paper, we evaluate load forecasting methods, assumptions, and outcomes for 12 Western U.S. utilities by examining and comparing plans filed in the early 2000s against recent plans, up to year 2014. We find a convergence in the methods and data sources used. We also find that forecasts in more recent IRPs generally took account of new information, but that there continued to be a systematic over-estimation of load growth rates during the period studied. We compare planned and procured resource expansion against customer load and year-to-year load growth rates, but do not find a direct relationship. Load sensitivities performed in resource plans do not appear to be related to later procurement strategies even in the presence of large forecast errors. These findings suggest that resource procurement decisions may be driven by other factors than customer load growth. Our results have important implications for the integrated resource planning process, namely that load forecast accuracy may not be as important for resource procurement as is generally believed, that load forecast sensitivities could be used to improve the procurement process, and that management of load uncertainty should be prioritized over more complex forecasting techniques.
Application of chaotic ant swarm optimization in electric load forecasting
Hong, Wei-Chiang [Department of Information Management, Oriental Institute of Technology, 58, Section 2, Sichuan Rd., Panchiao, Taipei County 220 (China)
2010-10-15
Support vector regression (SVR) had revealed strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, however, these employed evolutionary algorithms themselves have several drawbacks, such as converging prematurely, reaching slowly the global optimal solution, and trapping into a local optimum. This investigation presents an SVR-based electric load forecasting model that applied a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching its suitable parameters combination. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other alternative methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model. (author)
Adaptive load forecasting of the Hellenic electric grid
S. Sp. PAPPAS; L. EKONOMOU; V. C. MOUSSAS; P. KARAMPELAS; S. K. KATSIKAS
2008-01-01
Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique,which will use efficiently the information contained in the available data,is required,so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA),for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load,an anomaly is detected and,furthermore,when the pattern matches a known case of anomaly,the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A.,Athens,Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.
Short Term Electrical Load Forecasting by Artificial Neural Network
Hong Li
2016-07-01
Full Text Available This paper presents an application of artificial neural networks for short-term times series electrical load forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of training process. Historical data of hourly power load as well as hourly wind power generation are sourced from European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training with the adaptive learning factor starting at different initial value and errors behave volatile with constant learning factors with different values
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Daily peak electricity load forecasting in South Africa using a ...
major source of variation in peak demand forecasting and the inclusion of temperature has a significant ... This definition of electrical demand has its weaknesses. Electrical ..... The coefficient of t is positive, showing a positive linear trend.
None
1994-02-01
This publication documents the load forecast scenarios and assumptions used to prepare BPA's Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.
Using adaptive network based fuzzy inference system to forecast regional electricity loads
Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)
2008-02-15
Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)
Gayatri Dwi Santika
2017-03-01
Full Text Available This study applied Fuzzy Inference System Sugeno to forecast electrical load by considering the external factors. To see the accuracy of forecasting using Fuzzy Inference System Sugeno, then a comparison between the forecasting results of Fuzzy Inference System Sugeno using historical data with Fuzzy Inference System Sugeno using external factors was done. By using external factors method, resulted the smallest RMSE of 0762 and using historical data obtained error (RMSE of 1028. The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy Inference System Sugeno using only historical data.
Gayatri Dwi Santika; wayan f mahmudy
2017-01-01
.... The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy...
Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
Yuancheng Li
2016-11-01
Full Text Available The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays. Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.
Advances in electric power and energy systems load and price forecasting
2017-01-01
A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...
Load forecast method of electric vehicle charging station using SVR based on GA-PSO
Lu, Kuan; Sun, Wenxue; Ma, Changhui; Yang, Shenquan; Zhu, Zijian; Zhao, Pengfei; Zhao, Xin; Xu, Nan
2017-06-01
This paper presents a Support Vector Regression (SVR) method for electric vehicle (EV) charging station load forecast based on genetic algorithm (GA) and particle swarm optimization (PSO). Fuzzy C-Means (FCM) clustering is used to establish similar day samples. GA is used for global parameter searching and PSO is used for a more accurately local searching. Load forecast is then regressed using SVR. The practical load data of an EV charging station were taken to illustrate the proposed method. The result indicates an obvious improvement in the forecasting accuracy compared with SVRs based on PSO and GA exclusively.
Research in Residential Electricity Characteristics and Short-Term Load Forecasting
Haixia Feng
2013-07-01
Full Text Available In this paper we make research in Residential short-term load forecasting. Different application scenes have different affecting factors of short-term load, so we should specifically analysis of factors that affect the load of the residential electricity. We use SPSS (Statistic Package for Social Science to figure out the relationship between the daily load and temperature, weather conditions and other factors, finding the main factors among the impacting factors, and analyzing residential electricity consumption habits and load characteristics. Then, the paper introduces the common prediction methods. Combining with the above analysis to choose short-term load forecasting methods for residential users, we create automatic linear regression model and artificial neural network model to predict the future electricity load, calculating the residual between the predicted values and the actual values and mean square deviation of the values, and evaluating the accuracy of the load forecasting. The results prove that automatic linear regression model is effective in residential short-term electricity load forecasting.
A New Two-Stage Approach to Short Term Electrical Load Forecasting
Dragan Tasić
2013-04-01
Full Text Available In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach.
Møller Andersen, Frits; Larsen, Helge V.; Boomsma, Trine Krogh
2013-01-01
Data for aggregated hourly electricity demand shows systematic variations over the day, week, and seasons, and forecasting of aggregated hourly electricity load has been the subject of many studies. With hourly metering of individual customers, data for individual consumption profiles is availabl...... as the shares of consumption by categories of customers change and new consumption technologies such as electrical vehicles and (for Denmark in particular) individual heat pumps are introduced. © 2013 Elsevier Ltd. All rights reserved.......Data for aggregated hourly electricity demand shows systematic variations over the day, week, and seasons, and forecasting of aggregated hourly electricity load has been the subject of many studies. With hourly metering of individual customers, data for individual consumption profiles is available....... Using this data and analysing the case of Denmark, we show that consumption profiles for categories of customers are equally systematic but very different for distinct categories, that is, distinct categories of customers contribute differently to the aggregated electricity load profile. Therefore...
Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting
Li-Ling Peng
2016-03-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD method and quantum particle swarm optimization algorithm (QPSO for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia market and the New York Independent System Operator (NYISO, New York, USA are used for comparing the forecasting performances of different forecasting models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Forecasting Peak Load Electricity Demand Using Statistics and Rule Based Approach
Z. Ismail
2009-01-01
Full Text Available Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as the consequences of under or over forecasting the demand are serious and will affect all stakeholders in the electricity supply industry. Approach: If under estimated, the result is serious since plant installation cannot easily be advanced, this will affect the economy, business, loss of time and image. If over estimated, the financial penalty for excess capacity (i.e., over-estimated and wasting of resources. Therefore this study aimed to develop new forecasting model for forecasting electricity load demand which will minimize the error of forecasting. In this study, we explored the development of rule-based method for forecasting electricity peak load demand. The rule-based system synergized human reasoning style of fuzzy systems through the use of set of rules consisting of IF-THEN approximators with the learning and connectionist structure. Prior to the implementation of rule-based models, SARIMAT model and Regression time series were used. Results: Modification of the basic regression model and modeled it using Box-Jenkins auto regressive error had produced a satisfactory and adequate model with 2.41% forecasting error. With rule-based based forecasting, one can apply forecaster expertise and domain knowledge that is appropriate to the conditions of time series. Conclusion: This study showed a significant improvement in forecast accuracy when compared with the traditional time series model. Good domain knowledge of the experts had contributed to the increase in forecast accuracy. In general, the improvement will depend on the conditions of the data
Performance of fuzzy approach in Malaysia short-term electricity load forecasting
Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee
2014-12-01
Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in forecasting electricity load when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.
Electricity demand load forecasting of the Hellenic power system using an ARMA model
Pappas, S.Sp. [ASPETE - School of Pedagogical and Technological Education Department of Electrical Engineering Educators N. Heraklion, 141 21 Athens (Greece); Ekonomou, L.; Chatzarakis, G.E.; Skafidas, P.D. [ASPETE-School of Pedagogical and Technological Education, Department of Electrical Engineering Educators, N. Heraklion, 141 21 Athens (Greece); Karampelas, P. [Hellenic American University, IT Department, 12 Kaplanon Str., 106 80 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24 100 Kalamata (Greece); Katsikas, S.K. [University of Piraeus, Department of Technology Education and Digital Systems, 150 Androutsou St., 18 532 Piraeus (Greece)
2010-03-15
Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts. (author)
Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting
Nenad Floranović
2013-02-01
Full Text Available Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI has been recently proposed in regression tasks, mostly for feature selection and for identifying the real instances from training sets that contains noise and outliers. This paper proposes a methodology for the training set selection in a least squares support vector machines (LS-SVMs load forecasting model. A new application of the concept of MI is presented for the selection of a training set based on MI computation between initial training set instances and testing set instances. Accordingly, several LS-SVMs models have been trained, based on the proposed methodology, for hourly prediction of electric load for one day ahead. The results obtained from a real-world data set indicate that the proposed method increases the accuracy of load forecasting as well as reduces the size of the initial training set needed for model training.
Hybrid partial least squares and neural network approach for short-term electrical load forecasting
Shukang YANG; Ming LU; Huifeng XUE
2008-01-01
Intelligent systems and methods such as the neural network (NN) are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares (PLS) and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.
Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H
2016-01-01
Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.
Load forecasting of supermarket refrigeration
Rasmussen, Lisa Buth; Bacher, Peder; Madsen, Henrik
2016-01-01
This paper presents a novel study of models for forecasting the electrical load for supermarket refrigeration. The data used for building the models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village...... in Denmark. Every hour the hourly electrical load for refrigeration is forecasted for the following 42 h. The forecast models are adaptive linear time series models. The model has two regimes; one for opening hours and one for closing hours, this is modeled by a regime switching model and two different...
Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus
Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud
1996-01-01
The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the
Jaime Buitrago
2017-01-01
Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.
Matlab for Forecasting of Electric Power Load Based on BP Neural Network
Wang, Xi-Ping; Shi, Ming-Xi
Modeling and predicting electricity consumption play a vital role both in developed and developing countries for policy makers and related organizations. Improve load forecasting technology level is not only beneficial to plan power management and make reasonable construction plan, but also good for saving energy and reducing power cost, and then, it can improve the economic benefits and social benefit for power system. BP neural network is one of the most widely used neural networks and it has many advantages in the power load forecasting. Matlab has become the best technology application software which has been internationally recognized, the software has many characteristics, such as data visualization function and neural network toolbox, for these, it is the essential software when we do some research on neural network.
Short-Term Electrical Load Forecasting using Neuro-Fuzzy Models
Park, Young Jin; Shim, Hyun Jeong; Wang, Bo Hyeun [Kang Nung National University (Korea)
2000-03-01
This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models, The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptions, radial basis function networks, and neuro-fuzzy models without the structure learning. (author). 23 refs., 11 figs., 8 tabs.
Bozkurt, Ömer Özgür; Biricik, Göksel; Tayşi, Ziya Cihan
2017-01-01
Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.
A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems
Farshid Keynia
2011-03-01
Full Text Available Short-term load forecast (STLF is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.
Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts
Bidong Liu; Jakub Nowotarski; Tao Hong; Rafal Weron
2015-01-01
Majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a prac...
Yunxuan Dong
2017-04-01
Full Text Available The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.
Bao Wang
2012-11-01
Full Text Available The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA, generalized regression neural network (GRNN and regression model.
Day-Ahead Short-Term Forecasting Electricity Load via Approximation
Khamitov, R. N.; Gritsay, A. S.; Tyunkov, D. A.; E Sinitsin, G.
2017-04-01
The method of short-term forecasting of a power consumption which can be applied to short-term forecasting of power consumption is offered. The offered model is based on sinusoidal function for the description of day and night cycles of power consumption. Function coefficients - the period and amplitude are set up is adaptive, considering dynamics of power consumption with use of an artificial neural network. The presented results are tested on real retrospective data of power supply company. The offered method can be especially useful if there are no opportunities of collection of interval indications of metering devices of consumers, and the power supply company operates with electrical supply points. The offered method can be used by any power supply company upon purchase of the electric power in the wholesale market. For this purpose, it is necessary to receive coefficients of approximation of sinusoidal function and to have retrospective data on power consumption on an interval not less than one year.
Yan Hong Chen
2016-01-01
Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.
Load forecasting for supermarket refrigeration
Bacher, Peder; Madsen, Henrik; Aalborg Nielsen, Henrik
This report presents a study of models for forecasting the load for supermarket refrigeration. The data used for building the forecasting models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village...... in Denmark. The load for refrigeration is the sum of all cabinets in the supermarket, both low and medium temperature cabinets, and spans a period of one year. As input to the forecasting models the ambient temperature observed near the supermarket together with weather forecasts are used. Every hour...... the hourly load for refrigeration for the following 42 hours is forecasted. The forecast models are adaptive linear time-series models which are fitted with a computationally efficient recursive least squares scheme. The dynamic relations between the inputs and the load is modeled by simple transfer...
HYBRID AND INTEGRATED APPROACH TO SHORT TERM LOAD FORECASTING
J. P. Rothe,
2010-12-01
Full Text Available The forecasting of electricity demand has become one of the major research fields in Electrical Engineering. In recent years, much research has been carried out on the application of artificial intelligence techniques to the Load-Forecasting problem. Various Artificial Intelligence (AI techniques used for load forecasting are Expert systems, Fuzzy, Genetic Algorithm, Artificial Neural Network (ANN. This research work is an attempt to apply hybrid and ntegrated effort to forecast load. Regression, Fuzzy and Neural alongwith Genetic Algorithm will empower the analysts to strongly forecast fairly accurate load demand on hourly base.
Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br
2009-07-01
This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)
短期电力负荷预测研究%Study on short term electric load forecasting
许祎; 王世芳
2015-01-01
Power system load forecasting through the historical data analysis,forecast future demand. In this paper,we use wavelet clustering to load data.Then we use the classical genetic algorithm,Elman neural network,wavelet neural network and combined intelligent algorithm to build the forecasting model.By comparing the simulation results of several short-term power load forecasting models,the hybrid intelligent algorithm can greatly enhance the accuracy and reliability of the load forecasting results,and has good application prospect.%电力系统负荷预测通过对历史数据分析，预测未来需求。本文先用小波聚类对数据进行负荷分类，再分别用经典的遗传算法、Elman神经网络算法、小波-神经网络算法和组合智能算法建立预测模型。通过比较以上几种短期电力负荷预测模型的仿真结果，验证了混合智能算法可以大大增强负荷预测结果的准确性和可靠性，具有良好的应用前景。
Improving the Model for Energy Consumption Load Demand Forecasting
Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak
This paper proposes an application of a filter method in preprocessing stage for mid-term load demand forecasting to improve electricity load forecasting and to guarantee satisfactory forecasting accuracy. Case study employs the historical electricity consumption demand data in Thailand which were recorded in the 12 years of 1997 through to 2007. The load demand forecasted value is used for unit commitment and fuel reserve planning in the power system. This method consists of a trend component and a cyclical component decomposed from the original load demand using the Hodrick-Prescott (HP) filter in the preprocessing stage and the forecasting of each component using Double Neural Networks (DNNs) in the forecasting stage. Experimental results show that with preprocessing before forecasting can predict the load demand better than that without preprocessing.
A strategy for short-term load forecasting in Ireland
Fay, Damien
2004-01-01
Electric utilities require short-term forecasts of electricity demand (load) in order to schedule generating plant up to several days ahead on an hourly basis. Errors in the forecasts may lead to generation plant operation that is not required or sub-optimal scheduling of generation plants. In addition, with the introduction of the Electricity Regulation Act 1999, a deregulated market structure has been introduced, adding increased impetus to reducing forecast error and the associated costs. ...
Nantian Huang
2016-09-01
Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.
NASA Products to Enhance Energy Utility Load Forecasting
Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.
2012-01-01
Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.
A New Strategy for Short-Term Load Forecasting
Yi Yang
2013-01-01
Full Text Available Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.
Online load forecasting for supermarket refrigeration
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
2013-01-01
This paper presents a study of models for forecasting the load for supermarket refrigeration. The data used for building the forecasting models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village...... in Denmark. Every hour the hourly load for refrigeration for the following 42 hours is forecasted. The forecast models are time adaptive linear time-series models. The dynamic relations between the inputs and the load is modeled by simple transfer functions. The system operates in two regimes: one...
A Simple Hybrid Model for Short-Term Load Forecasting
Suseelatha Annamareddi
2013-01-01
Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.
Unsupervised/supervised learning concept for 24-hour load forecasting
Djukanovic, M. (Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia)); Babic, B. (Electrical Power Industry of Serbia, Belgrade (Yugoslavia)); Sobajic, D.J.; Pao, Y.-H. (Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Computer Science)
1993-07-01
An application of artificial neural networks in short-term load forecasting is described. An algorithm using an unsupervised/supervised learning concept and historical relationship between the load and temperature for a given season, day type and hour of the day to forecast hourly electric load with a lead time of 24 hours is proposed. An additional approach using functional link net, temperature variables, average load and last one-hour load of previous day is introduced and compared with the ANN model with one hidden layer load forecast. In spite of limited available weather variables (maximum, minimum and average temperature for the day) quite acceptable results have been achieved. The 24-hour-ahead forecast errors (absolute average) ranged from 2.78% for Saturdays and 3.12% for working days to 3.54% for Sundays. (Author)
Forecasting electricity usage using univariate time series models
Hock-Eam, Lim; Chee-Yin, Yip
2014-12-01
Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.
Comparison of Wind Power and Load Forecasting Error Distributions: Preprint
Hodge, B. M.; Florita, A.; Orwig, K.; Lew, D.; Milligan, M.
2012-07-01
The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.
Regression based peak load forecasting using a transformation technique
Haida, Takeshi; Muto, Shoichi (Tokyo Electric Power Co. (Japan). Computer and Communication Research Center)
1994-11-01
This paper presents a regression based daily peak load forecasting method with a transformation technique. In order to forecast the load precisely through a year, the authors should consider seasonal load change, annual load growth and the latest daily load change. To deal with these characteristics in the load forecasting, a transformation technique is presented. This technique consists of a transformation function with translation and reflection methods. The transformation function is estimated with the previous year's data points, in order that the function converts the data points into a set of new data points with preserving the shape of temperature-load relationships in the previous year. Then, the function is slightly translated so that the transformed data points will fit the shape of temperature-load relationships in the year. Finally, multivariate regression analysis with the latest daily loads and weather observations estimates the forecasting model. Large forecasting errors caused by the weather-load nonlinear characteristic in the transitional seasons such as spring and fall are reduced. Performance of the technique which is verified with simulations on actual load data of Tokyo Electric Power Company is also described.
Short Term Load Forecast Using Wavelet Neural Network
Gui Min; Rong Fei; Luo An
2005-01-01
This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impacts on load level were used in the proposed forecasting model. The model used the three-layer feed forward network trained by the error back-propagation algorithm. To enhance the forecasting accuracy by neural networks, wavelet multi-resolution analysis method was introduced to pre-process these data and reconstruct the predicted output. The proposed model has been evaluated with actual data of electricity load and temperature of Hunan Province. The simulation results show that the model is capable of providing a reasonable forecasting accuracy in STLF.
Deep Neural Network Based Demand Side Short Term Load Forecasting
Seunghyoung Ryu
2016-12-01
Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.
On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models
Fay, D; Ringwood, John; Condon, M.
2004-01-01
Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...
Short-term load forecasting of power system
Xu, Xiaobin
2017-05-01
In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.
Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks
Antonio J. Sanchez-Esguevillas
2013-03-01
Full Text Available Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc., which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN that performs Short-Term Load Forecasting (STLF. In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.
FORECASTING ELECTRICITY PRICES IN DEREGULATED WHOLESALE SPOT ELECTRICITY MARKET - A REVIEW
Girish Godekere Panchakshara Murthy,
2014-01-01
Full Text Available In the new framework of competitive electricity markets, all power market participants need accurate price forecasting tools. Electricity price forecasts characterize significant information that can help captive power producer, independent power producer, power generation companies, power distribution companies or open access consumers in careful planning of their bidding strategies for maximizing their profits, benefits and utilities from long term, medium term and short term perspective. Short term spot electricity price forecasting techniques are either inspired from electrical engineering literature (i.e. load forecasting or from economics literature (i.e. game theory models and the time-series econometric models. In this study we investigate the emergence of spot electricity markets with particular emphasis on Indian electricity market which has never been done before and review selected finance and econometrics inspired literature and models for forecasting electricity spot prices in deregulated wholesale spot electricity markets.
YOGESH K BICHPURIYA; S A SOMAN; A SUBRAMANYAM
2016-10-01
We present an empirical analysis to show that combination of short term load forecasts leads to better accuracy. We also discuss other aspects of combination, i.e.,distribution of weights, effect of variation in the historical window and distribution of forecast errors. The distribution of forecast errors is analyzed in order to get a robust forecast. We define a robust forecaster as one which has consistency in forecast accuracy, lesser shocks (outliers) and lower standard deviation in the distribution of forecast errors. We propose a composite ranking (CRank) scheme based on a composite score which considers three performance measures—standard deviation, kurtosis of distribution of forecast errors and accuracy of forecasts. The CRank helps in identification of a robust forecasts given a choice of individual and combined forecaster. The empirical analysis has been done with the real life data sets of two distribution companies in India.
Load forecasting method considering temperature effect for distribution network
Meng Xiao Fang
2016-01-01
Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.
Dynamic Hybrid Model for Short-Term Electricity Price Forecasting
Marin Cerjan
2014-05-01
Full Text Available Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.
Short term load forecasting of anomalous load using hybrid soft computing methods
Rasyid, S. A.; Abdullah, A. G.; Mulyadi, Y.
2016-04-01
Load forecast accuracy will have an impact on the generation cost is more economical. The use of electrical energy by consumers on holiday, show the tendency of the load patterns are not identical, it is different from the pattern of the load on a normal day. It is then defined as a anomalous load. In this paper, the method of hybrid ANN-Particle Swarm proposed to improve the accuracy of anomalous load forecasting that often occur on holidays. The proposed methodology has been used to forecast the half-hourly electricity demand for power systems in the Indonesia National Electricity Market in West Java region. Experiments were conducted by testing various of learning rate and learning data input. Performance of this methodology will be validated with real data from the national of electricity company. The result of observations show that the proposed formula is very effective to short-term load forecasting in the case of anomalous load. Hybrid ANN-Swarm Particle relatively simple and easy as a analysis tool by engineers.
Electric power systems advanced forecasting techniques and optimal generation scheduling
Catalão, João P S
2012-01-01
Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie
George P. Papaioannou
2016-08-01
Full Text Available In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC technique and the traditional multiple regression (PC-regression, for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN and Support Vector Machines (SVM. Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.
Supplier Short Term Load Forecasting Using Support Vector Regression and Exogenous Input
Matijaš, Marin; Vukićcević, Milan; Krajcar, Slavko
2011-09-01
In power systems, task of load forecasting is important for keeping equilibrium between production and consumption. With liberalization of electricity markets, task of load forecasting changed because each market participant has to forecast their own load. Consumption of end-consumers is stochastic in nature. Due to competition, suppliers are not in a position to transfer their costs to end-consumers; therefore it is essential to keep forecasting error as low as possible. Numerous papers are investigating load forecasting from the perspective of the grid or production planning. We research forecasting models from the perspective of a supplier. In this paper, we investigate different combinations of exogenous input on the simulated supplier loads and show that using points of delivery as a feature for Support Vector Regression leads to lower forecasting error, while adding customer number in different datasets does the opposite.
Forecasting residential electricity demand in provincial China.
Liao, Hua; Liu, Yanan; Gao, Yixuan; Hao, Yu; Ma, Xiao-Wei; Wang, Kan
2017-03-01
In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016-2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016-2020, and populous provinces such as Guangdong will be the main contributors to the increments.
Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR for Load Forecasting
Cheng-Wen Lee
2016-10-01
Full Text Available Hybridizing chaotic evolutionary algorithms with support vector regression (SVR to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.
Saravanan, S; Kannan, S.; C. Thangaraj
2012-01-01
Power System planning starts with Electric load (demand) forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount...
A simple forecasting model for industrial electric energy consumption
Al-Shehri, Abdallah [King Fahd Univ. of Petroleum and Minerals, Electrical Engineering Dept., Dhaharan (Saudi Arabia)
2000-07-01
A single-equation model is developed and employed for forecasting industrial electric energy consumption in the Saudi Consolidated Electric Company in the Eastern Province (SCECO-East) of Saudi Arabia. SCECO-East's industrial loads are composed mainly of oil-related and petrochemical industries. Even though industrial loads are generally characterised by their steadiness, the harsh weather conditions of the Eastern Province cause great variations in the industrial electric energy consumption at SCECO-East. The developed model reflects these variations. MATLAB is used to solve the model. (Author)
Kim, K.H. [Kangwon National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Park, J.K. [Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Hwang, K.J. [Univ. of Ulsan (Korea, Republic of). Dept. of Electrical Engineering; Kim, S.H. [Korea Electric Power Co., Seoul (Korea, Republic of). Power System Control Dept.
1995-08-01
In this paper, a hybrid model for short-term load forecast that integrates artificial neural networks and fuzzy expert systems is presented. The forecasted load is obtained by passing through two steps. In the first procedure, the artificial neural networks are trained with the load patterns corresponding to the forecasting hour, and the provisional forecasted load is obtained by the trained artificial neural networks. In the second procedure, the fuzzy expert systems modify the provisional forecasted load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. In the test case of 1994 for implementation in short term load forecasting expert system of Korea Electric Power Corporation (KEPCO), the proposed hybrid model provided good forecasting accuracy of the mean absolute percentage errors below 1.3%. The comparison results with exponential smoothing method showed the efficiency and accuracy of the hybrid model.
Short-term heat load forecasting for single family houses
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg;
2013-01-01
This paper presents a method for forecasting the load for space heating in a single-family house. The forecasting model is built using data from sixteen houses located in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each ...
基于多因素加法模型的中期电力负荷预测%Multiple Factors Addictive Model for Mid-Term Electric Load Forecasting
翁金芳; 黄伟; 江育娥; 林劼
2016-01-01
提前准确预测所需电力负荷,做好电力规划是电力部门保证电力供应稳定不可或缺的重要环节.基于欧洲智能网络(EUNITE)竞赛电力数据和北美电力数据,提出一种多因素加法模型,进行中期电力预测.考虑到温度、假期、星期等因素对电力负荷产生不同的影响,拟合出这些因素与电力负荷之间的映射关系,相加得到电力负荷预测的函数.还比较了业界常用的7种不同的算法模型,使用6种不同指标对这些模型和多因素加法模型进行评估,实验结果发现,在这8种不同算法模型中,多因素加法模型有着更加精确的预测性能,运算速度比其他模型快,并且模型更加容易理解和解释.%Accuracy forecasting of electric load is important for power system to make plan. A Multiple Factors Addictive (MFA) model is proposed to predict mid-term electric load based on Europe (EUNITE) competition dataset and North American electric dataset. Firstly, MFA considers factors such as temperature, holiday, and week separately to fit functions for electric loads. And then all these fitted functions are added together to a unified function, which is used to make prediction of the electric load. Seven other state-of-art algorithms which are popular in the field are also used to make forecasting. The performances of prediction models are evaluated by using 6 different metrics. Compared with 7 other kinds of different models prediction results, MFA has the advantages of more accurate forecasting performance and faster operational speed, and is simple and easy to understand.
Electrical load detection aparatus
2010-01-01
A load detection technique for a load comprising multiple frequency-dependant sub-loads comprises measuring a representation of the impedance characteristic of the load; providing stored representations of a multiplicity of impedance characteristics of the load; each one of the stored representat...
Forecasting Electricity Spot Prices Accounting for Wind Power Predictions
Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg
2013-01-01
-varying regression model. In a second step, time-series models, i.e., ARMA and Holt–Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pool's Elspot, during a two......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...
大数据背景下的充电站负荷预测方法%Load Forecasting Method for Electric Vehicle Charging Station Based on Big Data
黄小庆; 陈颉; 陈永新; 杨夯; 曹一家; 江磊
2016-01-01
The load forecast of electric vehicles (EVs) is the foundation of planning and scheduling of charging stations. Compared with the traditional method,the load forecast method under big data has the feature that the data to be forecast is quickly observable,real time,etc.Hence the need of the corresponding adjustments of the load forecast methods.This paper first analyzes the data demand for charging station planning and scheduling,and then the ways of main data acquisition.Based on volume,variety,velocity data,each EV”s start time,duration and location for charging,it will be possible to build the load model of a single EV.Furthermore,the total charging power of a charging station can be estimated by origin-destination(OD) flow statistics or adding up all the EV loads that are connected with its related transport line and node.Finally,a case study is given around the load forecast of EV station,and the load forecasting results from different load forecasting methods are compared.%电动汽车负荷预测是充电站规划及调度的研究基础。相比传统的负荷预测，大数据背景下的负荷预测具有待预测数据可快速观测的特点，此时负荷预测方法需要相应调整。首先分析了充电站负荷预测所需数据及主要数据来源。其次，针对单辆电动汽车，基于大量、快速更新、多种类的数据分析电动汽车的充电习惯，预测每一辆电动汽车的充电开始时间、持续时间和充电地点，获取单辆电动汽车的负荷模型。该模型综合考虑电池状态、出行时间、行驶路径与速度、充电偏好等信息。然后，面向任意充电站，对与其相关的路网节点与交通线路上的所有电动汽车负荷求和，估算该充电站的总充电功率。最后，进行实例仿真，并与传统方法下的充电负荷预测结果进行了对比。
傅军栋; 刘晶; 喻勇
2015-01-01
The accuracy of annual electric load forecasting plays an important role in economic and social benefits of electric power systems. The Gray Neural Network is an innovative computing approach, which has found wide application in reality. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a GNN-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the parameters for the GNN model to improve the forecasting accuracy and stability of the model. By taking the annual electricity consumption of China as an in⁃stance, the computational result shows that the GNN combined with FOA outperforms other alternative methods, namely the single GNN, the generalized regression neural network, the least squares support vector machine (LSSUM) and the regression model.%年电力负荷预测的准确性对电力系统的经济效益和社会效益具有重要作用。灰色神经网络（GNN）是一种创新的智能计算方法，在实际中广泛应用。尤其在预测问题方面具有极大的潜力。作为一种新型的启发式和进化算法，果蝇优化算法（FOA）具有易理解和快速收敛到全局最优解的优点。为提高预测性能，提出一种以GNN为基础的年电力负荷预测模型，使用FOA自动确定GNN模型的相应参数值，提高模型的稳定性和预测精度。通过利用中国的年用电量为实例，计算结果表明，GNN结合FOA（GNN-FOA）优于GNN ，广义回归神经网络（GRNN），最小二乘支持向量机（LSSVM）和回归模型等其他替代方法。
2010-01-01
... forecast. Examples of internal uses include providing information for developing or monitoring demand side management programs, supply resource planning, load flow studies, wholesale power marketing, retail...
A Novel Hybrid Method for Short-Term Power Load Forecasting
Huang Yuansheng
2016-01-01
Full Text Available Influenced by many uncertain and random factors, nonstationary, nonlinearity, and time-variety appear in power load series, which is difficult to forecast accurately. Aiming at locating these issues of power load forecasting, an innovative hybrid method is proposed to forecast power load in this paper. Firstly, ensemble empirical mode decomposition (EEMD is used to decompose the power load series into a series of independent intrinsic mode functions (IMFs and a residual term. Secondly, genetic algorithm (GA is then applied to determine the best weights of each IMF and the residual term named ensemble empirical mode decomposition based on weight (WEEMD. Thirdly, least square support vector machine (LSSVM and nonparametric generalized autoregressive conditional heteroscedasticity (NPGARCH are employed to forecast the subseries, respectively, based on the characteristics of power load series. Finally, the forecasted power load of each component is summed as the final forecasted result of power load. Compared with other methods, the forecasting results of this proposed model applied to the electricity market of Pennsylvania-New Jersey-Maryland (PJM indicate that the proposed model outperforms other models.
Valgas, Helio Moreira; Pinto, Roberto del Giudice R.; Franca, Carlos [Companhia Energetica de Minas Gerais (CEMIG), Belo Horizonte, MG (Brazil); Lambert-Torres, Germano; Silva, Alexandre P. Alves da; Pires, Robson Celso; Costa Junior, Roberto Affonso [Escola Federal de Engenharia de Itajuba, MG (Brazil)
1994-12-31
Accurate dynamic load models allow more precise calculations of power system controls and stability limits, which are critical mainly in the operation planning of power systems. This paper describes the development of a computer program (software) for static and dynamic load model studies using the measurement approach for the CEMIG system. Two dynamic load model structures are developed and tested. A procedure for applying a set of measured data from an on-line transient recording system to develop load models is described. (author) 6 refs., 17 figs.
Support vector machine for day ahead electricity price forecasting
Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti
2015-05-01
Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.
AR-based Algorithms for Short Term Load Forecast
Zuhairi Baharudin
2014-02-01
Full Text Available Short-term load forecast plays an important role in planning and operation of power systems. The accuracy of the forecast value is necessary for economically efficient operation and effective control of the plant. This study describes the methods of Autoregressive (AR Burg’s and Modified Covariance (MCOV in solving the short term load forecast. Both algorithms are tested with power load data from Malaysian grid and New South Wales, Australia. The forecast accuracy is assessed in terms of their errors. For the comparison the algorithms are tested and benchmark with the previous successful proposed methods.
Electricity forecasting on the individual household level enhanced based on activity patterns.
Gajowniczek, Krzysztof; Ząbkowski, Tomasz
2017-01-01
Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents' daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.
Residential Saudi load forecasting using analytical model and Artificial Neural Networks
Al-Harbi, Ahmad Abdulaziz
In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.
A Statistical Approach for Interval Forecasting of the Electricity Price
Zhao, Jun Hua; Dong, Zhao Yang; Xu, Zhao
2008-01-01
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......Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting...... the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value...
Yukita, Kazuto; Kato, Shinya; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Kawashima, Yasuhiro
Recently, the independent power producers (IPPs) and the distributed power generations (DGs) are increase on by the electric power system with the power system deregulation. And the power system becomes more complicated. It is necessary to carry out the electric power demand forecasting in order to the power system is operated for the high economical and the high-efficient. For the improvement of electric power demand forecasting, many methods, such as the methods using fuzzy theory, neural network and SDP data, are proposed. In this paper, we proposed the method using STROGANOFF (STructured Re-presentation on Genetic Algorithms for Non-linear Function Fitting) that approximate the value of predictive to the future data by the past data is obtained. Also, the weather condition was considered for the forecasting that is improvement, and the daily peak load forecasting in next day on Chubu district in Japan was carried out, and the effectiveness of proposed method was examined.
电力用户侧大数据分析与并行负荷预测%Big Data Analysis and Parallel Load Forecasting of Electric Power User Side
王德文; 孙志伟
2015-01-01
With the development of smart grids, communication network and sensor technology, the electric power user side data is growing exponentially, more complexi, and gradually forms the big data of electric power user side. Now the traditional data analysis model can’t meet the demand of big data, so a new data analysis model aiming at analyzing and processing big data of power user side is urgently necessary. The source of the big data of electric power user side is analyzed in this paper. Those challenges facing data storage, availability, processing of the power user side are pointed out based on volume, variety and speed and other characteristics of the big data. Combining cloud computing technology, an analysis and processing platform of big data of electric power user side is given, which integrates smart meter data, SCADA systems data and various sensors data to be processed by MapReduce or Spark. A load forecasting method based on parallel random forests algorithm is proposed. Parallelization random forest algorithm is used to analyze data, such as load data, temperature, wind speed. The method shortens the time of load forecasting and improves random forests algorithm on data processing capability. Parallel load forecasting prototype system of electric power users side big data based on Hadoop is designed and implemented, including cluster management, data management, predictive classification algorithms library functions and so on. By using data sets of different sizes to do load forecasting experiment with parallelization random forest algorithm, the experiment results show that the prediction accuracy of the parallel random forest algorithm is significant higher than that of the decision tree. The prediction accuracy of different data sets is generally higher than the forecast accuracy of the decision tree, and applying the parallel random forest algorithm to analyze and processing big data is a better choice.%随着智能电网、通信网络技术和传
Incorporating weather uncertainty in demand forecasts for electricity market planning
Ziser, C. J.; Dong, Z. Y.; Wong, K. P.
2012-07-01
A major component of electricity network planning is to ensure supply capability into the future, through generation and transmission development. Accurate forecasts of maximum demand are a crucial component of this process, with future weather conditions having a large impact on forecast accuracy. This article presents an improved methodology for the consideration of weather uncertainty in electricity demand forecasts. Case studies based on the Australian national electricity market are used to validate the proposed methodology.
牛东晓; 刘达; 邢棉
2008-01-01
A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
A hybrid approach for probabilistic forecasting of electricity price
Wan, Can; Xu, Zhao; Wang, Yelei
2014-01-01
The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due...... to probabilistic interval forecasts can be of great importance to quantify the uncertainties of potential forecasts, thus effectively supporting the decision making activities against uncertainties and risks ahead. This paper proposes a hybrid approach to construct prediction intervals of MCPs with a two...... 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....
Short-term heat load forecasting for single family houses
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
2013-01-01
This paper presents a method for forecasting the load for space heating in a single-family house. The forecasting model is built using data from sixteen houses located in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each...... characteristics for each house, such as the level of adaptivity and the thermal dynamical response of the building, which is modeled with simple transfer functions. Identification of a model, which is suitable for all the houses, is carried out. The results show that the one-step ahead errors are close to white...
7 CFR 1710.209 - Approval requirements for load forecast work plans.
2010-01-01
... 7 Agriculture 11 2010-01-01 2010-01-01 false Approval requirements for load forecast work plans... LOANS AND GUARANTEES Load Forecasts § 1710.209 Approval requirements for load forecast work plans. (a... utility plant of $500 million or more must maintain an approved load forecast work plan. RUS...
Electricity demand forecasting using regression, scenarios and pattern analysis
Khuluse, S
2009-02-01
Full Text Available The objective of the study is to forecast national electricity demand patterns for a period of twenty years: total annual consumption and understanding seasonal effects. No constraint on the supply of electricity was assumed...
Impact of festival factor on electric quantity multiplication forecast model
无
2008-01-01
This research aims to improve the forecasting precision of electric quantity. It is discovered that the total electricity consumption considerably increased during the Spring Festival by the analysis of the electric quantity time series from 2002 to 2007 in Shandong province. The festival factor is ascertained to be one of the important seasonal factors affecting the electric quantity fluctuations, and the multiplication model for forecasting is improved by introducing corresponding variables and parameters...
Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak
2010-02-01
This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.
Modelling and forecasting Turkish residential electricity demand
Dilaver, Zafer, E-mail: Z.dilaver@surrey.ac.uk [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom); The Republic of Turkey Prime Ministry, PK 06573, Ankara (Turkey); Hunt, Lester C [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom)
2011-06-15
This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being -0.09 and -0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008. - Research Highlights: > Estimated short run and long run expenditure elasticities of 0.38 and 1.57, respectively. > Estimated short run and long run price elasticities of -0.09 and -0.38, respectively. > Estimated UEDT has increasing (i.e. energy using) and decreasing (i.e. energy saving) periods. > Predicted Turkish residential electricity demand between 48 and 80 TWh in 2020.
Improved Neural Networks with Random Weights for Short-Term Load Forecasting.
Kun Lang
Full Text Available An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW. The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.
Improved Neural Networks with Random Weights for Short-Term Load Forecasting.
Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo
2015-01-01
An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.
Hongze Li
2014-01-01
Full Text Available Short-term power load forecasting is one of the most important issues in the economic and reliable operation of electricity power system. Taking the characteristics of randomness, tendency, and periodicity of short-term power load into account, a new method (SSA-AR model which combines the univariate singular spectrum analysis and autoregressive model is proposed. Firstly, the singular spectrum analysis (SSA is employed to decompose and reconstruct the original power load series. Secondly, the autoregressive (AR model is used to forecast based on the reconstructed power load series. The employed data is the hourly power load series of the Mid-Atlantic region in PJM electricity market. Empirical analysis result shows that, compared with the single autoregressive model (AR, SSA-based linear recurrent method (SSA-LRF, and BPNN (backpropagation neural network model, the proposed SSA-AR method has a better performance in terms of short-term power load forecasting.
Statistical approaches to short-term electricity forecasting
Kellova, Andrea
The study of the short-term forecasting of electricity demand has played a key role in the economic optimization of the electric energy industry and is essential for power systems planning and operation. In electric energy markets, accurate short-term forecasting of electricity demand is necessary mainly for economic operations. Our focus is directed to the question of electricity demand forecasting in the Czech Republic. Firstly, we describe the current structure and organization of the Czech, as well as the European, electricity market. Secondly, we provide a complex description of the most powerful external factors influencing electricity consumption. The choice of the most appropriate model is conditioned by these electricity demand determining factors. Thirdly, we build up several types of multivariate forecasting models, both linear and nonlinear. These models are, respectively, linear regression models and artificial neural networks. Finally, we compare the forecasting power of both kinds of models using several statistical accuracy measures. Our results suggest that although the electricity demand forecasting in the Czech Republic is for the considered years rather a nonlinear than a linear problem, for practical purposes simple linear models with nonlinear inputs can be adequate. This is confirmed by the values of the empirical loss function applied to the forecasting results.
Hybrid artificial neural network system for short-term load forecasting
Ilić Slobodan A.
2012-01-01
Full Text Available This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF. The system comprises of two Artificial Neural Networks (ANN, assembled in a hierarchical order. The first ANN is a Multilayer Perceptron (MLP which functions as integrated load predictor (ILP for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor (HLP for a forecasting day. By using a separate ANN that predicts the integral of the load (ILP, additional information is presented to the actual forecasting ANN (HLP, while keeping its input space relatively small. This property enables online training and adaptation, as new data become available, because of the short training time. Different sizes of training sets have been tested, and the optimum of 30 day sliding time-window has been determined. The system has been verified on recorded data from Serbian electrical utility company. The results demonstrate better efficiency of the proposed method in comparison to non-hybrid methods because it produces better forecasts and yields smaller mean average percentage error (MAPE.
Moriano, Javier; Rodríguez, Francisco Javier; Martín, Pedro; Jiménez, Jose Antonio; Vuksanovic, Branislav
2016-01-12
In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected.
Javier Moriano
2016-01-01
Full Text Available In recent years, Secondary Substations (SSs are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected.
A Top-Down Spatially Resolved Electrical Load Model
Martin Robinius
2017-03-01
Full Text Available The increasing deployment of variable renewable energy sources (VRES is changing the source regime in the electrical energy sector. However, VRES feed-in from wind turbines and photovoltaic systems is dependent on the weather and only partially predictable. As a result, existing energy sector models must be re-evaluated and adjusted as necessary. In long-term forecast models, the expansion of VRES must be taken into account so that future local overloads can be identified and measures taken. This paper focuses on one input factor for electrical energy models: the electrical load. We compare two different types to describe this, namely vertical grid load and total load. For the total load, an approach for a spatially-resolved electrical load model is developed and applied at the municipal level in Germany. This model provides detailed information about the load at a quarterly-hour resolution across 11,268 German municipalities. In municipalities with concentrations of energy-intensive industry, high loads are expected, which our simulation reproduces with a good degree of accuracy. Our results also show that municipalities with energy-intensive industry have a higher simulated electric load than neighboring municipalities that do not host energy-intensive industries. The underlying data was extracted from publically accessible sources and therefore the methodology introduced is also applicable to other countries.
Modeling and forecasting electricity price jumps in the Nord Pool power market
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...... i) price drivers, ii) persistence, iii) seasonality of electricity prices. The models are shown to outperform commonly-used benchmark. The paper shows how crucial for price jumps forecasting is to incorporate additional knowledge on price drivers like loads, temperature and water reservoir level...
Electricity Price Forecasting Based on AOSVR and Outlier Detection
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.
Chaotic Load Series Forecasting Based on MPMR
Liu Zunxiong; Cheng Quanhua; Zhang Deyun
2006-01-01
Minimax probability machine regression (MPMR) was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε to the true regression function. After exploring the principle of MPMR, and verifying the chaotic property of the load series from a certain power system, one-day-ahead predictions for 24 time points next day were done with MPMR. The results demonstrate that MPMP has satisfactory prediction efficiency. Kernel function shape parameter and regression tube value may influence the MPMR-based system performance. In the experiments, cross validation was used to choose the two parameters.
Palchak, David
Electrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.
Model Predictive Load Scheduling Using Solar Power Forecasting
Habib, Abdulelah H.; Kleissl, Jan; de Callafon, Raymond A.
2016-01-01
In this paper a model is developed to solve the on/off scheduling of (non-linear) dynamic electric loads based on predictions of the power delivery of a (standalone) solar power source. Knowledge of variations in the solar power output is used to optimally select the timing and the combinations of a set of given electric loads, where each load has a desired dynamic power profile. The optimization exploits the desired power profiles of the electric loads in terms of dynamic power ramp up/down ...
Short-term load and wind power forecasting using neural network-based prediction intervals.
Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas
2014-02-01
Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.
Short-term Power Load Forecasting Based on Gray Theory
Cui Herui
2013-11-01
Full Text Available Power load forecasting provides the basis for the preparation of power planning, especially the accurate short-term power load forecasting. It can formulate power rationing program of area load reliably and timely, to maintain the normal production and life. This article describes the gray prediction method, and improves GM (1,1 model via processing the original data sequence smoothly, using the correction model of parameteramending parameter values, adding the residual model, and also applying the idea of the metabolism. It conducts an empirical analysis of the 10KV large cable of Guigang Power Supply Bureau in Nan Ping, and verifies the limitations of ordinary gray theory. The improved gray model has a higher prediction accuracy than the conventional GM (1,1 model.
Comparisons of Short Term Load Forecasting using Artificial Neural Network and Regression Method
Rajesh Deshmukh
2011-12-01
Full Text Available In power systems the next day’s power generation must be scheduled every day, day ahead short-term load forecasting (STLF is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peaking units. On the other hand, over prediction of STLF leads to the unnecessarily large reserve capacity, which is also related to high operating cost. the research work in this area is still a challenge to the electrical engineering scholars because of its high complexity. How to estimate the future load with the historical data has remained a difficulty up to now, especially for the load forecasting of holidays, days with extreme weather and other anomalous days. With the recent development of new mathematical, data mining and artificial intelligence tools, it is potentially possible to improve the forecasting result. This paper presents a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network. Correlation analysis of weather data determines the input parameters of the neural networks. And its results compare to regression method.
HUSSEIN A. ABDULQADER
2012-08-01
Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.
Electric load management and energy conservation
Kheir, N. A.
1976-01-01
Electric load management and energy conservation relate heavily to the major problems facing power industry at present. The three basic modes of energy conservation are identified as demand reduction, increased efficiency and substitution for scarce fuels. Direct and indirect load management objectives are to reduce peak loads and have future growth in electricity requirements in such a manner to cause more of it to fall off the system's peak. In this paper, an overview of proposed and implemented load management options is presented. Research opportunities exist for the evaluation of socio-economic impacts of energy conservation and load management schemes specially on the electric power industry itself.
Jason Grant
2014-03-01
Full Text Available The power output capacity of a local electrical utility is dictated by its customers’ cumulative peak-demand electrical consumption. Most electrical utilities in the United States maintain peak-power generation capacity by charging for end-use peak electrical demand; thirty to seventy percent of an electric utility’s bill. To reduce peak demand, a real-time energy monitoring system was designed, developed, and implemented for a large government building. Data logging, combined with an application of artificial neural networks (ANNs, provides short-term electrical load forecasting data for controlled peak demand. The ANN model was tested against other forecasting methods including simple moving average (SMA, linear regression, and multivariate adaptive regression splines (MARSplines and was effective at forecasting peak building electrical demand in a large government building sixty minutes into the future. The ANN model presented here outperformed the other forecasting methods tested with a mean absolute percentage error (MAPE of 3.9% as compared to the SMA, linear regression, and MARSplines MAPEs of 7.7%, 17.3%, and 7.0% respectively. Additionally, the ANN model realized an absolute maximum error (AME of 8.2% as compared to the SMA, linear regression, and MARSplines AMEs of 26.2%, 45.1%, and 22.5% respectively.
Weide Li
2017-01-01
Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.
Forecasting the electricity consumption of the Mexican border states maquiladoras
Flores, C.E.; Phelan, P.E. [Arizona State Univ., Dept. of Mechanical and Aerospace Engineering, Tempe, AZ (United States); Mou, J.-I. [Taiwan Semiconductor Manufacturing Co., Operation Planning Div., Hsin-Chu (Taiwan); Bryan, H. [Arizona State Univ., School of Architecture, Tempe, AZ (United States)
2004-07-01
The consumption of electricity by maquiladora industries in the Mexican border states is an important driver for determining future powerplant needs in that area. An industrial electricity forecasting model is developed for the border states' maquiladoras, and the outputs are compared with a reference forecasting model developed for the US industrial sector, for which considerably more data are available. This model enables the prediction of the effect of implementing various energy efficiency measures in the industrial sector. As an illustration, here the impact of implementing energy-efficient lighting and motors in the Mexican border states' maquiladoras was determined to be substantial. Without such energy efficiency measures, electricity consumption for these industries is predicted to rise by 64% from 2001 to 2010, but if these measures are implemented on a gradual basis over the same time period, electricity consumption is forecast to rise by only 36%. (Author)
Temperature and seasonality influences on Spanish electricity load
Pardo, Angel; Meneu, Vicente [Departamento de Economia Financiera y Matematica, Facultad de Economia, Avda. de los Naranjos s/n., Edificio Departamental Oriental, Universidad de Valencia, 46022 Valencia (Spain); Valor, Enric [Departamento de Termodinamica, Universidad de Valencia, 46100 Burjassot, Valencia (Spain)
2002-01-01
Deregulation of the Spanish electricity market in 1998 and the possible listing of electricity or weather derivative contracts have encouraged the study of the relationship between electricity demand and weather in Spain. In this paper, a transfer function intervention model is developed for forecasting daily electricity load from cooling and heating degree-days. The influence of weather and seasonality is proved, and is significant even when the autoregressive effects and the dynamic specification of the temperature are taken into account. The estimated general model shows a high predictive power. The results and information presented in this paper could be of interest for current users and potential traders in the deregulated Spanish electricity market.
Short-term load forecast using trend information and process reconstruction
Santos, P.J.; Pires, A.J.; Martins, J.F. [Instituto Politecnico de Setubal (Portugal). Dept. of Electrical Engineering; Martins, A.G. [University of Coimbra (Portugal). Dept. of Electrical Engineering; Mendes, R.V. [Instituto Superior Tecnico, Lisboa (Portugal). Laboratorio de Mecatronica
2005-07-01
The algorithms for short-term load forecast (STLF), especially within the next-hour horizon, belong to a group of methodologies that aim to render more effective the actions of planning, operating and controlling electric energy systems (EES). In the context of the progressive liberalization of the electricity sector, unbundling of the previous monopolistic structure emphasizes the need for load forecast, particularly at the network level. Methodologies such as artificial neural networks (ANN) have been widely used in next-hour load forecast. Designing an ANN requires the proper choice of input variables, avoiding overfitting and an unnecessarily complex input vector (IV). This may be achieved by trying to reduce the arbitrariness in the choice of endogenous variables. At a first stage, we have applied the mathematical techniques of process-reconstruction to the underlying stochastic process, using coding and block entropies to characterize the measure and memory range. At a second stage, the concept of consumption trend in homologous days of previous weeks has been used. The possibility to include weather-related variables in the IV has also been analysed, the option finally being to establish a model of the non-weather sensitive type. The paper uses a real-life case study. (author)
Load research and load estimation in electricity distribution
Seppaelae, A. [VTT Energy, Espoo (Finland). Energy Systems
1996-12-31
The topics introduced in this thesis are: the Finnish load research project, a simple form customer class load model, analysis of the origins of customers load distribution, a method for the estimation of the confidence interval of customer loads and Distribution Load Estimation (DLE) which utilises both the load models and measurements from distribution networks. The Finnish load research project started in 1983. The project was initially coordinated by the Association of Finnish Electric Utilities and 40 utilities joined the project. Now there are over 1000 customer hourly load recordings in a database. A simple form customer class load model is introduced. The model is designed to be practical for most utility applications and has been used by the Finnish utilities for several years. The only variable of the model is the customers annual energy consumption. The model gives the customers average hourly load and standard deviation for a selected month, day and hour. The statistical distribution of customer loads is studied and a model for customer electric load variation is developed. The model results in a lognormal distribution as an extreme case. Using the `simple form load model`, a method for estimating confidence intervals (confidence limits) of customer hourly load is developed. The two methods selected for final analysis are based on normal and lognormal distribution estimated in a simplified manner. The estimation of several cumulated customer class loads is also analysed. Customer class load estimation which combines the information from load models and distribution network load measurements is developed. This method, called Distribution Load Estimation (DLE), utilises information already available in the utilities databases and is thus easy to apply
Schroedter-Homscheidt, Marion; Oumbe, Armel; Benedetti, Angela; Morcrette, Jean-Jacques
2013-01-01
The potential for transferring a larger share of our energy supply toward renewable energy is a widely discussed goal in society, economics, environment, and climate-related programs. For a larger share of electricity to come from fluctuating solar and wind energy-based electricity, production forecasts are required to ensure successful grid integration. Concentrating solar power holds the potential to make the fluctuating solar electricity a dispatchable resource by using both heat storage s...
Price forecasting of day-ahead electricity markets using a hybrid forecast method
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.
A neutral network based technique for short-term forecasting of anomalous load periods
Sforna, M. [ENEL, s.p.a, Italian Power Company (Italy); Lamedica, R.; Prudenzi, A. [Rome Univ. `La Sapienza`, Rome (Italy); Caciotta, M.; Orsolini Cencelli, V. [Rome Univ. III, Rome (Italy)
1995-01-01
The paper illustrates a part of the research activity conducted by authors in the field of electric Short Term Load Forecasting (STLF) based on Artificial Neural Network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architecture provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to `anomalous` load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen`s Self Organizing Map (SOM). The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer percept ron with a back propagation learning algorithm similar to the ones above mentioned. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations.
A method for short term electricity spot price forecasting
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
Pituk Bunnoon
2013-08-01
Full Text Available The multi-point values of an appropriate smoothing parameter of HP-filter algorithm for midterm electricity load demand (MELD forecasting are proposed. The case study employs the data based on the organization of the Electricity Generating Authority of Thailand (EGAT. The research shows the growth at rate of weather and economic factors influencing to the electricity demand. The main focus of the article proposes the multi-point values of smoothing parameter, and also uses the appropriate values or better smoothing parameter of HP-filter for separating the electricity load demand (kWh signal based on preprocessing stage. The method used for forecasting is an artificial neural network. Also, these approaches show the best results of in forecasting. As the result, the multi-point values of smoothing parameters of the research can be improved the accuracy of the electricity demand forecasting.
An Electrical Energy Consumption Monitoring and Forecasting System
J. L. Rojas-Renteria
2016-10-01
Full Text Available Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.
Sharing wind power forecasts in electricity markets: A numerical analysis
Exizidis, Lazaros; Pinson, Pierre; Kazempour, Jalal
2016-01-01
In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day......-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding...... flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making...
Singh, Navneet K.; Singh, Asheesh K.; Tripathy, Manoj
2012-05-01
For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.
Modelling and forecasting electricity price variability
Haugom, Erik
2012-07-01
The liberalization of electricity sectors around the world has induced a need for financial electricity markets. This thesis is mainly focused on calculating, modelling, and predicting volatility for financial electricity prices. The four first essays examine the liberalized Nordic electricity market. The purposes in these papers are to describe some stylized properties of high-frequency financial electricity data and to apply models that can explain and predict variation in volatility. The fifth essay examines how information from high-frequency electricity forward contracts can be used in order to improve electricity spot-price volatility predictions. This essay uses data from the Pennsylvania-New Jersey-Maryland wholesale electricity market in the U.S.A. Essay 1 describes some stylized properties of financial high-frequency electricity prices, their returns and volatilities at the Nordic electricity exchange, Nord Pool. The analyses focus on distribution properties, serial correlation, volatility clustering, the influence of extreme events and seasonality in the various measures. The objective of Essay 2 is to calculate, model, and predict realized volatility of financial electricity prices for quarterly and yearly contracts. The total variation is also separated into continuous and jump variation. Various market measures are also included in the models in order potentially to improve volatility predictions. Essay 3 compares day-ahead predictions of Nord Pool financial electricity price volatility obtained from a GARCH approach with those obtained using standard time-series techniques on realized volatility. The performances of a total of eight models (two representing the GARCH family and six representing standard autoregressive models) are compared and evaluated. Essay 4 examines whether predictions of day-ahead and week-ahead volatility can be improved by additionally including volatility and covariance effects from related financial electricity contracts
Using Seasonal Forecasts for medium-term Electricity Demand Forecasting on Italy
De Felice, M.; Alessandri, A.; Ruti, P.
2012-12-01
Electricity demand forecast is an essential tool for energy management and operation scheduling for electric utilities. In power engineering, medium-term forecasting is defined as the prediction up to 12 months ahead, and commonly is performed considering weather climatology and not actual forecasts. This work aims to analyze the predictability of electricity demand on seasonal time scale, considering seasonal samples, i.e. average on three months. Electricity demand data has been provided by Italian Transmission System Operator for eight different geographical areas, in Fig. 1 for each area is shown the average yearly demand anomaly for each season. This work uses data for each summer during 1990-2010 and all the datasets have been pre-processed to remove trends and reduce the influence of calendar and economic effects. The choice of focusing this research on the summer period is due to the critical peaks of demand that power grid is subject during hot days. Weather data have been included considering observations provided by ECMWF ERA-INTERIM reanalyses. Primitive variables (2-metres temperature, pressure, etc) and derived variables (cooling and heating degree days) have been averaged for summer months. A particular attention has been given to the influence of persistence of positive temperature anomaly and a derived variable which count the number of consecutive days of extreme-days has been used. Electricity demand forecast has been performed using linear and nonlinear regression methods and stepwise model selection procedures have been used to perform a variable selection with respect to performance measures. Significance tests on multiple linear regression showed the importance of cooling degree days during summer in the North-East and South of Italy with an increase of statistical significance after 2003, a result consistent with the diffusion of air condition and ventilation equipment in the last decade. Finally, using seasonal climate forecasts we evaluate
Short-term load forecasting based on a multi-model
Faller, C. [ETH, Zurich (Switzerland). Faculty of Electrical Engineering; Dvorakova, R.; Horacek, P. [Czech Technical University (Czech Republic). Faculty of Electrical Engineering
2000-07-01
Two algorithms for short-term electricity demand forecasting in the regional electricity distribution network are presented. Several approaches - feedforward neural network, adaptive modelling and fuzzy modelling - are applied to the forecast. Two different models are designed. A one hour forecasting is based on the General Regression Neural Network (GRNN) model and Principle Component Analysis. The multi-model with adaptive features and fuzzy reasoning is used for a longer-term forecast. (author)
Efficient Resources Provisioning Based on Load Forecasting in Cloud
Rongdong Hu
2014-01-01
Full Text Available Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements.
Efficient resources provisioning based on load forecasting in cloud.
Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin
2014-01-01
Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements.
Electricity load modelling using computational intelligence
Ter Borg, R.W.
2005-01-01
As a consequence of the liberalisation of the electricity markets in Europe, market players have to continuously adapt their future supply to match their customers' demands. This poses the challenge of obtaining a predictive model that accurately describes electricity loads, current in this thesis.
Eto, J.H.; Moezzi, M.M.
1993-12-01
This report summarizes findings from a unique project to improve the end-use electricity load shape and peak demand forecasts made by the Pacific Gas and Electric Company (PG&E) and the California Energy Commission (CEC). First, the direct incorporation of end-use metered data into electricity demand forecasting models is a new approach that has only been made possible by recent end-use metering projects. Second, and perhaps more importantly, the joint-sponsorship of this analysis has led to the development of consistent sets of forecasting model inputs. That is, the ability to use a common data base and similar data treatment conventions for some of the forecasting inputs frees forecasters to concentrate on those differences (between their competing forecasts) that stem from real differences of opinion, rather than differences that can be readily resolved with better data. The focus of the analysis is residential space cooling, which represents a large and growing demand in the PG&E service territory. Using five years of end-use metered, central air conditioner data collected by PG&E from over 300 residences, we developed consistent sets of new inputs for both PG&E`s and CEC`s end-use load shape forecasting models. We compared the performance of the new inputs both to the inputs previously used by PG&E and CEC, and to a second set of new inputs developed to take advantage of a recently added modeling option to the forecasting model. The testing criteria included ability to forecast total daily energy use, daily peak demand, and demand at 4 P.M. (the most frequent hour of PG&E`s system peak demand). We also tested the new inputs with the weather data used by PG&E and CEC in preparing their forecasts.
Pulusani, Praneeth R.
As the number of electric vehicles on the road increases, current power grid infrastructure will not be able to handle the additional load. Some approaches in the area of Smart Grid research attempt to mitigate this, but those approaches alone will not be sufficient. Those approaches and traditional solution of increased power production can result in an insufficient and imbalanced power grid. It can lead to transformer blowouts, blackouts and blown fuses, etc. The proposed solution will supplement the ``Smart Grid'' to create a more sustainable power grid. To solve or mitigate the magnitude of the problem, measures can be taken that depend on weather forecast models. For instance, wind and solar forecasts can be used to create first order Markov chain models that will help predict the availability of additional power at certain times. These models will be used in conjunction with the information processing layer and bidirectional signal processing components of electric vehicle charging systems, to schedule the amount of energy transferred per time interval at various times. The research was divided into three distinct components: (1) Renewable Energy Supply Forecast Model, (2) Energy Demand Forecast from PEVs, and (3) Renewable Energy Resource Estimation. For the first component, power data from a local wind turbine, and weather forecast data from NOAA were used to develop a wind energy forecast model, using a first order Markov chain model as the foundation. In the second component, additional macro energy demand from PEVs in the Greater Rochester Area was forecasted by simulating concurrent driving routes. In the third component, historical data from renewable energy sources was analyzed to estimate the renewable resources needed to offset the energy demand from PEVs. The results from these models and components can be used in the smart grid applications for scheduling and delivering energy. Several solutions are discussed to mitigate the problem of overloading
7 CFR 1710.205 - Minimum approval requirements for all load forecasts.
2010-01-01
... projections, forecast assumptions, and the methods and procedures used to develop the forecast; (3... management activities, if applicable; (6) Graphic representations of the variables specifically identified by... per consumer projections from the load forecast to develop system design criteria. The assumptions...
artificial neural network (ann) approach to electrical load
2004-08-18
Aug 18, 2004 ... short term hourly load forecasts has a significant impact on the economic ... other peaking plants such as gas turbines. The ... load, usually into basic and weather dependent ... as sudden weather or special events. ANN can ...
Electricity Crisis and Load Management in Bangladesh
Rajib Kanti Das
2012-09-01
Full Text Available Bangladesh is a densely populated country. Only a small part of her area is electrified which cover around 18% of total population. The people who are in the electrified area are suffering from severe load shedding. A systematic load management procedure related to demand side may improve the situation is the research problem. The major objectives serve by the research are to analyze contemporary electricity status with a view to drawing inference about demand supply gap and extracting benefits from load management. Data supplied by the Bangladesh Power Development Board, World Bank and outcome of survey are analyzed with some simple statistical tools to test the hypothesis. Analysis discloses that with properly managed uses of electricity with load switch and rotation week-end can improve the concurrent condition of electricity. Moreover, introducing smart distribution system, reducing system loss, shifting load to off-peak, large scale use of prepaid mete, observing energy week and using energy efficient home and office appliance are recommended to improve load through demand side management. Some other recommendations such as introducing alternative energy, public private partnership and using renewable energy development and producing energy locally are made for load management from the supply side.
An adaptive neural network approach to one-week ahead load forecasting
Peng, T.M. (Pacific Gas and Electric Co., San Francisco, CA (United States)); Hubele, N.F.; Karady, G.G. (Arizona State Univ., Tempe, AZ (United States))
1993-08-01
A new neural network approach is applied to one-week ahead load forecasting. This approach uses a linear adaptive neuron or adaptive linear combiner called Adaline.'' An energy spectrum is used to analyze the periodic components in a load sequence. The load sequence mainly consists of three components: base load component, and low and high frequency load components. Each load component has a unique frequency range. Load decomposition is made for the load sequence using digital filters with different passband frequencies. After load decomposition, each load component can be forecasted by an Adaline. Each Adaline has an input sequence, an output sequence, and a desired response-signal sequence. It also has a set of adjustable parameters called the weight vector. In load forecasting, the weight vector is designed to make the output sequence, the forecasted load, follow the actual load sequence; it also has a minimized Least Mean Square error. This approach is useful in forecasting unit scheduling commitments. Mean absolute percentage errors of less than 3.4 percent are derived from five months of utility data, thus demonstrating the high degree of accuracy that can be obtained without dependence on weather forecasts.
Load As A Reliability Resource in the Restructured Electricity Market
Kueck, J.D.
2002-06-10
condition (e.g., forecast reserves fall below a threshold), rather than those triggered by price (e.g., real-time prices). Third, the report examines the status of the underlying metering, communication, and control technologies required to enable customer loads to participate in competitive electricity markets (Section 4). Following the three-part assessment, we offer preliminary thoughts on directions for future research (Section 5).
Modeling and Analysis of Commercial Building Electrical Loads for Demand Side Management
Berardino, Jonathan
In recent years there has been a push in the electric power industry for more customer involvement in the electricity markets. Traditionally the end user has played a passive role in the planning and operation of the power grid. However, many energy markets have begun opening up opportunities to consumers who wish to commit a certain amount of their electrical load under various demand side management programs. The potential benefits of more demand participation include reduced operating costs and new revenue opportunities for the consumer, as well as more reliable and secure operations for the utilities. The management of these load resources creates challenges and opportunities to the end user that were not present in previous market structures. This work examines the behavior of commercial-type building electrical loads and their capacity for supporting demand side management actions. This work is motivated by the need for accurate and dynamic tools to aid in the advancement of demand side operations. A dynamic load model is proposed for capturing the response of controllable building loads. Building-specific load forecasting techniques are developed, with particular focus paid to the integration of building management system (BMS) information. These approaches are tested using Drexel University building data. The application of building-specific load forecasts and dynamic load modeling to the optimal scheduling of multi-building systems in the energy market is proposed. Sources of potential load uncertainty are introduced in the proposed energy management problem formulation in order to investigate the impact on the resulting load schedule.
ELECTRICAL LOAD ANTICIPATOR AND RECORDER
Werme, J.E.
1961-09-01
A system is described in which an indication of the prevailing energy consumption in an electrical power metering system and a projected power demand for one demand in terval is provided at selected increments of time within the demand interval. Each watt-hour meter in the system is provided with an impulse generator that generates two impulses for each revolution of the meter disc. In each demand interval, for example, one half-hour, of the metering system, the total impulses received from all of the meters are continuously totaled for each 5-minute interval and multiplied by a number from 6 to 1 depending upon which 5- minute interval the impulses were received. This value is added to the total pulses received in the intervals preceding the current 5-minute interval within the half-hour demand interval tc thereby provide an indication of the projected power demand every 5 minutes in the demand interval.
SVR-Boosting ensemble model for electricity price forecasting in electric power market
ZHOU Dian-min; GAO Lin; GUAN Xiao-hong; GAO Feng
2008-01-01
A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boos-ting) is presented in this paper for electricity price forecasting in electric power market. In the light of charac-the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accu-racy, and possess comparatively satisfactory generalization capability.
姚刚; 陈政石; 李晓竹
2011-01-01
As conventional BP network for the slow convergence and easy to fall into local minimum problem, the use of LM algorithm for network training, the improved particle swarm optimization BP network initial weights and threshold. Application of this method in the text grid in a city short - term load forecasting, showed that compared with conventional BP network, L- M algorithm to improve prediction models. This article describes the results of the prediction algorithm in the prediction accuracy and speed have increased greatly.%针对常规BP网络收敛速度慢，易陷入局部极小值等问题，采用L—M算法对网络进行训练，利用改进粒子群算法优化BP网络初始权值和阈值。将该方法应用在南方某市短期电网负荷预测中，预测结果表明，相较于常规BP网络、L—M算法改进预测模型，该预测算法在预测结果精度和速度上均有较大幅度提高。
An Electrical Energy Consumption Monitoring and Forecasting System
Rojas-Renteria, J. L.; T. D. Espinoza-Huerta; F. S. Tovar-Pacheco; Gonzalez-Perez, J. L.; Lozano-Dorantes, R.
2016-01-01
Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecastin...
A combined gray neural network model of seasonal heating load forecast
QIAOXiaozhuang; YANGChangzhi
2003-01-01
Seasonal heating load time sequence has the double trends of increasing and fluctuating, so it''s difficult to select a model to forecast it. In this paper, a combined model of gray model and artificial neural network model was presented to forecast seasonal heating load. A concrete model was established and was verified through actual examples.
Adkins, R.D.; Watkins, E.L. [Missouri Public Service, Kansas City, MO (United States); Baxter, T.A. [Aspen Systems Corp., Oak Ridge, TN (United States)
1995-05-01
The development of end-use energy consumption and load shape data is important for end-use load forecasting and demand-side management (DSM) planning applications. This paper discusses the objectives, sample design data issues and applications of residential end-use load research and customer survey data at Missouri Public Service (MPS); a combination electric and natural gas utility division of UtiliCorp United Inc. Multiple objectives including rate design, market research, end-use load forecasting and DSM planning, determined that an integrated end-use load research sample design would be the most cost-effective approach. Comprehensive end-use customer survey data was collected and analyzed using XENERGY`s RECAP bill disaggregation program in conjunction with and end-use load research project in 1992 to calibrate EPRI`s REEPS forecasts. The resulting end-use load shapes and UEC`s provide consistent inputs to SRC`s COMPASS model and EPRI`s DSManager model for DSM Planning
Ping Jiang
2015-01-01
Full Text Available The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.
Hill, D.C. [University Coll. of North Wales, Menai Bridge (United Kingdom). School of Ocean Science; Infield, D.G. [Loughborough Univ. of Technology (United Kingdom). Dept. of Electronic and Electrical Engineering
1995-11-01
A load forecasting technique, based upon an autoregressive (AR) method is presented. Its use for short term load forecasting is assessed by direct comparison with real forecasts made by human operators of the Lerwick power station on the Shetland Islands. A substantial improvement in load prediction, as measured by a reduction of RMS error, is demonstrated. Shetland has a total installed capacity of about 68 MW, and an average load (1990) of around 20 MW. Although the operators could forecast the load for a few distinct hours better than the AR method, results from simulations of the scheduling and operation of the generating plant show that the AR forecasts provide increased overall system performance. A detailed model of the island power system, which includes plant scheduling, was run using the AR and Lerwick operators` forecasts as input to the scheduling routine. A reduction in plant cycling, underloading and fuel consumption was obtained using the AR forecasts rather than the operators` forecasts in simulations over a 28 day study period. It is concluded that the load forecasting method presented could be of benefit to the operators of such mesoscale power systems. (author)
Electricity generation modeling and photovoltaic forecasts in China
Li, Shengnan
With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979--2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.
Short-term load forecasting study of wind power based on Elman neural network
Tian, Xinran; Yu, Jing; Long, Teng; Liu, Jicheng
2017-01-01
Since wind power has intermittent, irregular and volatility nature, improving load forecasting accuracy of wind power has significant influence on controlling wind system and guarantees stable operation of power grids. This paper constructed the wind farm loading forecasting in short-term based on Elman neural network, and made a numerical example analysis. . Examples show that, using input delayed of feedback Elman neural network, can reflect the inherent laws of wind load operation better, so as to present a new idea for short-term load forecasting of wind power.
Short-Term Load Forecasting: The Similar Shape Functional Time Series Predictor
Paparoditis, Efstathios
2012-01-01
We introduce a novel functional time series methodology for short-term load forecasting. The prediction is performed by means of a weighted average of past daily load segments, the shape of which is similar to the expected shape of the load segment to be predicted. The past load segments are identified from the available history of the observed load segments by means of their closeness to a so-called reference load segment, the later being selected in a manner that captures the expected qualitative and quantitative characteristics of the load segment to be predicted. Weak consistency of the suggested functional similar shape predictor is established. As an illustration, we apply the suggested functional time series forecasting methodology to historical daily load data in Cyprus and compare its performance to that of a recently proposed alternative functional time series methodology for short-term load forecasting.
Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-23
In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.
Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-07-26
In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.
Study on load forecasting to data centers of high power density based on power usage effectiveness
Zhou, C. C.; Zhang, F.; Yuan, Z.; Zhou, L. M.; Wang, F. M.; Li, W.; Yang, J. H.
2016-08-01
There is usually considerable energy consumption in data centers. Load forecasting to data centers is in favor of formulating regional load density indexes and of great benefit to getting regional spatial load forecasting more accurately. The building structure and the other influential factors, i.e. equipment, geographic and climatic conditions, are considered for the data centers, and a method to forecast the load of the data centers based on power usage effectiveness is proposed. The cooling capacity of a data center and the index of the power usage effectiveness are used to forecast the power load of the data center in the method. The cooling capacity is obtained by calculating the heat load of the data center. The index is estimated using the group decision-making method of mixed language information. An example is given to prove the applicability and accuracy of this method.
30 CFR 56.6602 - Static electricity dissipation during loading.
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Static electricity dissipation during loading... Explosives Extraneous Electricity § 56.6602 Static electricity dissipation during loading. When explosive material is loaded pneumatically into a blasthole in a manner that generates a static electricity hazard...
Online short-term heat load forecasting for single family houses
Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg
2013-01-01
This paper presents a method for forecasting the load for heating in a single-family house. Both space and hot tap water heating are forecasted. The forecasting model is built using data from sixteen houses in Sønderborg, Denmark, combined with local climate measurements and weather forecasts....... The models are optimized to fit the level of optimal adaptivity and the thermal dynamical response of the building. Identification of a model, which is suitable for application to all the houses, is carried out. The results show that the forecasting errors mainly are related to: unpredictable high frequency...
D6.2–Load and generation forecasting methods and prototypes
Madsen, Per Printz; Dueñas, Lara Pérez; Moraga, Carlos Castaño
consumption. Energy consumption forecasting and renewable energy generation forecasting are two completely different problems that have been addressed separately, although they require similar inputs and a similar architecture. The modelling and forecasting of the energy consumed by a building usually leads...... than energy load forecasting due to immediate impact of weather conditions – wind or cloudiness – on power output. Renewable generation depends on environmental conditions (wind speed, solar irradiation), which are subject to large fluctuations and their reliable forecasts are not always available...
Yi Liang
2016-10-01
Full Text Available Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT and least squares support vector machine (LSSVM, which is optimized by an improved cuckoo search (CS. To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day’s load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system.
Jingmin Wang; Jian Zhang; Jing Nie
2016-01-01
Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity con...
Saini, Lalit Mohan [Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana 136119 (India)
2008-07-15
Up to 7 days ahead electrical peak load forecasting has been done using feed forward neural network based on Steepest descent, Bayesian regularization, Resilient and adaptive backpropagation learning methods, by incorporating the effect of eleven weather parameters and past peak load information. To avoid trapping of network into a state of local minima, the optimization of user-defined parameters viz., learning rate and error goal has been performed. The sliding window concept has been incorporated for selection of training data set. It was then reduced as per relevant selection according to the day type and season for which the forecast is made. To reduce the dimensionality of input matrix, the Principal Component Analysis method of factor extraction or correlation analysis technique has been used and their performance has been compared. The resultant data set was used for training of three-layered neural network. In order to increase the learning speed, the weights and biases were initialized according to Nguyen and Widrow method. To avoid over fitting, early stopping of training was done at the minimum validation error. (author)
Energy Systems Scenario Modelling and Long Term Forecasting of Hourly Electricity Demand
Alberg Østergaard, Poul; Møller Andersen, Frits; Kwon, Pil Seok
2015-01-01
. The results show that even with a limited short term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrate wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant...... or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model...... effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps...
Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint
Steckler, N.; Florita, A.; Zhang, J.; Hodge, B. M.
2013-11-01
As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.
Love, C G
1976-08-23
These appendixes are referenced in Volume II of this report. They contain the detailed electrical distribution equipment requirements and input material requirements forecasts. Forecasts are given for three electric energy usage scenarios. Also included are data on worldwide reserves and demand for 30 raw materials required for the manufacture of electrical distribution equipment.
Prediction by simulation. Part 3. Forecaste for electric power demand; Denryoku juyo yosoku
Haida, T. [Tokyo Electric Co. Ltd. (Japan)
1997-08-20
Electric power demand forecast can be divided into short-range demand forecasting and long-range demand forecasting. A discussion is made placing emphasis on the forecast of the maximum powers for the day and for the following day which are considered to be most important in the short-range demand forecasting. The regression model is a method which persons in charge of forecasting can comprehend easily because it agrees fairly well with their experience and institution. Applications of time series model to demand forecasting for every hour for the following day and for the week are reported. Many attempts are being made recently to use neural network for the forecasting model. For the estimation of the maximum power, meteorological conditions of the day of forecasting are indispensable. As a result, the ultimate accuracy of forecasting is influenced greatly by the accuracy of the forecasted weather. Every electric power company in Japan has a maximum power forecasting support system at the present time. For long-range demand forecasting for a few years and for ten-odd years, macro-methods and micro-methods of employing accumulated demands for applications such as electric lights and electricity for business use are adopted. 11 refs., 6 figs.
Rogers, J.; Porter, K.
2011-03-01
The report and accompanying table addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America. The first part of the table focuses on electric utilities and regional transmission organizations that have central wind power forecasting in place; the second part focuses on electric utilities and regional transmission organizations that plan to adopt central wind power forecasting in 2010. This is an update of the December 2009 report, NREL/SR-550-46763.
Haben, Stephen
2016-01-01
We present a model for generating probabilistic forecasts by combining kernel density estimation (KDE) and quantile regression techniques, as part of the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014. The KDE method is initially implemented with a time-decay parameter. We later improve this method by conditioning on the temperature or the period of the week variables to provide more accurate forecasts. Secondly, we develop a simple but effective quantile regression forecast. The novel aspects of our methodology are two-fold. First, we introduce symmetry into the time-decay parameter of the kernel density estimation based forecast. Secondly we combine three probabilistic forecasts with different weights for different periods of the month.
Electric Power Demand Forecasting: A Case Study of Lucknow City
A.K. Bhardwaj and R.C. Bansal
2011-03-01
Full Text Available The study of forecasting identifies the urgent need for special attention in evolving effective energy policies to alleviate an energy famine in the near future. Since power demand is increasing day by day in entire world and it is also one of the fundamental infrastructure input for the development, its prospects and availability sets significant constraints on the socio-economic growth of every person as well as every country. A care full long-term power plan is imperative for the development of power sector. This need assumes more importance in the state of Uttar Pradesh where the demand for electrical energy is growing at a rapid pace. This study analyses the requirement of electricity with respect to the future population for the major forms of energy in the Lucknow city in Uttar Pradesh state of India. A model consisting of significant key energy indicators have been used for the estimation. Model wherever required refined in the second stage to remove the effect of auto-correlation. The accuracy of the model has been checked using standard statistical techniques and validated against the past data by testing for ‘expost’ forecast accuracy.
Extracting Operating Modes from Building Electrical Load Data: Preprint
Frank, S.; Polese, L. G.; Rader, E.; Sheppy, M.; Smith, J.
2012-01-01
Empirical techniques for characterizing electrical energy use now play a key role in reducing electricity consumption, particularly miscellaneous electrical loads, in buildings. Identifying device operating modes (mode extraction) creates a better understanding of both device and system behaviors. Using clustering to extract operating modes from electrical load data can provide valuable insights into device behavior and identify opportunities for energy savings. We present a fast and effective heuristic clustering method to identify and extract operating modes in electrical load data.
Yang, Yi; Du, Liang
2016-09-13
A system for different electric loads includes sensors structured to sense voltage and current signals for each of the different electric loads; a hierarchical load feature database having a plurality of layers, with one of the layers including a plurality of different load categories; and a processor. The processor acquires voltage and current waveforms from the sensors for a corresponding one of the different electric loads; maps a voltage-current trajectory to a grid including a plurality of cells, each of which is assigned a binary value of zero or one; extracts a plurality of different features from the mapped grid of cells as a graphical signature of the corresponding one of the different electric loads; derives a category of the corresponding one of the different electric loads from the database; and identifies one of a plurality of different electric load types for the corresponding one of the different electric loads.
ZENG Ming; LU Chunquan; TIAN Kuo; XUE Song
2011-01-01
During the Twelfth Five-Year plan, large-scale construction of smart grid with safe and stable operation requires a timely and accurate short-term load forecasting method. Moreover, along with the full-scale smart grid construction, the power supply mode and consumption mode of the whole system can be optimized through the accurate short-term load forecasting; and the security, stability and cleanness of the system can be guaranteed.
Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models
Tan, Zhongfu; Zhang, Jinliang; Xu, Jun [North China Electric Power University, Beijing 102206 (China); Wang, Jianhui [Argonne National Laboratory, Argonne, IL 60439 (United States)
2010-11-15
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)
Forecasting of Hourly Photovoltaic Energy in Canarian Electrical System
Henriquez, D.; Castaño, C.; Nebot, R.; Piernavieja, G.; Rodriguez, A.
2010-09-01
The Canarian Archipelago face similar problems as most insular region lacking of endogenous conventional energy resources and not connected to continental electrical grids. A consequence of the "insular fact" is the existence of isolated electrical systems that are very difficult to interconnect due to the considerable sea depths between the islands. Currently, the Canary Islands have six isolated electrical systems, only one utility generating most of the electricity (burning fuel), a recently arrived TSO (REE) and still a low implementation of Renewable Energy Resources (RES). The low level of RES deployment is a consequence of two main facts: the weakness of the stand-alone grids (from 12 MW in El Hierro up to only 1 GW in Gran Canaria) and the lack of space to install RES systems (more than 50% of the land protected due to environmental reasons). To increase the penetration of renewable energy generation, like solar or wind energy, is necessary to develop tools to manage them. The penetration of non manageable sources into weak grids like the Canarian ones causes a big problem to the grid operator. There are currently 104 MW of PV connected to the islands grids (Dec. 2009) and additional 150 MW under licensing. This power presents a serious challenge for the operation and stability of the electrical system. ITC, together with the local TSO (Red Eléctrica de España, REE) started in 2008 and R&D project to develop a PV energy prediction tool for the six Canarian Insular electrical systems. The objective is to supply reliable information for hourly forecast of the generation dispatch programme and to predict daily solar radiation patterns, in order to help program spinning reserves. ITC has approached the task of weather forecasting using different numerical model (MM5 and WRF) in combination with MSG (Meteosat Second Generation) images. From the online data recorded at several monitored PV plants and meteorological stations, PV nominal power and energy produced
S. Saravanan
2012-07-01
Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.
Radziukynas V.
2016-04-01
Full Text Available The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011 and planned wind power capacities (the year 2023.
Peak Electric Load Relief in Northern Manhattan
Hildegaard D. Link
2014-08-01
Full Text Available The aphorism “Think globally, act locally,” attributed to René Dubos, reflects the vision that the solution to global environmental problems must begin with efforts within our communities. PlaNYC 2030, the New York City sustainability plan, is the starting point for this study. Results include (a a case study based on the City College of New York (CCNY energy audit, in which we model the impacts of green roofs on campus energy demand and (b a case study of energy use at the neighborhood scale. We find that reducing the urban heat island effect can reduce building cooling requirements, peak electricity loads stress on the local electricity grid and improve urban livability.
Short-term load forecasting using neural network for future smart grid application
Zennamo, Joseph Anthony, III
Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.
Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting
Xia Hua; Gang Zhang; Jiawei Yang; Zhengyuan Li
2015-01-01
Aiming at the low accuracy problem of power system short⁃term load forecasting by traditional methods, a back⁃propagation artifi⁃cial neural network (BP⁃ANN) based method for short⁃term load forecasting is presented in this paper. The forecast points are re⁃lated to prophase adjacent data as well as the periodical long⁃term historical load data. Then the short⁃term load forecasting model of Shanxi Power Grid (China) based on BP⁃ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP⁃ANN method is simple and with higher precision and practicality.
30 CFR 57.6602 - Static electricity dissipation during loading.
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Static electricity dissipation during loading... MINES Explosives Extraneous Electricity-Surface and Underground § 57.6602 Static electricity dissipation... generates a static electricity hazard— (a) An evaluation of the potential static electricity hazard shall be...
Wavelet time series MPARIMA modeling for power system short term load forecasting
冉启文; 单永正; 王建赜; 王骐
2003-01-01
The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near-periodicity, nonstationarity and nonlinearity existed in power system short term quarter-hour load time series, and can therefore accurately forecast the quarter-hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.
Experimenting with Electrical Load Sensing on a Backhoe Loader
Andersen, Torben Ole; Hansen, Michael Rygaard; Pedersen, Henrik Clemmensen
2005-01-01
Where traditional load sensing is made using hydro-mechanical regulators and load pressure is fed back hydraulically, electrical load sensing employs the usage of electronic sensors and electrically actuated components. This brings forth new possibilities, but also imposes problems concerning...
Dynamical prediction and pattern mapping in short-term load forecasting
Aguirre, Luis Antonio; Rodrigues, Daniela D.; Lima, Silvio T. [Departamento de Engenharia Eletronica, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil); Martinez, Carlos Barreira [Departamento de Engenharia Hidraulica e Recursos Hidricos, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil)
2008-01-15
This work will not put forward yet another scheme for short-term load forecasting but rather will provide evidences that may improve our understanding about fundamental issues which underlay load forecasting problems. In particular, load forecasting will be decomposed into two main problems, namely dynamical prediction and pattern mapping. It is argued that whereas the latter is essentially static and becomes nonlinear when weekly features in the data are taken into account, the former might not be deterministic at all. In such cases there is no determinism (serial correlations) in the data apart from the average cycle and the best a model can do is to perform pattern mapping. Moreover, when there is determinism in addition to the average cycle, the underlying dynamics are sometimes linear, in which case there is no need to resort to nonlinear models to perform dynamical prediction. Such conclusions were confirmed using real load data and surrogate data analysis. In a sense, the paper details and organizes some general beliefs found in the literature on load forecasting. This sheds some light on real model-building and forecasting problems and helps understand some apparently conflicting results reported in the literature. (author)
Xunlin Jiang; Haifeng Ling; Jun Yan; Bo Li; Zhao Li
2013-01-01
Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, ...
K.A.D. Deshani
2014-05-01
Full Text Available Accurate prediction of electricity demand can bring extensive benefits to any country as the forecasted values help the relevant authorities to take decisions regarding electricity generation, transmission and distribution appropriately. The literature reveals that, when compared to conventional time series techniques, the improved artificial intelligent approaches provide better prediction accuracies. However, the accuracy of predictions using intelligent approaches like neural networks are strongly influenced by the correct selection of inputs and the number of neuro-forecasters used for prediction. Deshani, Hansen, Attygalle, & Karunarathne (2014 suggested that a cluster analysis could be performed to group similar day types, which contribute towards selecting a better set of neuro-forecasters in neural networks. The cluster analysis was based on the daily total electricity demands as their target was to predict the daily total demands using neural networks. However, predicting half-hourly demand seems more appropriate due to the considerable changes of electricity demand observed during a particular day. As such clusters are identified considering half-hourly data within the daily load distribution curves. Thus, this paper is an improvement to Deshani et. al. (2014, which illustrates how the half hourly demand distribution within a day, is incorporated when selecting the inputs for the neuro-forecasters.
Analytical Treatment of Forecasts of Electric Energy Consumption in Latvia
Balodis, M.; Gavars, V.; Andersons, J.
2014-06-01
In the paper, the changes in electric energy consumption are analyzed as associated with structural changes in the Latvian economy of postsocialistic period. To the analysis, a particular approach is applied, which consists in comparison of the basic and specific electricity consumption indices in West-, Central-, and East-European states for the time span of 1990-2010, with differences and tendencies of changes revealed. Tendencies of the type are determined for the electric energy consumption in Latvia, and recommendations are given for the use of such indices in the relevant forecasts. Rakstā apskatītas elektroenerģijas patēriņa izmaiņas, kas saistītas ar Latvijas postsociālisma perioda ekonomikas strukturālām izmaiņām. Rakstā dota Latvijas galveno elektroenerģijas patēriņa indikatoru analīze, lietojot īpašu pieeju - Rietumeiropas, Centrāleiropas un Austrumeiropas valstu indikatoru salīdzinājumu. Analizēts periods no 1990. gada līdz 2010. gadam. Salīdzināti Eiropas valstu grupu īpatnējie elektroenerģijas patēriņa indikatori un noskaidrotas to atšķirības un izmaiņu tendences. Noteiktas elektroenerģijas patēriņa izmaiņu tendences Latvijā. Dotas rekomendācijas par šo indikatoru izmantošanu elektroenerģijas patēriņa prognozēšanā. 07.05.2014.
Lianhui Li
2015-12-01
Full Text Available Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.
A Simplified Short Term Load Forecasting Method Based on Sequential Patterns
Kouzelis, Konstantinos; Bak-Jensen, Birgitte; Mahat, Pukar
2014-01-01
, require considerable expertise for model construction and re-construction. Consequently, they might be impractical to use in case multiple regional forecasts are to be conducted. In this perspective, a simplified hour-ahead load forecasting algorithm was created so as to provide an automated approach...... to the problem as an alternative to other established forecasting techniques. This algorithm is based on sequential patterns and, hence, the continuous data are discretized in order to compare recent to past patterns. Although some error due to discretization is introduced, the method performs adequately well...... in comparison with an ARIMA model....
Assessing and Reducing Miscellaneous Electric Loads (MELs) in Lodging
Rauch, Emily M.
2011-09-01
Miscellaneous electric loads (MELs) are the loads outside of a building's core functions of heating, ventilating, air conditioning, lighting, and water heating. This report reviews methods to reduce MELs in lodging.
Electrical consumption forecast using actual data of building end-use decomposition
Escrivá Escrivá, Guillermo; Roldán Blay, Carlos; Álvarez Bel, Carlos María
2014-01-01
The calculation of electricity consumption forecast a few days ahead is a complex issue and studies about this matter are continually being performed. Advances in this field enable obtaining consumption forecasts increasingly accurate. These consumption forecasts aim to improve the knowledge of the facilities, the planning and control of consumption and the measurement and verification of energy saving measures, among others. In this study the authors present several advances related to consu...
Lu, Bin; Harley, Ronald G.; Du, Liang; Yang, Yi; Sharma, Santosh K.; Zambare, Prachi; Madane, Mayura A.
2014-06-17
A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.
Jingmin Wang
2016-01-01
Full Text Available Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity consumption forecast as a difficult study influenced by various factors. This paper proposed an improved Artificial Bee Colony (ABC algorithm which combined with multivariate linear regression (MLR for exploring the influencing mechanism of various factors on Chinese electricity consumption and forecasting electricity consumption in the future. The results indicated that the improved ABC algorithm in view of the various factors is superior to traditional models just considering unilateralism in accuracy and persuasion. The overall findings cast light on this model which provides a new scientific and effective way to forecast the medium and long term electricity consumption.
A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.
Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan
2015-01-01
In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.
A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs
Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan
2015-01-01
In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042
A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.
Yuanfu Mo
Full Text Available In a vehicular ad hoc network (VANET, the periodic exchange of single-hop status information broadcasts (beacon frames produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.
A Review of Demand Forecast for Charging Facilities of Electric Vehicles
Jiming, Han; Lingyu, Kong; Yaqi, Shen; Ying, Li; Wenting, Xiong; Hao, Wang
2017-05-01
The demand forecasting of charging facilities is the basis of its planning and locating, which has important role in promoting the development of electric vehicles and alleviating the energy crisis. Firstly, this paper analyzes the influence of the charging mode, the electric vehicle population and the user’s charging habits on the demand of charging facilities; Secondly, considering these factors, the recent analysis on charging and switching equipment demand forecast is divided into two methods—forecast based on electric vehicle population and user traveling behavior. Then, the article analyzes the two methods and puts forward the advantages and disadvantages. Finally, in view of the defects of current research, combined with the current situation of the development of the city and comprehensive consideration of economic, political, environmental and other factors, this paper proposes an improved demand forecasting method which has great practicability and pertinence and lays the foundation for the plan of city electric facilities.
Electrical Load and Energy Management. Course Outline and Instructional Materials.
Wang, Paul
Presented are 13 lecture outlines with accompanying handouts and reference lists for teaching school administrators and maintenance personnel the use of electrical load management as an energy conservation tool. To aid course participants in making cost effective use of electrical power, methods of load management in a variety of situations are…
Cheng-Ming Lee
2016-11-01
Full Text Available A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF in this article. The proposed model integrates radial basis function neural network (RBFNN, support vector regression (SVR, and adaptive annealing learning algorithm (AALA. In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN. In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from the Taiwan Power Company (TPC. Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision compared to various RBFNNs.
Degradation forecast for PEMFC cathode-catalysts under cyclic loads
Moein-Jahromi, M.; Kermani, M. J.; Movahed, S.
2017-08-01
Degradation of Fuel Cell (FC) components under cyclic loads is one of the biggest bottlenecks in FC commercialization. In this paper, a novel experimental based algorithm is presented to predict the Catalyst Layer (CL) performance loss during cyclic load. The algorithm consists of two models namely Models 1 and 2. The Model 1 calculates the Electro-Chemical Surface Area (ECSA) and agglomerate size (e.g. agglomerate radius, rt,agg) for the catalyst layer under cyclic load. The Model 2 is the already-existing model from our earlier studies that computes catalyst performance with fixed structural parameters. Combinations of these two Models predict the CL performance under an arbitrary cyclic load. A set of parametric/sensitivity studies is performed to investigate the effects of operating parameters on the percentage of Voltage Degradation Rate (VDR%) with rank 1 for the most influential one. Amongst the considered parameters (such as: temperature, relative humidity, pressure, minimum and maximum voltage of the cyclic load), the results show that temperature and pressure have the most and the least influences on the VDR%, respectively. So that, increase of temperature from 60 °C to 80 °C leads to over 20% VDR intensification, the VDR will also reduce 1.41% by increasing pressure from 2 atm to 4 atm.
M. Schroedter-Homscheidt
2017-02-01
Full Text Available The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI. The European Centre for Medium-Range Weather Forecasts (ECMWF has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998 global to direct irradiance
Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes
Exizidis, Lazaros; Kazempour, Jalal; Pinson, Pierre
2017-01-01
by offering better knowledge of the market operation, leading subsequently to a more competitive energy market. Driven by the above regulation, we consider an equilibrium study to address how public information of aggregate wind power forecasts can potentially affect market results, social welfare as well......Following a call to foster a transparent and more competitive market, member states of the European transmission system operator are required to publish, among other information, aggregate wind power forecasts. The publication of the latter information is expected to benefit market participants......-theoretic approach (diagonalization) is incorporated in order to investigate the existence of an equilibrium for various values of aggregate forecast. As anticipated, variations in public forecasts will affect market results and, more precisely, under-forecasts can mislead power producers to make decisions...
Biao Yang
2016-01-01
Full Text Available Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh.
Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems
Luis Hernández
2014-03-01
Full Text Available The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.
Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin
2017-08-01
The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.
MCMC simulation of GARCH model to forecast network traffic load
Akhter Raza Syed
2012-05-01
Full Text Available The performance of a computer network can be enhanced by increasing number of servers, upgrading the hardware, and gaining additional bandwidth but this solution require the huge amount to invest. In contrast to increasing the bandwidth and hardware resources, network traffic modeling play a significant role in enhancing the network performance. As the emphasis of telecommunication service providers shifted towards the high-speed networks providing integrated services at a prescribed Quality of Service (QoS, the role of accurate traffic models in network design and network simulation becomes ever more crucial. We analyze a traffic volume time series of internet requests made to a workstation. This series exhibits a long-range dependence and self-similarity in large time scale and exhibits multifractal in small time scale. In this paper, for this time series, we proposed Generalized Autoregressive Conditional Heteroscedastic, (GARCH model, and practical techniques for model fitting, Markov Chain Monte Carlo simulation and forecasting issues are demonstrated. The proposed model provides us simple and accurate approach for simulating internet data traffic patterns.
Predicting the Response of Electricity Load to Climate Change
Sullivan, Patrick [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Colman, Jesse [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kalendra, Eric [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2015-07-28
Our purpose is to develop a methodology to quantify the impact of climate change on electric loads in the United States. We perform simple linear regression, assisted by geospatial smoothing, on paired temperature and load time-series to estimate the heating- and coolinginduced sensitivity to temperature across 300 transmission zones and 16 seasonal and diurnal time periods. The estimated load sensitivities can be coupled with climate scenarios to quantify the potential impact of climate change on load, with a primary application being long-term electricity scenarios. The method allows regional and seasonal differences in climate and load response to be reflected in the electricity scenarios. While the immediate product of this analysis was designed to mesh with the spatial and temporal resolution of a specific electricity model to enable climate change scenarios and analysis with that model, we also propose that the process could be applied for other models and purposes.
Load Forecasting with Artificial Intelligence on Big Data
Glauner, Patrick; State, Radu
2016-01-01
In the domain of electrical power grids, there is a particular interest in time series analysis using artificial intelligence. Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to self-learn feature hierarchies from the data rather than modeling hand-crafted features. I...
Wei Sun
2015-01-01
Full Text Available Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM which is optimized by fruit fly algorithm (FOA for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.
Methods for Analyzing Electric Load Shape and its Variability
Price, Philip
2010-05-12
Current methods of summarizing and analyzing electric load shape are discussed briefly and compared. Simple rules of thumb for graphical display of load shapes are suggested. We propose a set of parameters that quantitatively describe the load shape in many buildings. Using the example of a linear regression model to predict load shape from time and temperature, we show how quantities such as the load?s sensitivity to outdoor temperature, and the effectiveness of demand response (DR), can be quantified. Examples are presented using real building data.
谭忠富; 何楠; 周凤翱
2012-01-01
提出了一种基于果蝇优化算法（FOA）和最小二乘支持向量机（LSSVM）模型的日均电价混合预测模型。将日均电价的历史数据和负荷数据作为输入变量,利用FOA优化选择用于电价预测的LSSVM模型最优参数值,进而对日均电价进行预测。以澳大利亚NSW电力市场的实际数据为例对该模型进行了仿真测试,其结果表明：与自适应LSSVM、模拟退火LSSVM和ARIMA-GARCH模型相比,本文提出的预测模型的预测性能最好,其收敛速度快,预测精度高。%A hybrid daily average electricity price forecasting model based on fruit flies optimization algorithm(FOA) and least squares support vector machine(LSSVM) model is proposed.The historical data of daily average electricity price and load data are taken as the input variables;the optimal parameter values of LSSVM model are selected by use of FOA;the daily average electricity price is thus forecast.The simulation test based on the actual data of Australia NSW electricity market was performed.The result shows that this proposed model has achieved better forecasting performance due to its faster convergence speed and higher forecasting accuracy compared with self-adaptive LSSVM,simulated annealing LSSVM,and ARIMA-GARCH model.
Mining Rules from Electrical Load Time Series Data Set
无
2002-01-01
The mining of the rules from the electrical load time series data which are collected from the EMS (Energy Management System) is discussed. The data from the EMS are too huge and sophisticated to be understood and used by the power system engineer, while useful information is hidden in the electrical load data. The authors discuss the use of fuzzy linguistic summary as data mining method to induce the rules from the electrical load time series. The data preprocessing techniques are also discussed in the paper.
Xunlin Jiang
2013-01-01
Full Text Available Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN and particle swarm optimization (PSO. A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data.
Jeng-Fung Chen
2017-03-01
Full Text Available Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA and Cuckoo Optimization Algorithm (COA, are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.
Peak load pricing in the electric utility industry
Wenders, J.T.
In the electric utility industry, cost minimization requires that heterogeneous electric generation technologies be used to produce electricity demands of different durations. In contrast to the conclusions of traditional peak-load pricing theory, the existence of a heterogeneous capital stock means that off-peak marginal cost prices almost always should include some marginal capacity costs, and that the profit maximizing regulated electric utility may set peak price above marginal cost and off-peak price below marginal cost in order to encourage the expansion of capital-intensive base load generating capacity. (auth)
Chuntian Cheng
2016-10-01
Full Text Available For the power systems, for which few data are available for mid-term electricity market clearing price (MCP forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N model is proposed in this paper. Over the traditional GM(0, N model, three major improvements of the proposed model are: (i the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii the model parameters are identified by an improved particle swarm optimization (PSO instead of the least square method (LSM for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR model, the traditional GM(0, N model and the artificial neural network (ANN model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market.
Energy systems scenario modelling and long term forecasting of hourly electricity demand
Poul Alberg Østergaard
2015-06-01
Full Text Available The Danish energy system is undergoing a transition from a system based on storable fossil fuels to a system based on fluctuating renewable energy sources. At the same time, more of and more of the energy system is becoming electrified; transportation, heating and fuel usage in industry and elsewhere. This article investigates the development of the Danish energy system in a medium year 2030 situation as well as in a long-term year 2050 situation. The analyses are based on scenario development by the Danish Climate Commission. In the short term, it is investigated what the effects will be of having flexible or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model. The results show that even with a limited short-term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrated wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long-term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps and electric vehicles in the long-term future overshadows any effects of changes in hourly demand curve profiles.
Cheng Yugui
2013-01-01
A kind of power forecast model combined cellular genetic algorithm with BP neural network was established in this article. Mid-long term power demand in urban areas was done load forecasting and analysis based on material object of the actual power consumption in urban areas of Nanchang. The results show that this method has the characteristic of the minimum training times, the shortest consumption time, the minimum error and the shortest operation time to obtain the best fitting effect.
Forecasting electricity spot market prices with a k-factor GIGARCH process
Diongue, Abdou Ka [Universite Gaston Berger de Saint-Louis, UFR SAT, BP 234, Saint-Louis Senegal and Research Fellow at Queensland University of Technology, 2 George Street, GPO Box 2434, Brisbane QLD 4001 (Australia); Guegan, Dominique [Paris School of economics, CES-MSE, Universite Paris1 Pantheon-Sorbonne, 106 boulevard de l' Hopital, 75647 Paris, Cedex 13 (France); Vignal, Bertrand [Ingenieur EDF R and D, 1 avenue du general de Gaulle, 92141 Clamart cedex (France)
2009-04-15
In this article, we investigate conditional mean and conditional variance forecasts using a dynamic model following a k-factor GIGARCH process. Particularly, we provide the analytical expression of the conditional variance of the prediction error. We apply this method to the German electricity price market for the period August 15, 2000-December 31, 2002 and we test spot prices forecasts until one-month ahead forecast. The forecasting performance of the model is compared with a SARIMA-GARCH benchmark model using the year 2003 as the out-of-sample. The proposed model outperforms clearly the benchmark model. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria. (author)
Energy efficiency indicators for high electric-load buildings
Aebischer, Bernard; Balmer, Markus A.; Kinney, Satkartar; Le Strat, Pascale; Shibata, Yoshiaki; Varone, Frederic
2003-06-01
Energy per unit of floor area is not an adequate indicator for energy efficiency in high electric-load buildings. For two activities, restaurants and computer centres, alternative indicators for energy efficiency are discussed.
Forecasting of the electrical actuators condition using stator’s current signals
Kruglova, T. N.; Yaroshenko, I. V.; Rabotalov, N. N.; Melnikov, M. A.
2017-02-01
This article describes a forecasting method for electrical actuators realized through the combination of Fourier transformation and neural network techniques. The method allows finding the value of diagnostic functions in the iterating operating cycle and the number of operational cycles in time before the BLDC actuator fails. For forecasting of the condition of the actuator, we propose a hierarchical structure of the neural network aiming to reduce the training time of the neural network and improve estimation accuracy.
Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market
Claudio Monteiro
2015-09-01
Full Text Available This paper presents the analysis of the importance of a set of explanatory (input variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL. The available input variables include extensive hourly time series records of weather forecasts, previous prices, and regional aggregation of power generations and power demands. The paper presents the comparisons of the forecasting results achieved with a model which includes all these available input variables (EMPF model with respect to those obtained by other forecasting models containing a reduced set of input variables. These comparisons identify the most important variables for forecasting purposes. In addition, a novel Reference Explanatory Model for Price Estimations (REMPE that achieves hourly price estimations by using actual power generations and power demands of such day is described in the paper, which offers the lowest limit for the forecasting error of the EMPF model. All the models have been implemented using the same technique (artificial neural networks and have been satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. The relative importance of each explanatory variable is identified for the day-ahead price forecasts in the MIBEL. The comparisons also allow outlining guidelines of the value of the different types of input information.
Pierro, Marco; De Felice, Matteo; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina
2017-04-01
The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid and increase the amount of energy reserve and the energy imbalance cost. On regional scale, solar power estimation and forecast is becoming essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators of generation. Indeed the estimation of regional PV power can be used for PV power supervision and real time control of residual load. Mid-term PV power forecast can be employed for transmission scheduling to reduce energy imbalance and related cost of penalties, residual load tracking, trading optimization, secondary energy reserve assessment. In this context, a new upscaling method was developed and used for estimation and mid-term forecast of the photovoltaic distributed generation in a small area in the north of Italy under the control of a local DSO. The method was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. It requires a low computational effort and very few input information should be provided by users. The power estimation model achieved a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% while the one and two days forecast achieve to a RMSE of 7% and 7.5%. A model to estimate the forecast error and the prediction intervals was also developed. The photovoltaic production in the considered region provided the 6.9% of the electric consumption in 2015. Since the PV penetration is very similar to the one observed at national level (7.9%), this is a good case study to analyse the impact of PV generation on the electric grid and the effects of PV power forecast on transmission scheduling and on secondary reserve estimation. It appears that, already with 7% of PV
A Statistical Survey of the UK Residential Sector Electrical Loads
Tsagarakis, George; Collin, Adam; Kiprakis, Aristides
2013-09-01
This article presents a comprehensive statistical analysis of data obtained from a wide range of literature on the most widely used appliances in the UK residential load sector, as well as a comprehensive technology and market survey conducted by the authors. The article focuses on the individual appliances and begins by consideration of the electrical operations performed by the load. This approach allows for the loads to be categorised based on the electrical characteristics, which is particularly important for implementing load-use statistics in power system analysis. In addition to this, device ownership statistics and probability density functions of power demand are presented for the main residential loads. Although the data presented is primarily intended as a resource for the development of load profiles for power system analysis, it contains a large volume of information that provides a useful database for the wider research community.
Chester, M.; Bartos, M. D.; Eisenberg, D. A.; Gorman, B.; Johnson, N.
2015-12-01
Climate change may hinder future electricity reliability by reducing electric transmission capacity while simultaneously increasing electricity demand. This study estimates potential climate impacts to electric transmission capacity and peak electricity load in the United States. Electric power cables suffer decreased transmission capacity as they get hotter; similarly, during the summer peak period, electricity demand typically increases with hotter ambient air temperatures due to increased cooling loads. As atmospheric carbon concentrations increase, higher air temperatures may strain power infrastructure by reducing transmission capacity and increasing peak electricity loads. Taken together, these coincident impacts may have unpredictable consequences for electric power reliability. We estimate the effects of climate change on both the rated capacity of transmission infrastructure and expected electricity demand for 120 electrical utilities across the United States. We estimate climate-attributable capacity reductions to transmission lines by constructing thermal models of representative conductors, then forcing these models with downscaled CMIP5 temperature projections to determine the relative change in rated ampacity over the twenty-first century. Next, we assess the impact of climate change on electricity demand by using historical relationships between ambient temperature and utility-scale summertime peak load to estimate the extent to which climate change will incur additional peak load increases. We use downscaled temperature projections from 11 CMIP5 GCM models under 3 atmospheric carbon scenarios. We find that by mid-century (2040-2060), climate change may reduce average summertime transmission capacity by 4-6% relative to the 1990-2010 reference period. At the same time, peak summertime loads may rise by roughly 2-12% on average due to increases in daily maximum air temperature. In the absence of energy efficiency gains, demand-side management programs
Application of Interval Type-2 Fuzzy Logic System in Short Term Load Forecasting on Special Days
Agus Dharma
2011-05-01
Full Text Available This paper presents the application of Interval Type-2 fuzzy logic systems (Interval Type-2 FLS in short term load forecasting (STLF on special days, study case in Bali Indonesia. Type-2 FLS is characterized by a concept called footprint of uncertainty (FOU that provides the extra mathematical dimension that equips Type-2 FLS with the potential to outperform their Type-1 counterparts. While a Type-2 FLS has the capability to model more complex relationships, the output of a Type-2 fuzzy inference engine needs to be type-reduced. Type reduction is used by applying the Karnik-Mendel (KM iterative algorithm. This type reduction maps the output of Type-2 FSs into Type-1 FSs then the defuzzification with centroid method converts that Type-1 reduced FSs into a number. The proposed method was tested with the actual load data of special days using 4 days peak load before special days and at the time of special day for the year 2002-2006. There are 20 items of special days in Bali that are used to be forecasted in the year 2005 and 2006 respectively. The test results showed an accurate forecasting with the mean average percentage error of 1.0335% and 1.5683% in the year 2005 and 2006 respectively.
Jiani Heng
2016-01-01
Full Text Available Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm, and WNN (Wavelet Neural Network, is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network models such as BPNN (Back Propagation Neural Network, GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm, and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.
H. Taherian
2014-05-01
Full Text Available The price signal in a competitive electricity market has a major importance in all planning and commissioning activities. Also, the electricity price has a non-deterministic nature and is affected by various parameters in short and long terms. Active players in electricity market need accurate and effective price forecasting for risk management. With the increased use of renewable energies, especially wind energy, the electricity price is being affected by this new parameter, as the intermittent nature of wind generation has further complicated the process of instantaneous balancing of power system demand against power generation. In this paper, using the Nord Pool electricity market data, the effect of wind units' generation on price forecasting is studied. The main idea is based on presenting an intelligent model for forecasting the Market Clearing Price through the use of a multilayer perceptron neural network based on hybrid genetic model and Imperialist Competitive algorithm. This hybrid model has a better accuracy, compared to the conventional neural networks (based on gradient-based optimization algorithms, and has the ability of converging towards the absolute optimum. The results verify the high accuracy of this model in short term electricity price forecasting.
Operation of Battery Energy Storage System in Demand Side using Local Load Forecasting
Hida, Yusuke; Yokoyama, Ryuichi; Shimizukawa, Jun; Iba, Kenji; Tanaka, Kouji; Seki, Tomomichi
Recently, the various political movements, which reduce CO2-emission, have been proposed against global warming. Therefore, battery energy storage systems (BESSs) such as NAS (sodium and sulfur) battery are attracting attention around the world. The first purpose of BESS was the improvement of load factors. The second purpose is the improvement of power quality, especially against voltage-sag. The recent interest is oriented to utilize BESS to mitigate the intermittency of renewable energy. NAS battery has two operation modes. The first one is a fixed pattern operation, which is time-schedule in advance. The second mode is the load following operation. Although this mode can perform more the flexible operation by adjusting the change of load, it has the risks of shortage/surplus of battery energy. In this paper, an accurate demand forecasting method, which is based on multiple regression models, is proposed. Using this load forecasting, the more advanced control of load following operation for NAS battery is proposed.
Bigdeli, N.; Afshar, K. [EE Department, IKIU, Qazvin (Iran); Amjady, N. [EE Department, Semnan University, Semnan (Iran)
2009-06-15
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)
Wind farm electrical power production model for load flow analysis
Segura-Heras, Isidoro; Escriva-Escriva, Guillermo; Alcazar-Ortega, Manuel [Institute for Energy Engineering, Universidad Politecnica de Valencia, Camino de Vera, s/n, edificio 8E, escalera F, 2a planta, 46022 Valencia (Spain)
2011-03-15
The importance of renewable energy increases in activities relating to new forms of managing and operating electrical power: especially wind power. Wind generation is increasing its share in the electricity generation portfolios of many countries. Wind power production in Spain has doubled over the past four years and has reached 20 GW. One of the greatest problems facing wind farms is that the electrical power generated depends on the variable characteristics of the wind. To become competitive in a liberalized market, the reliability of wind energy must be guaranteed. Good local wind forecasts are therefore essential for the accurate prediction of generation levels for each moment of the day. This paper proposes an electrical power production model for wind farms based on a new method that produces correlated wind speeds for various wind farms. This method enables a reliable evaluation of the impact of new wind farms on the high-voltage distribution grid. (author)
Reallocating Charging Loads of Electric Vehicles in Distribution Networks
Mohammed Jasim M. Al Essa
2016-02-01
Full Text Available In this paper, the charging loads of electric vehicles were controlled to avoid their impact on distribution networks. A centralized control algorithm was developed using unbalanced optimal power flow calculations with a time resolution of one minute. The charging loads were optimally reallocated using a central controller based on non-linear programming. Electric vehicles were recharged using the proposed control algorithm considering the network constraints of voltage magnitudes, voltage unbalances, and limitations of the network components (transformers and cables. Simulation results showed that network components at the medium voltage level can tolerate high uptakes of uncontrolled recharged electric vehicles. However, at the low voltage level, network components exceeded their limits with these high uptakes of uncontrolled charging loads. Using the proposed centralized control algorithm, these high uptakes of electric vehicles were accommodated in the network under study without the need of upgrading the network components.
USING ARTIFICIAL NEURAL NETWORKS (ANNs FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH
Vahid Nourani
2009-06-01
Full Text Available Without a doubt the carried sediment load by a river is the most important factor in creating and formation of the related Delta in the river mouth. Therefore, accurate forecasting of the river sediment load can play a significant role for study on the river Delta. However considering the complexity and non-linearity of the phenomenon, the classic experimental or physical-based approaches usually could not handle the problem so well. In this paper, Artificial Neural Network (ANN as a non-linear black box interpolator tool is used for modeling suspended sediment load which discharges to the Talkherood river mouth, located in northern west Iran. For this purpose, observed time series of water discharge at current and previous time steps are used as the model input neurons and the model output neuron will be the forecasted sediment load at the current time step. In this way, various schemes of the ANN approach are examined in order to achieve the best network as well as the best architecture of the model. The obtained results are also compared with the results of two other classic methods (i.e., linear regression and rating curve methods in order to approve the efficiency and ability of the proposed method.
USING ARTIFICIAL NEURAL NETWORKS (ANNs FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH
Vahid Nourani
2009-01-01
Full Text Available Without a doubt the carried sediment load by a river is the most important factor in creating and formation of the related Delta in the river mouth. Therefore, accurate forecasting of the river sediment load can play a significant role for study on the river Delta. However considering the complexity and non-linearity of the phenomenon, the classic experimental or physical-based approaches usually could not handle the problem so well. In this paper, Artificial Neural Network (ANN as a non-linear black box interpolator tool is used for modeling suspended sediment load which discharges to the Talkherood river mouth, located in northern west Iran. For this purpose, observed time series of water discharge at current and previous time steps are used as the model input neurons and the model output neuron will be the forecasted sediment load at the current time step. In this way, various schemes of the ANN approach are examined in order to achieve the best network as well as the best architecture of the model. The obtained results are also compared with the results of two other classic methods (i.e., linear regression and rating curve methods in order to approve the efficiency and ability of the proposed method.
Novel grey forecast model and its application
丁洪发; 舒双焰; 段献忠
2003-01-01
The advancement of grey system theory provides an effective analytic tool for power system load fore-cast. All kinds of presently available grey forecast models can be well used to deal with the short-term load fore-cast. However, they make big errors for medium or long-term load forecasts, and the load that does not satisfythe approximate exponential increasing law in particular. A novel grey forecast model that is capable of distin-guishing the increasing law of load is adopted to forecast electric power consumption (EPC) of Shanghai. Theresults show that this model can be used to greatly improve the forecast precision of EPC for a secondary industryor the whole society.
Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks
Zhisheng Zhang
2016-01-01
Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.
Effects of using passive filter for reduce electrical load harmonics
Sucita, T.
2016-04-01
Due to the use of electrical current load that uses a lot of electronic components (passive non-linear electrical loads), so the impact will cause harmonics in the electrical network system. These harmonics can unwittingly cause a relatively large loss in electrical energy consumption and can lower the power factor of an electrical installation. Limits how much the harmonic distortion that is installed on the load adjusted to the IEEE 519-1992 standard. The study was conducted by taking data on a network of electrical installation of a building using measuring devices Fluke 43B Power Quality Analyser. The data is then processed and consulted with the standard IEEE 519-1992. Once the data has a discrepancy with the standard, further made the filter design using linear passive components. The design is then installed on the network installation by means of simulated order harmonic losses can be overcome so that the circuit meets the IEEE standard installation by changing the parameters of the linear load L and C. The results of this study indicate that THDi value decreased after the installation of filters for phase R fell by 9.39%, the S phase decreased by 7.54% and for the T phase decreased by 16.88%. So that meets the IEEE standard by 15%.
A regime-switching stochastic volatility model for forecasting electricity prices
Exterkate, Peter; Knapik, Oskar
In a recent review paper, Weron (2014) pinpoints several crucial challenges outstanding in the area of electricity price forecasting. This research attempts to address all of them by i) showing the importance of considering fundamental price drivers in modeling, ii) developing new techniques...... for probabilistic (i.e. interval or density) forecasting of electricity prices, iii) introducing an universal technique for model comparison. We propose new regime-switching stochastic volatility model with three regimes (negative jump, normal price, positive jump (spike)) where the transition matrix depends...... 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....
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-25
This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.
Load management through agent based coordination of flexible electricity consumers
Clausen, Anders; Jørgensen, Bo Nørregaard
2015-01-01
Demand Response (DR) offers a cost-effective and carbonfriendly way of performing load balancing. DR describes a change in the electricity consumption of flexible consumers in response to the supply situation. In DR, flexible consumers may perform their own load balancing through load management....... In this paper, we propose an approach to perform such coordination through a Virtual Power Plant (VPP)[1]. We represent flexible electricity consumers as software agents and we solve the coordination problem through multi-objective multi-issue optimization using a mediator-based negotiation mechanism. We...... illustrate how we can coordinate flexible consumers through a VPP in response to external events simulating the need for load balancing services....
An Electrical Energy Consumption Monitoring and Forecasting System
J. L. Rojas-Renteria; T. D. Espinoza-Huerta; F. S. Tovar-Pacheco; J. L. Gonzalez-Perez; R. Lozano-Dorantes
2016-01-01
Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations...
Haida, T.; Muto, S.; Takahashi, Y.; Ishii, Y. [Tokyo Electric Power Co. Inc., Tokyo (Japan)
1997-07-20
This paper presents a regression based daily peak load forecasting method using multiple years data with trend cancellation and trend estimation techniques. Daily peak load heavily depends on temperature in daytime and is influenced by the other weather factors such as humidity. Since a characteristic of the load is varying, peak loads just before a forecasting day are more significant for the forecasting. The regression model can represent relationships between these weather factors and peak loads. However, the forecasting model is sometime not adequate for precise load forecasting. The regression model is well matched with the late data, but the model causes large forecasting errors in transitional seasons because of seasonal change of load characteristics. In order to forecast precisely through a year, a method of using seasonal or whole year data in past years is proposed. In this paper, two kinds of trend data processing techniques are described. The first is trend cancellation. The second is trend estimation. The trend cancellation technique removes annual load growth by means of division or subtraction processes with morning load on the forecasting day. The trend estimation technique estimates the trend between the forecasting year`s load and the past year`s load by using the variable transformation techniques. Performance of the both techniques verified with simulations on actual load data- is also described. 12 refs., 8 figs.
Jie Liang
2017-03-01
Full Text Available Electricity demand forecasting can provide the scientific basis for the country to formulate the power industry development strategy and the power-generating target, which further promotes the sustainable, healthy and rapid development of the national economy. In this paper, a new mathematical hybrid method is proposed to forecast electricity demand. In line with electricity demand feature, the framework of joint-forecasting model is established and divided into two procedures: firstly, the modified GM(1,1 model and the Logistic model are used to make single forecasting. Then, the induced ordered weighted harmonic averaging operator (IOWHA is applied to combine these two single models and make joint-forecasting. Forecasting results demonstrate that this new hybrid model is superior to both single-forecasting approaches and traditional joint-forecasting methods, thus verifying the high prediction validity and accuracy of mentioned joint-forecasting model. Finally, detailed forecasting-outcomes on electricity demand of China in 2016–2020 are discussed and displayed a slow-growth smoothly over the next five years.
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
Loading models for electric vehicles; Lademodelle fuer Elektrofahrzeuge
Mezger, Tomas [Forschungsstelle fuer Energiewirtschaft e.V., Muenchen (Germany)
2011-07-01
About 5 to 7 million electric vehicles have adverse effects on the electricity grid. Thus, there is a need to develop optimized loading concepts, which generate an added value both for the energy industry as well as for the vehicle owners. A two-stage analysis method is used for this. Within the first step it is examined, how defined fleet of electric vehicles can be loaded optimally with electricity taking into account the specific vehicle usage. In the second step, the charging models are again evaluated from a user perspective. The impact of the driving behavior and charge characteristics on the aging of the battery are considered specifically. Charging possibilities are identified in order to increase the battery life.
Evaluation of solar thermal storage for base load electricity generation
Adinberg, R.
2012-10-01
In order to stabilize solar electric power production during the day and prolong the daily operating cycle for several hours in the nighttime, solar thermal power plants have the options of using either or both solar thermal storage and fossil fuel hybridization. The share of solar energy in the annual electricity production capacity of hybrid solar-fossil power plants without energy storage is only about 20%. As it follows from the computer simulations performed for base load electricity demand, a solar annual capacity as high as 70% can be attained by use of a reasonably large thermal storage capacity of 22 full load operating hours. In this study, the overall power system performance is analyzed with emphasis on energy storage characteristics promoting a high level of sustainability for solar termal electricity production. The basic system parameters, including thermal storage capacity, solar collector size, and annual average daily discharge time, are presented and discussed.
Characterization of electric load with Information Theory quantifiers
Aquino, Andre L. L.; Ramos, Heitor S.; Frery, Alejandro C.; Viana, Leonardo P.; Cavalcante, Tamer S. G.; Rosso, Osvaldo A.
2017-01-01
This paper presents a study of the electric load behavior based on the Causality Complexity-Entropy Plane. We use a public data set, namely REDD, which contains detailed power usage information from several domestic appliances. In our characterization, we use the available power data of the circuit/devices of all houses. The Bandt-Pompe methodology combined with the Causality Complexity-Entropy Plane was used to identify and characterize regimes and behaviors over these data. The results showed that this characterization provides a useful insight into the underlying dynamics that govern the electric load.
Electricity spot price forecasting in free power market
Lilleberg, J.; Laitinen, E.K. [Vaasa Univ. (Finland)
1998-08-01
Deregulation has brought many changes to the electricity market. Freedom of choice has been granted to both the consumers and the utilities. Consumers may choose the seller of their energy. Utilities have a wider array of sources to acquire their electricity from. Also the types of sales contracts used are changing to fill the needs of this new situation. The consumers` right to choose has introduced a new risk uncertainty of volume, which was not true during the times of monopoly. As sold volume is unsure and the energy is not sold on same terms as it is bought, a price risk has to be dealt with also. The electric utility has to realize this, select a risk level that suits its business strategy and optimize its actions according to the selected risk level. The number of participants will grow as the electricity market integrates into a common market for Scandinavia and even Europe. Big customers are also taking a more active role in the market, further increasing the number of participants. This makes old bilateral arrangements outdated. New tools are needed to control the new business environment. The goal of this project has been to develop a theoretical model to predict the price in the Finnish electricity exchange, El-Ex Oy. An extensive literature review was conducted in order to (1) examine the solutions in deregulation of electricity markets in other countries, esp. in Norway and UK, (2) find similarities and differences in electricity exchange and exchanges generally and (3) find major sources of problems and inefficiency in the market
Electricity Consumption Forecasting in the Age of Big Data
Xiaojia Wang
2013-09-01
Full Text Available In the age of big data, information mining technology has undergone tremendous change; traditional forecasting mining technology has not been able to solve the information mining problems under a large scale of data. this paper put forward a modeling mechanism of information analysis and mining under the age of big data, the modeling mechanism is, first, construct the model of task decomposition of information by MapReduce tool, then, make data preprocessing and mining operation according to the single task data sheet, use mathematical model, artificial intelligence and other methods to construct the new ideas of information analysis and data mining under the age of big data, finally, a case study presented to demonstrate the feasibility and rationality of our approach.
2012-11-26
... COMMISSION Preoperational Testing of Onsite Electric Power Systems To Verify Proper Load Group Assignments... Power Systems to Verify Proper Load Group Assignments, Electrical Separation, and Redundancy.'' DG-1294... encompass preoperational testing of electrical power systems used to meet current Station...
Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models
Nguyen, Hang T.; Nabney, Ian T. [Non-linearity and Complexity Research Group, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET (United Kingdom)
2010-09-15
This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. (author)
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.
Cheng Yugui
2013-07-01
Full Text Available A kind of power forecast model combined cellular genetic algorithm with BP neural network was established in this article. Mid-long term power demand in urban areas was done load forecasting and analysis based on material object of the actual power consumption in urban areas of Nanchang. The results show that this method has the characteristic of the minimum training times, the shortest consumption time, the minimum error and the shortest operation time to obtain the best fitting effect.
Controlling Electricity Consumption by Forecasting its Response to Varying Prices
Corradi, Olivier; Ochsenfeld, Henning Peter; Madsen, Henrik
2013-01-01
electricity consumption using a one-way price signal. Estimation of the price-response is based on data measurable at grid level, removing the need to install sensors and communication devices between each individual consumer and the price-generating entity. An application for price-responsive heating systems......In a real-time electricity pricing context where consumers are sensitive to varying prices, having the ability to anticipate their response to a price change is valuable. This paper proposes models for the dynamics of such price-response, and shows how these dynamics can be used to control...
Electrical load management for the California water system
Krieg, B.; Lasater, I.; Blumstein, C.
1977-07-01
To meet its water needs California has developed an extensive system for transporting water from areas with high water runoff to areas with high water demand. This system annually consumes more than 6 billion kilowatt hours (kWh) of electricity for pumping water and produces more than 12 billion kWh/year of hydroelectric power. From the point of view of energy conservation, the optimum operation of the California water supply system would require that pumping be done at night and generation be done during the day. Night pumping would reduce electric power peak load demand and permit the pumps to be supplied with electricity from ''base load'' generating plants. Daytime hydro power generation would augment peak load power generation by fossil-fuel power plants and save fuel. The technical and institutional aspects of this type of electric power load management for water projects are examined for the purpose of explaining some of the actions which might be pursued and to develop recommendations for the California Energy Resources Conservation and Development Commission (ERCDC). The California water supply system is described. A brief description is given of various energy conservation methods, other than load management, that can be used in the management of water resources. An analysis of load management is presented. Three actions for the ERCDC are recommended: the Commission should monitor upcoming power contract negotiations between the utilities and the water projects; it should determine the applicability of the power-pooling provisions of the proposed National Energy Act to water systems; and it should encourage and support detailed studies of load management methods for specific water projects.
Nose Filho, Kenji; Araujo, Klayton A.M.; Maeda, Jorge L.Y.; Lotufo, Anna Diva P. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil)], Emails: kenjinose@yahoo.com.br, klayton_ama@hotmail.com, jorge-maeda@hotmail.com, annadiva@dee.feis.unesp.br
2009-07-01
This paper presents a development and implementation of a program to electrical load forecasting with data from a Brazilian electrical company, using four different architectures of neural networks of the MATLAB toolboxes: multilayer backpropagation gradient descendent with momentum, multilayer backpropagation Levenberg-Marquardt, adaptive network based fuzzy inference system and general regression neural network. The program presented a satisfactory performance, guaranteeing very good results. (author)
Ninh Nguyen Quang
2013-11-01
Full Text Available In this paper, an original scheduling approach for optimal dispatch of electrical Energy Storage Systems (ESS in modern distribution networks is proposed. The control system is based on fuzzy rules and does not use forecasts since it repairs the past history according to the real time data on the electrical energy cost, renewable energy production and load. When the system detects a worsening of performances, the fuzzy logic rule-based control system self-adapts its membership functions using an economic indicator. The common use, in the relevant literature, of forecasted values in such systems can lead to large errors and economic losses. Moreover the speed of calculation guaranteed by the fuzzy control system allows the execution of new calculations even with high frequency. After the Introduction section, where the state of the art on the topic is outlined, the problem formulation is presented and an interesting application of the considered approach to the control on a medium size battery with real world data is proposed.
Electricity Price Forecasting using Sale and Purchase Curves: The X-Model
Florian Ziel; Rick Steinert
2015-01-01
Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usually done in time series or data mining approaches, we model and utilize its true source: the sale and purchase curves of the electricity exchange. We will refer to this new model ...
Power quality load management for large spacecraft electrical power systems
Lollar, Louis F.
1988-01-01
In December, 1986, a Center Director's Discretionary Fund (CDDF) proposal was granted to study power system control techniques in large space electrical power systems. Presented are the accomplishments in the area of power system control by power quality load management. In addition, information concerning the distortion problems in a 20 kHz ac power system is presented.
Michael A. Fosberg
1987-01-01
Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.
Goldman, Charles A.; Eto, Joseph H.; Barbose, Galen L.
2002-05-01
Recurring electricity shortages and rolling blackouts were widely forecasted for summer 2001 in California. Despite these predictions, blackouts were never ordered - in large part, due to the dramatic reductions in electricity use throughout the state. Compared to summer 2000, Californians reduced electricity usage by 6 percent and average monthly peak demand by 8 percent. Our analysis suggests that these reductions were not caused by either the weather or the downturn in the state's economy; rather, they were the result of extraordinary efforts by Californians to reduce electricity consumption. Based on the California Independent System Operator's (CAISO) available operating reserve margin during summer 2001, we estimate that the peak load reductions, which ranged between 3,200 and 5,600 MW in the four summer months, potentially avoided between 50 and 160 hours of rolling blackouts. This extraordinary response by Californians can be attributed to several factors including media coverage and informational campaigns that affected public awareness and understanding, real and/or perceived increases in electricity rates, and various policies and programs deployed by state policymakers and regulators to facilitate customer load reductions. Among these programs, we review the state's 20/20 rebate program, the utilities' energy efficiency programs, programs or initiatives implemented by the California Energy Commission and other state agencies, and load management and demand response programs offered by the state's investor-owned electric utilities and the CAISO. We estimate that energy efficiency and onsite generation projects that were initiated in 2001 will account for about 1,100 MW of customer load reductions, once all projects are installed. These savings represent about 25-30 percent of the observed load reductions and are likely to persist for many years. The persistence of the remaining savings, which were due to changes that customers
Assessing and Reducing Miscellaneous Electric Loads (MELs) in Banks
Rauch, Emily M.
2012-09-01
Miscellaneous electric loads (MELs) are loads outside of a building's core functions of heating, ventilating, air conditioning, lighting, and water heating. MELs are a large percentage of total building energy loads. This report reviews methods for reducing MELs in Banks. Reducing MELs in a bank setting requires both local and corporate action. Corporate action centers on activities to prioritize and allocate the right resources to correct procurement and central control issues. Local action includes branch assessment or audits to identify specific loads and needs. The worksheet at the end of this guide can help with cataloging needed information and estimating savings potential. The following steps provide a guide to MEL reductions in Bank Branches. The general process has been adapted from a process developed for office buildings the National Renewable Energy Laboratory (NREL, 2011).
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)
2008-09-15
This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)
Broad electrical tuning of graphene-loaded plasmonic antennas.
Yao, Yu; Kats, Mikhail A; Genevet, Patrice; Yu, Nanfang; Song, Yi; Kong, Jing; Capasso, Federico
2013-03-13
Plasmonic antennas enable the conversion of light from free space into subwavelength volumes and vice versa, which facilitates the manipulation of light at the nanoscale. Dynamic control of the properties of antennas is desirable for many applications, including biochemical sensors, reconfigurable meta-surfaces and compact optoelectronic devices. The combination of metallic structures and graphene, which has gate-voltage dependent optical properties, is emerging as a possible platform for electrically controlled plasmonic devices. In this paper, we demonstrate in situ control of antennas using graphene as an electrically tunable load in the nanoscale antenna gap. In our experiments, we demonstrate electrical tuning of graphene-loaded antennas over a broad wavelength range of 650 nm (∼140 cm(-1), ∼10% of the resonance frequency) in the mid-infrared (MIR) region. We propose an equivalent circuit model to quantitatively analyze the tuning behavior of graphene-loaded antenna pairs and derive an analytical expression for the tuning range of resonant wavelength. In a separate experiment, we used doubly resonant antenna arrays to achieve MIR optical intensity modulation with maximum modulation depth of more than 30% and bandwidth of 600 nm (∼100 cm(-1), 8% of the resonance frequency). This study shows that combining graphene with metallic nanostructures provides a route to electrically tunable optical and optoelectronic devices.
Electric Loading Simulation System for Missile Wings and Rudders
QI Rong; LIN Hui; CHEN Ming
2006-01-01
The design and the realization of missile wings and rudders loading simulation system based on digital signal processor (DSP) TMS320LF2407 and direct torque control (DTC) servo driver ACS600 are discussed. The structure and opration principle for the system are presented. Speediness and elimination of superabundant torque are two key difficulties for electric loading simulation system. The method which can eliminate the superabundant torque is researched. Test results show the airflow resistance when missile wings and rudders are spreading can be rapidly simulated with high accuracy.
Usage of Lightning Arrester Line to Feed Light Electrical Loads
Hani B. Odeh
2009-01-01
Full Text Available In remote areas, light loads (tens of kilowatts are scattered and situated in the field of high voltage lines (66KV and above. These loads are very far from the main feeders/sub-stations (33KV-0.380KV. Feeding such loads in the traditional ways like provision of Diesel-Powered Stations, installation of new distribution lines from the Feeding Centers, or building new Sub-Stations are not practical ways from the economical point of view, because it requires huge additional expenses and will increase electrical power losses. These expenses are not worthy for such loads and therefore, it is necessary to search for other methods to supply them. One of these methods is to use the lightning arrester line as capacitive divider to supply the light loads. In this research, the induced voltage of the lightning arrester line was calculated when it is isolated from the earth. We found the capacitance between lightning arrester line versus the phases and lightning arrester. It was also found the selective power out of the lightning arrester line and the required length which is to be isolated from the earth keeping the main function of the lightning arrester line. When economically comparing between supplying the light electrical loads by traditional ways and the method of lightning arrester, it was found the advantage of using lightning arresters to supply such loads. Also, by using the traditional methods, it was noted that there is a power loss in the power transmission lines by a percentage of 1.8%.
Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression
Taieb, Souhaib Ben
2016-03-02
Smart electricity meters are currently deployed in millions of households to collect detailed individual electricity consumption data. Compared with traditional electricity data based on aggregated consumption, smart meter data are much more volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand in order to undertake appropriate planning of generation and distribution. We propose to estimate an additive quantile regression model for a set of quantiles of the future distribution using a boosting procedure. By doing so, we can benefit from flexible and interpretable models, which include an automatic variable selection. We compare our approach with three benchmark methods on both aggregated and disaggregated scales using a smart meter data set collected from 3639 households in Ireland at 30-min intervals over a period of 1.5 years. The empirical results demonstrate that our approach based on quantile regression provides better forecast accuracy for disaggregated demand, while the traditional approach based on a normality assumption (possibly after an appropriate Box-Cox transformation) is a better approximation for aggregated demand. These results are particularly useful since more energy data will become available at the disaggregated level in the future.
Forecasting electricity demand in South Africa: A critique of Eskom’s projections
Anastassios Pouris
2010-03-01
Full Text Available Within a short period, Eskom has applied to the National Energy Regulator of South Africa (NERSA for the third time since the 2008 electricity crisis, proposing a multiyear price determination for the periods 2010−2011 and 2012−2013. The new application, submitted at the end of September 2009, motivated for the debate of strategies with which the consequences of the proposed price hikes could be predicted, measured and controlled. In his presentation to Parliament in February 2009, Eskom’s then CEO, Mr Jacob Maroga presented the current energy situation in the country, the reasons for the crisis in 2007−2008, as well as the challenges of the future. The purpose of this paper is to contribute some new ideas and perspectives to Eskom’s existing arguments regarding the demand for electricity. The most important issue is the fact that Eskom does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. This study proposed that prices have a high impact on the demand for electricity (price elasticity of -0.5. Employing similar assumptions for the country’s economic growth as Eskom, the results of the forecasting exercise indicated a substantial decrease in demand (scenario 1: -31% in 2025 and scenario 2:-18% in 2025. This study’s findings contrasted significantly with Eskom’s projection, which has extensive implications as far as policy is concerned.
Zhang, Li; Jabbari, Faryar; Brown, Tim; Samuelsen, Scott
2014-12-01
Plug-in electric vehicles (PEVs) shift energy consumption from petroleum to electricity for the personal transportation sector. This work proposes a decentralized charging protocol for PEVs with grid operators updating the cost signal. Each PEV calculates its own optimal charging profile only once based on the cost signal, after it is plugged in, and sends the result back to the grid operators. Grid operators only need to aggregate charging profiles and update the load and cost. The existing PEV characteristics, national household travel survey (NHTS), California Independent System Operator (CAISO) demand, and estimates for future renewable generation in California are used to simulate PEV operation, PEV charging profiles, grid demand, and grid net load (demand minus renewable). Results show the proposed protocol has good performance for overnight net load valley filling if the costs to be minimized are proportional to the net load. Annual results are shown in terms of overnight load variation and comparisons are made with grid level valley filling results. Further, a target load can be approached in the same manner by using the gap between current load and the target load as the cost. The communication effort involved is quite modest.
Medium- and long-term electric power demand forecasting based on the big data of smart city
Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie
2017-08-01
Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.
I. V. Zhezhelenko
2006-01-01
Full Text Available The influence of limitations on possible theta-ordinate values of group non-uniform electrical load graphs on the rated values of theta peaks and theta troughs and, particularly, on the rated heating load value is considered in the paper. It is shown that neglect of limitations on graph theta- ordinates of an electrical load leads in some cases to errors in evaluation of load theta peaks and theta troughs that exceed the tolerable values by ±10 %.
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL
Joao Bosco de Castro
2010-11-01
Full Text Available This work aims to propose the application of the ARX model to forecast residential electricity consumption in Brazil. Such estimates are critical for decision making in the energy sector, from a technical, economic and environmentally sustainable standpoint. The demand for electricity follows a multiplicative model based on economic theory and involves four explanatory variables: the cost of residential electricity, the actual average income, the inflation of domestic utilities and the electricity consumption. The coefficients of the electricity consumption equation were determined using the ARX model, which considers the influence of exogenous variables to estimate the dependent variable and employs an autoregression process for residual modeling to improve the explanatory power. The resulting model has a determination coefficient of 95.4 percent and all estimated coefficients were significant at the 0.10 descriptive level. Residential electricity consumption estimates were also determined for January and February 2010 within the 95 percent confidence interval, which included the actual consumption figures observed. The proposed model has been shown to be useful for estimating residential electricity consumption in Brazil. Key-words: Time series. Electricity consumption. ARX modeling.
Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach
Karin Kandananond
2011-08-01
Full Text Available Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA, artificial neural network (ANN and multiple linear regression (MLR—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance
Huiru Zhao
2016-01-01
Full Text Available Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1 (GM (1,1, optimized by moth-flame optimization (MFO algorithm with rolling mechanism (Rolling-MFO-GM (1,1, was put forward. The parameters a and b of GM (1,1 were optimized by employing moth-flame optimization algorithm (MFO, which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1 for annual electricity consumption prediction when compared with least square regression (LSR, GM (1,1, FOA (fruit fly optimization-GM (1,1, MFO-GM (1,1, Rolling-LSR, Rolling-GM (1,1 and Rolling-FOA-GM (1,1. The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.
Zhanglin Peng
2015-04-01
Full Text Available Purpose: Electric vehicles industry has gotten a rapid development in the world, especially in the developed countries, but still has a gap among different countries or regions. The advanced industrialization experiences of the EVs in the developed countries will have a great helpful for the development of EVs industrialization in the developing countries. This paper seeks to research the industrialization path & prospect of American EVs by forecasting electric vehicles demand and its proportion to the whole car sales based on the historical 37 EVs monthly sales and Cars monthly sales spanning from Dec. 2010 to Dec. 2013, and find out the key measurements to help Chinese government and automobile enterprises to promote Chinese EVs industrialization. Design/methodology: Compared with Single Exponential Smoothing method and Double Exponential Smoothing method, Triple exponential smoothing method is improved and applied in this study. Findings: The research results show that: American EVs industry will keep a sustained growth in the next 3 months. Price of the EVs, price of fossil oil, number of charging station, EVs technology and the government market & taxation polices have a different influence to EVs sales. So EVs manufacturers and policy-makers can adjust or reformulate some technology tactics and market measurements according to the forecast results. China can learn from American EVs polices and measurements to develop Chinese EVs industry. Originality/value: The main contribution of this paper is to use the triple exponential smoothing method to forecast the electric vehicles demand and its proportion to the whole automobile sales, and analyze the industrial development of Chinese electric vehicles by American EVs industry.
Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.
2010-01-01
The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter
A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting
Francisco Martínez-Álvarez
2015-11-01
Full Text Available Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i to provide a compact mathematical formulation of the mainly used techniques; (ii to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.
A RBF neural network model with GARCH errors: Application to electricity price forecasting
Coelho, Leandro dos Santos [Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Santos, Andre A.P. [Department of Statistics, Universidad Carlos III de Madrid, C/ Madrid, 126, 28903 Getafe, Madrid (Spain)
2011-01-15
In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose an estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices. (author)
Forecasting and decision-making in electricity markets with focus on wind energy
Jónsson, Tryggvi
This thesis deals with analysis, forecasting and decision making in liberalised electricity markets. Particular focus is on wind power, its interaction with the market and the daily decision making of wind power generators. Among recently emerged renewable energy generation technologies, wind power...... has become the global leader in terms of installed capacity and advancement. This makes wind power an ideal candidate to analyse the impact of growing renewable energy generation capacity on the electricity markets. Furthermore, its present status of a significant supplier of electricity makes...... derivation of practically applicable tools for decision making highly relevant. The main characteristics of wind power differ fundamentally from those of conventional thermal power. Its effective generation capacity varies over time and is directly dependent on the weather. This dependency makes future...
Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.
2010-09-01
The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique
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.
Jaime Lloret
2013-08-01
Full Text Available Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.
Steinmetz, Tarcisio; Souza, Glauber; Ferreira, Sandro; Santos, Jose V. Canto dos; Valiati, Joao [Universidade do Vale do Rio dos Sinos (PIPCA/UNISINOS), Sao Leopoldo, RS (Brazil). Programa de Pos-Graduacao em Computacao Aplicada], Emails: trsteinmetz@unisinos.br, gsouza@unisinos.br, sferreira, jvcanto@unisinos.br, jfvaliati@unisinos.br
2009-07-01
We present a methodology for the extraction of rules from Artificial Neural Networks (ANN) trained to forecast the electric load demand. The rules have the ability to express the knowledge regarding the behavior of load demand acquired by the ANN during the training process. The rules are presented to the user in an easy to read format, such as IF premise THEN consequence. Where premise relates to the input data submitted to the ANN (mapped as fuzzy sets), and consequence appears as a linear equation describing the output to be presented by the ANN, should the premise part holds true. Experimentation demonstrates the method's capacity for acquiring and presenting high quality rules from neural networks trained to forecast electric load demand for several amounts of time in the future. (author)
Long Term Energy Consumption Forecasting Using Genetic Programming
KARABULUT, Korhan; Alkan, Ahmet; YILMAZ, Ahmet
2008-01-01
Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information, such as climate and previous load demand data. In this paper, a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. ...
The control algorithm improving performance of electric load simulator
Guo, Chenxia; Yang, Ruifeng; Zhang, Peng; Fu, Mengyao
2017-01-01
In order to improve dynamic performance and signal tracking accuracy of electric load simulator, the influence of the moment of inertia, stiffness, friction, gaps and other factors on the system performance were analyzed on the basis of researching the working principle of load simulator in this paper. The PID controller based on Wavelet Neural Network was used to achieve the friction nonlinear compensation, while the gap inverse model was used to compensate the gap nonlinear. The compensation results were simulated by MATLAB software. It was shown that the follow-up performance of sine response curve of the system became better after compensating, the track error was significantly reduced, the accuracy was improved greatly and the system dynamic performance was improved.
Load shift potential of electric vehicles in Europe
Babrowski, Sonja; Heinrichs, Heidi; Jochem, Patrick; Fichtner, Wolf
2014-06-01
Many governments highly encourage electric mobility today, aiming at a high market penetration. This development would bring forth an impact on the energy system, which strongly depends on the driving and charging behavior of the users. While an uncontrolled immediate charging might strain the local grid and/or higher peak loads, there are benefits to be gained by a controlled charging. We examine six European mobility studies in order to display the effects of controlled and uncontrolled unidirectional charging. Taking into account country-specific driving patterns, we generate for each country a charging load curve corresponding to uncontrolled charging and consider the corresponding parking time at charging facilities in order to identify load shift potentials. The main results are that besides the charging power of the vehicles, the possibility to charge at the work place has a significant influence on the uncontrolled charging curve. Neither national nor regional differences are as significant. When charging is only possible at home, the vehicle availability at charging facilities during the day for all countries is at least 24%. With the additional possibility to charge at work, at least 45% are constantly available. Accordingly, we identified a big potential for load shifting through controlled charging.
Lee, C.E.; Pangburn, J.W.
1996-06-01
In order to forecast highway pavement performance and to design adequate pavement structures, detailed traffic loading information is essential. Traffic data collected by two unique weigh-in-motion (WIM) systems located in the southbound lanes of US 50 in east Texas have been analyzed and used to develop a methodology for forecasting future traffic loading patterns. The WIM systems, which have been in service continually since late 1992, have collected such data as the date, time, speed, lateral lane position, axle spacings, and wheel loads for about 7,500 individual vehicles per day. Thermocouples in the air and embedded in the pavement have measured and recorded hourly air and pavement temperatures, respectively.
HTGR-GT and electrical load integrated control
Chan, T.; Openshaw, F.; Pfremmer, D.
1980-05-01
A discussion of the control and operation of the HTGR-GT power plant is presented in terms of its closely coupled electrical load and core cooling functions. The system and its controls are briefly described and comparisons are made with more conventional plants. The results of analyses of selected transients are presented to illustrate the operation and control of the HTGR-GT. The events presented were specifically chosen to show the controllability of the plant and to highlight some of the unique characteristics inherent in this multiloop closed-cycle plant.
Ashfaq Ahmad
2015-12-01
Full Text Available In the operation of a smart grid (SG, day-ahead load forecasting (DLF is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN, predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN-based model for SGs is able to capture the non-linearity(ies in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 % .
Short-Term Load Forecast Based on Fuzzy Neural Network%基于模糊神经网络的电力负荷短期预测
范山东; 赵宏宇
2013-01-01
The forecast of the accurate short-term load is the important work for the power company.which is of great importance to determinting the Motor unit off/on reasonably and the plan of fuel supply .trading electricity.An approach based on the fuzzy neural network and genetic algorithms is proposed for the short-term load forecast and this algorithms' superiority and accuracy is analyzed through MATLAB.%电力系统短期负荷预测是电力部门的一项重要工作,它对合理安排机组启停、确定燃料供应计划、进行电力交易等都具有重要的意义.应用模糊神经网络结合遗传算法实现预测系统通过对历史数据的自适应学习建立的模糊预测模型,算法上采用改进的BP算法.通过MATLAB仿真分析了该预测系统的优越性和准确性.
Long term forecasting of hourly electricity consumption in local areas in Denmark
Møller Andersen, Frits; Larsen, Helge V.; Gaardestrup, R.B.
2013-01-01
Long term projections of hourly electricity consumption in local areas are important for planning of the transmission grid. In Denmark, at present the method used for grid planning is based on statistical analysis of the hour of maximum load and for each local area the maximum load is projected...... to change proportional to changes in the aggregated national electricity consumption. That is, specific local conditions are not considered. Yet, from measurements of local consumption we know that:. •consumption profiles differ between local areas,•consumption by categories of customers contribute....... The model describes the entire profile of hourly consumption and is a first step towards differentiated local predictions of electricity consumption.The model is based on metering of aggregated hourly consumption at transformer stations covering selected local areas and on national statistics of hourly...
Wang, Jianzong; Chen, Yanjun; Hua, Rui; Wang, Peng; Fu, Jia
2012-02-01
Photovoltaic is a method of generating electrical power by converting solar radiation into direct current electricity using semiconductors that exhibit the photovoltaic effect. Photovoltaic power generation employs solar panels composed of a number of solar cells containing a photovoltaic material. Due to the growing demand for renewable energy sources, the manufacturing of solar cells and photovoltaic arrays has advanced considerably in recent years. Solar photovoltaics are growing rapidly, albeit from a small base, to a total global capacity of 40,000 MW at the end of 2010. More than 100 countries use solar photovoltaics. Driven by advances in technology and increases in manufacturing scale and sophistication, the cost of photovoltaic has declined steadily since the first solar cells were manufactured. Net metering and financial incentives, such as preferential feed-in tariffs for solar-generated electricity; have supported solar photovoltaics installations in many countries. However, the power that generated by solar photovoltaics is affected by the weather and other natural factors dramatically. To predict the photovoltaic energy accurately is of importance for the entire power intelligent dispatch in order to reduce the energy dissipation and maintain the security of power grid. In this paper, we have proposed a big data system--the Solar Photovoltaic Power Forecasting System, called SPPFS to calculate and predict the power according the real-time conditions. In this system, we utilized the distributed mixed database to speed up the rate of collecting, storing and analysis the meteorological data. In order to improve the accuracy of power prediction, the given neural network algorithm has been imported into SPPFS.By adopting abundant experiments, we shows that the framework can provide higher forecast accuracy-error rate less than 15% and obtain low latency of computing by deploying the mixed distributed database architecture for solar-generated electricity.
Crack instability of ferroelectric solids under alternative electric loading
Chen, Hao-Sen; Wang, He-Ling; Pei, Yong-Mao; Wei, Yu-Jie; Liu, Bin; Fang, Dai-Ning
2015-08-01
The low fracture toughness of the widely used piezoelectric and ferroelectric materials in technological applications raises a big concern about their durability and safety. Up to now, the mechanisms of electric-field induced fatigue crack growth in those materials are not fully understood. Here we report experimental observations that alternative electric loading at high frequency or large amplitude gives rise to dramatic temperature rise at the crack tip of a ferroelectric solid. The temperature rise subsequently lowers the energy barrier of materials for domain switch in the vicinity of the crack tip, increases the stress intensity factor and leads to unstable crack propagation finally. In contrast, at low frequency or small amplitude, crack tip temperature increases mildly and saturates quickly, no crack growth is observed. Together with our theoretical analysis on the non-linear heat transfer at the crack tip, we constructed a safe operating area curve with respect to the frequency and amplitude of the electric field, and validated the safety map by experiments. The revealed mechanisms about how electro-thermal-mechanical coupling influences fracture can be directly used to guide the design and safety assessment of piezoelectric and ferroelectric devices.
An electricity price model with consideration to load and gas price effects
黄民翔; 陶小虎; 韩祯祥
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.
Obara, Shin'ya
An all-electric home using an electric storage heater with safety and cleaning is expanded. However, the general electric storage heater leads to an unpleasant room temperature and energy loss by the overs and shorts of the amount of heat radiation when the climate condition changes greatly. Consequently, the operation of the electric storage heater introduced into an all-electric home, a storage type electric water heater, and photovoltaics was planned using weather forecast information distributed by a communication line. The comfortable evaluation (the difference between a room-temperature target and a room-temperature result) when the proposed system was employed based on the operation planning, purchase electric energy, and capacity of photovoltaics was investigated. As a result, comfortable heating operation was realized by using weather forecast data; furthermore, it is expected that the purchase cost of the commercial power in daytime can be reduced by introducing photovoltaics. Moreover, when the capacity of the photovoltaics was increased, the surplus power was stored in the electric storage heater, but an extremely unpleasant room temperature was not shown in the investigation ranges of this paper. By obtaining weather information from the forecast of the day from an external service using a communication line, the heating system of the all-electric home with low energy loss and comfort temperature is realizable.
赵宇红; 汪普林; 梁海滨
2011-01-01
电力系统短期负荷预测是电力生产部门的重要工作之一，本文利用径向基函数网络（RBF）进行负荷预测，针对RBF在负荷预测中隐含层节点数难求问题，提出了一种改进的最近邻聚类学习算法即可解决该难点，又可提高RBF神经网络收敛速度和负荷预测精度．根据某地区电网的实例进行研究，结果发现本文算法比改进前的算法预测的最小、最大相对误差分别减小0．14和1．12，证明了改进后算法有效性和可行性，为电力系统负荷预测提供了一种新途径．%Power system Short term load forecasting is one important work of the electricity production sector. In this paper,radial basis function network （RBF） is used in load forecast ing. Load forecasting for the RBF in the hidden layer nodes is hard to find. An improved nearest neighbor clustering algorithm is proposed to solve the difficulties and improve RBF neural network convergence speed and load forecasting accuracy. According to the instance of a regional power grid study,we found that the minimum,maximmn relative error were reduced by 0. 14 and 1.12,if we used the improved algorithm to predict. Case study results prove its effectiveness and feasibility. It provides a new way for the power system load forecasting.
On load flow control in electric power systems
Herbig, Arnim
2000-01-01
This dissertation deals with the control of active power flow, or load flow in electric power systems. During the last few years, interest in the possibilities to control the active power flows in transmission systems has increased significantly. There is a number of reasons for this, coming both from the application side - that is, from power system operations - and from the technological side. where advances in power electronics and related technologies have made new system components available. Load flow control is by nature a multi-input multi-output problem, since any change of load flow in one line will be complemented by changes in other lines. Strong cross-coupling between controllable components is to be expected, and the possibility of adverse interactions between these components cannot be rejected straightaway. Interactions with dynamic phenomena in the power system are also a source of concern. Three controllable components are investigated in this thesis, namely the controlled series capacitor (CSC), the phase angle regulator (PAR), and the unified power flow controller (UPFC). Properties and characteristics of these devices axe investigated and discussed. A simple control strategy is proposed. This strategy is then analyzed extensively. Mathematical methods and physical knowledge about the pertinent phenomena are combined, and it is shown that this control strategy can be used for a fairly general class of devices. Computer simulations of the controlled system provide insight into the system behavior in a system of reasonable size. The robustness and stability of the control system are discussed as are its limits. Further, the behavior of the control strategy in a system where the modeling allows for dynamic phenomena are investigated with computer simulations. It is discussed under which circumstances the control action has beneficial or detrimental effect on the system dynamics. Finally, a graphical approach for analyzing the effect of controllers
D6.2–Load and generation forecasting methods and prototypes
Madsen, Per Printz; Dueñas, Lara Pérez; Moraga, Carlos Castaño
The objective of the forecasting module in ENCOURAGE Platform is to provide the system with data of what the consumption and the production will be in the next hours, so that the energy management modules can decide the future strategies in order to optimise energy flows and decrease overall...... consumption. Energy consumption forecasting and renewable energy generation forecasting are two completely different problems that have been addressed separately, although they require similar inputs and a similar architecture. The modelling and forecasting of the energy consumed by a building usually leads...... ahead. After assessment of the different available methods, the modelling has been achieved through neural networks, and compared to a persistence model that has served as baseline for comparison, obtaining good results. Forecasting of renewable power generation is a significantly more difficult task...
Bartos, Matthew; Chester, Mikhail; Johnson, Nathan; Gorman, Brandon; Eisenberg, Daniel; Linkov, Igor; Bates, Matthew
2016-11-01
Climate change may constrain future electricity supply adequacy by reducing electric transmission capacity and increasing electricity demand. The carrying capacity of electric power cables decreases as ambient air temperatures rise; similarly, during the summer peak period, electricity loads typically increase with hotter air temperatures due to increased air conditioning usage. As atmospheric carbon concentrations increase, higher ambient air temperatures may strain power infrastructure by simultaneously reducing transmission capacity and increasing peak electricity load. We estimate the impacts of rising ambient air temperatures on electric transmission ampacity and peak per-capita electricity load for 121 planning areas in the United States using downscaled global climate model projections. Together, these planning areas account for roughly 80% of current peak summertime load. We estimate climate-attributable capacity reductions to transmission lines by constructing thermal models of representative conductors, then forcing these models with future temperature projections to determine the percent change in rated ampacity. Next, we assess the impact of climate change on electricity load by using historical relationships between ambient temperature and utility-scale summertime peak load to estimate the extent to which climate change will incur additional peak load increases. We find that by mid-century (2040-2060), increases in ambient air temperature may reduce average summertime transmission capacity by 1.9%-5.8% relative to the 1990-2010 reference period. At the same time, peak per-capita summertime loads may rise by 4.2%-15% on average due to increases in ambient air temperature. In the absence of energy efficiency gains, demand-side management programs and transmission infrastructure upgrades, these load increases have the potential to upset current assumptions about future electricity supply adequacy.
Pablo García
2013-06-01
Full Text Available Thanks to the built in intelligence (deployment of new intelligent devices and sensors in places where historically they were not present, the Smart Grid and Microgrid paradigms are able to take advantage from aggregated load forecasting, which opens the door for the implementation of new algorithms to seize this information for optimization and advanced planning. Therefore, accuracy in load forecasts will potentially have a big impact on key operation factors for the future Smart Grid/Microgrid-based energy network like user satisfaction and resource saving, and new methods to achieve an efficient prediction in future energy landscapes (very different from the centralized, big area networks studied so far. This paper proposes different improved models to forecast next day’s aggregated load using artificial neural networks, taking into account the variables that are most relevant for the aggregated. In particular, seven models based on the multilayer perceptron will be proposed, progressively adding input variables after analyzing the influence of climate factors on aggregated load. The results section presents the forecast from the proposed models, obtained from real data.
Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian
2017-01-01
The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.
Uses and Applications of Climate Forecasts for Power Utilities.
Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David
1995-05-01
The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector.
Ping Jiang
2016-08-01
Full Text Available The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist in the literature, there have been few investigations on improving the forecasting effectiveness of electricity price from the perspective of reducing the volatility of data with satisfactory accuracy. Based on reducing the volatility of the electricity price and the forecasting nature of the radial basis function network (RBFN, this paper successfully develops a two-stage model to forecast the day-ahead electricity price, of which the first stage is particle swarm optimization (PSO-core mapping (CM with self-organizing-map and fuzzy set (PCMwSF, and the second stage is selection rule (SR. The PCMwSF stage applies CM, fuzzy set and optimized weights to obtain the future price, and the SR stage is inspired by the forecasting nature of RBFN and effectively selects the best forecast during the test period. The proposed model, i.e., CM-PCMwSF-SR, not only overcomes the difficulty of reducing the high volatility of the electricity price but also leads to a superior forecasting effectiveness than benchmarks.
Empirical Investigations of the Opportunity Limits of Automatic Residential Electric Load Shaping
Cruickshank, Robert F.; Henze, Gregor P.; Balaji, Rajagopalan; Hodge, Bri-Mathias S.; Florita, Anthony R.
2017-05-11
Residential electric load shaping is often modeled as infrequent, utility-initiated, short-duration deferral of peak demand through direct load control. In contrast, modeled herein is the potential for frequent, transactive, intraday, consumer-configurable load shaping for storage-capable thermostatically controlled electric loads (TCLs), including refrigerators, freezers, and hot water heaters. Unique to this study are 28 months of 15-minute-interval observations of usage in 101 homes in the Pacific Northwest United States that specify exact start, duration, and usage patterns of approximately 25 submetered loads per home. The magnitudes of the load shift from voluntarily-participating TCL appliances are aggregated to form hourly upper and lower load-shaping limits for the coordination of electrical generation, transmission, distribution, storage, and demand. Empirical data are statistically analyzed to define metrics that help quantify load-shaping opportunities.
Cruickshank, Robert F.; Henze, Gregor P.; Balaji, Rajagopalan; Hodge, Bri-Mathias S.; Florita, Anthony R.
2017-04-01
Residential electric load shaping is often modeled as infrequent, utility-initiated, short-duration deferral of peak demand through direct load control. In contrast, modeled herein is the potential for frequent, transactive, intraday, consumer-configurable load shaping for storage-capable thermostatically controlled electric loads (TCLs), including refrigerators, freezers, and hot water heaters. Unique to this study are 28 months of 15-minute-interval observations of usage in 101 homes in the Pacific Northwest United States that specify exact start, duration, and usage patterns of approximately 25 submetered loads per home. The magnitudes of the load shift from voluntarily-participating TCL appliances are aggregated to form hourly upper and lower load-shaping limits for the coordination of electrical generation, transmission, distribution, storage, and demand. Empirical data are statistically analyzed to define metrics that help quantify load-shaping opportunities.
Wang, Qin; Wu, Hongyu; Florita, Anthony R.; Brancucci Martinez-Anido, Carlo; Hodge, Bri-Mathias
2016-12-01
The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was compared through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Finally, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.
Jin-peng Liu
2017-07-01
Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.
Summary of Market Opportunities for Electric Vehicles and Dispatchable Load in Electrolyzers
Denholm, Paul [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Eichman, Joshua [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Markel, Tony [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ma, Ookie [U.S Department of Energy, Washington, DC (United States)
2015-05-19
Electric vehicles (EVs) and electrolyzers are potentially significant sources of new electric loads. Both are flexible in that the amount of electricity consumed can be varied in response to a variety of factors including the cost of electricity. Because both EVs and electrolyzers can control the timing of electricity purchases, they can minimize energy costs by timing the purchases of energy to periods of lowest costs.
Ping Jiang
2014-01-01
Full Text Available With rapid economic growth, electricity demand is clearly increasing. It is difficult to store electricity for future use; thus, the electricity demand forecast, especially the electricity consumption forecast, is crucial for planning and operating a power system. Due to various unstable factors, it is challenging to forecast electricity consumption. Therefore, it is necessary to establish new models for accurate forecasts. This study proposes a hybrid model, which includes data selection, an abnormality analysis, a feasibility test, and an optimized grey model to forecast electricity consumption. First, the original electricity consumption data are selected to construct different schemes (Scheme 1: short-term selection and Scheme 2: long-term selection; next, the iterative algorithm (IA and cuckoo search algorithm (CS are employed to select the best parameter of GM(1,1. The forecasted day is then divided into several smooth parts because the grey model is highly accurate in the smooth rise and drop phases; thus, the best scheme for each part is determined using the grey correlation coefficient. Finally, the experimental results indicate that the GM(1,1 optimized using CS has the highest forecasting accuracy compared with the GM(1,1 and the GM(1,1 optimized using the IA and the autoregressive integrated moving average (ARIMA model.
Multi-Temporal Decomposed Wind and Load Power Models for Electric Energy Systems
Abdel-Karim, Noha
This thesis is motivated by the recognition that sources of uncertainties in electric power systems are multifold and may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, however, the uncertain price of electricity and hard-to-predict power produced by renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. The near-real-time power imbalances are compensated by means of frequency regulation and generally require fast-responding costly resources. Because of this, a more accurate forecast and look-ahead scheduling would result in a reduced need for expensive power balancing. Similarly, long-term planning and seasonal maintenance need to take into account long-term demand forecast as well as how the short-term generation scheduling is done. The better the demand forecast, the more efficient planning will be as well. Moreover, computer algorithms for scheduling and planning are essential in helping the system operators decide what to schedule and planners what to build. This is needed given the overall complexity created by different abilities to adjust the power output of generation technologies, demand uncertainties and by the network delivery constraints. Given the growing presence of major uncertainties, it is likely that the main control applications will use more probabilistic approaches. Today's predominantly deterministic methods will be replaced by methods which account for key uncertainties as decisions are made. It is well-understood that although demand and wind power cannot be predicted at very high accuracy, taking into consideration predictions and scheduling in a look-ahead way over several time horizons generally results in more efficient and reliable utilization, than when decisions are made assuming deterministic, often worst-case scenarios. This change is in approach is going to ultimately require new
Qi, Weiran; Miao, Hongxia; Miao, Xuejiao; Xiao, Xuanxuan; Yan, Kuo
2016-10-01
In order to ensure the safe and stable operation of the prefabricated substations, temperature sensing subsystem, temperature remote monitoring and management subsystem, forecast subsystem are designed in the paper. Wireless temperature sensing subsystem which consists of temperature sensor and MCU sends the electrical equipment temperature to the remote monitoring center by wireless sensor network. Remote monitoring center can realize the remote monitoring and prediction by monitoring and management subsystem and forecast subsystem. Real-time monitoring of power equipment temperature, history inquiry database, user management, password settings, etc., were achieved by monitoring and management subsystem. In temperature forecast subsystem, firstly, the chaos of the temperature data was verified and phase space is reconstructed. Then Support Vector Machine - Particle Swarm Optimization (SVM-PSO) was used to predict the temperature of the power equipment in prefabricated substations. The simulation results found that compared with the traditional methods SVM-PSO has higher prediction accuracy.
Mazurek, P. [Instytut Automatyki Systemow Energetycznych, Wroclaw (Poland)
1995-07-01
Application of the ITSM software package is presented together with the used iterative time series modeling methodology. The results obtained for the National Power System 24 hour load forecast were calculated for 200 days with acceptable accuracy. A time required for input data analysis and single forecast calculations was approx. 20 minutes on standard IBM PC. (author). 5 refs., 4 figs., 2 tabs.
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.
A cooling change-point model of community-aggregate electrical load
Ali, Muhammad Tauha; Mokhtar, Marwan; Chiesa, Matteo; Armstrong, Peter [Mechanical Engineering Program, Masdar Institute of Science and Technology, PO Box 54224, Abu Dhabi (United Arab Emirates)
2011-01-15
Estimates of daily electrical cooling load for a city of 800,000 are developed based on the relationship between weather variables and daily-average electricity consumption over 1 year. The relationship is found to be nearly linear above a threshold temperature. Temperature and humidity were found to be the largest, at 59%, and second largest, at 21%, contributors to electrical cooling load. Direct normal irradiation intercepted by a vertical cylinder, DNI sin {theta}, was found to be a useful explanatory variable when modeling aggregates of buildings without a known or dominant orientation. The best study case model used DNI sin {theta} and diffuse horizontal irradiation (DHI) as distinct explanatory variables with annual electrical cooling load contributions of 9% and 11% respectively. Although the seasonal variation in electrical cooling load is large - on peak summer days more than 1.5 times the winter base load - the combined direct and diffuse solar contribution is essentially flat through the year, a condition at odds with the common assumption that solar cooling always provides a good match between supply and demand. The final model gives an electrical cooling load estimate for Abu Dhabi Island that corresponds to 40% of the total annual electrical load and 61% on the peak day. (author)
An electricity price model with consideration to load and gas price effects
黄民翔; 陶小虎; 韩祯祥
2003-01-01
Some characteristics of the electricity load and prices are studied, and the relationship between electricity prices and gas (fnel) prices is analyzed in this paper. Because electricity prices are strongly depen-dent 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.
Impacts of Electric Vehicle Loads on Power Distribution Systems
Pillai, Jayakrishnan Radhakrishna; Bak-Jensen, Birgitte
2010-01-01
Electric vehicles (EVs) are the most promising alternative to replace a significant amount of gasoline vehicles to provide cleaner, CO2 free and climate friendly transportation. On integrating more electric vehicles, the electric utilities must analyse the related impacts on the electricity syste...... be accommodated in the network with the smart charging mode. The extent of integrating EVs in an area is constrained by the EV charging behavior and the safe operational limits of electricity system parameters....... operation. This paper investigates the effects on the key power distribution system parameters like voltages, line drops, system losses etc. by integrating electric vehicles in the range of 0-50% of the cars with different charging capacities. The dump as well as smart charging modes of electric vehicles......Electric vehicles (EVs) are the most promising alternative to replace a significant amount of gasoline vehicles to provide cleaner, CO2 free and climate friendly transportation. On integrating more electric vehicles, the electric utilities must analyse the related impacts on the electricity system...
Energy forecast. Final report; Energiudsigten. Slutrapport
2010-04-15
A number of instruments, i.e. Internet, media campaigns, boxes displaying electricity prices (SEE1) and spot contract has been tested for households to shift their electricity consumption to times when prices are low. Of the implemented media campaigns, only the daily viewing of Energy forecast on TV had an impact. Consumers gained greater knowledge of electricity prices and electricity consumption loads, but only showed little interest in shifting electricity consumption. However, a measurable effect appeared at night with the group that had both concluded a spot contract and received an SEE1. These factors increase the awareness of the price of electricity and the possibility of shifting electricity consumption. (Energy 10)
Antunes de Azevedo, J.
2015-12-01
High air temperatures have an impact on energy consumption, since the demand for cooling fans and air conditioning increases. With current climate projections indicating a general increase in air temperatures, as well as more frequent and intense heat waves, cooling energy demand will increase with time and should therefore be considered by industry and policy makers. Cooling degree days (CDD) are a standard approach used by energy industry to estimate cooling demand. The methodology compares ambient temperatures with a base value for air temperature considered representative of the city being analysed. However, due to the Urban Heat Island effect, temperature and energy consumption will vary considerably across a city. Hence, for CDD to be estimated across an urban area, air temperature data from dense urban networks are required. This study analysed air temperature data available from a dense urban meteorological network to estimate CDD and cooling needs across Birmingham-UK for summer 2013. From the results, it was possible to identify the potential role and limitations of urban meteorological networks in forecasting electricity demand within a city for future climate scenarios.
Electric Power Load Analysis (EPLA) for Surface Ships
2012-09-17
ESM can be based on a host of technologies to include batteries, flywheels , and ultra-capacitors. Energy storage can be provided for use in a...electrical generation, energy storage , and power conversion components and equipment and current requirements for electrical distribution equipment and...Demand factor 4 3.7 Demand power 4 3.8 Electric and propulsion plant concept of operations 4 3.9 Emergency ship control 4 3.10 Energy storage
Fidalgo, J.N. [Instituto de Engenharia de Sistema e Computadores (INESC), Porto (Portugal). E-mail: jfidalgo@inescn.pt
1999-07-01
This paper presents the model developed for current intensity forecasting at the substation terminals. The main objective consists of regression process definition which allows some estimations on the future values for those currents, based on related historical data. Consideration of different time scheduling is intended. Neuronal networks have been used as regression basic tool. Finally, the results obtained up to the present are presented which demonstrate that the adopted strategy and tools are suitable for the objective to be attained.
Detection of Periodic Beacon Loads in Electrical Distribution Substation Data
Hammerstrom, Donald J.; Guttromson, Ross T.; Lu, Ning; Boyd, Paul A.; Trudnowski, Daniel; Chassin, David P.; Bonebrake, Christopher A.; Shaw, James M.
2006-05-31
This research explores methods for identifying a whether a load is sending a signal to the utility SCADA system. Such a system can identify whether various loads are signialing using existing SCADA infrastructure, that is, without added, high cost communications infrastructure.
Andreas Georgantopoulos
2012-01-01
Full Text Available This paper tests for the existence and direction of causality between electricity consumption and real gross domestic product for Greece. The study examines a trivariate system with capital formation for the period 1980-2010. Robust empirical results indicate that all variables are integrated of order one and cointegration analysis reports that cointegrating relationship exists between the variables. VAR/VEC approach suggests that all variables return to the long-run equilibrium whenever there is a deviation from the cointegrating relationship and that unidirectional causal links exists running from capital formation and electricity consumption to RGDP in the short-run implying that the economy of Greece is strongly energy dependent. Forecasts for the period 2011-2020 indicate increasing consumption of electricity and positive growth rates from 2013. Policy makers will need to liberalise the electricity sector and to turn the economy towards renewable and natural gas sources in order to reduce imports of oil and coal dependency.
Dejan Mirčetić
2016-08-01
Full Text Available The paper focuses on the problem of forklifts engagement in warehouse loading operations. Two expert system (ES models are created using several machine learning (ML models. Models try to mimic expert decisions while determining the forklifts engagement in the loading operation. Different ML models are evaluated and adaptive neuro fuzzy inference system (ANFIS and classification and regression trees (CART are chosen as the ones which have shown best results for the research purpose. As a case study, a central warehouse of a beverage company was used. In a beverage distribution chain, the proper engagement of forklifts in a loading operation is crucial for maintaining the defined customer service level. The created ES models represent a new approach for the rationalization of the forklifts usage, particularly for solving the problem of the forklifts engagement incargo loading. They are simple, easy to understand, reliable, and practically applicable tool for deciding on the engagement of the forklifts in a loading operation.
基于支持向量机的变电站行业负荷构成比例预测%Industry Load Composition Proportion Forecasting of Substation Based on SVM
何春光; 陆骏; 卢志明; 姜春莹; 叶宇峰
2015-01-01
A new methodology based on support vector machines (SVM) for the industry load proportion forecasting of a substation is presented to solve the problem that parameters of substation composite load model are randomly time-varying. The SVM algorithm is used to forecast a substation daily load curve and extract characteristic quantities of the substation daily load. Based on this, typical characteristic quantities of each industry are obtained through fuzzy C-means clustering with the consumer daily load curve from load control system and then project weights on the substation daily load characteristic quantities respectively. Load proportion of each industry is finally worked out by further calculation of the weights. According to the characteristics of a region’s electricity utilization, this prediction method is taken to forecast industry load composition proportion of a substation in the region on its summer peak load day. The result shows that the approach is consistent with the actual operation of the grid.%为解决变电站综合负荷模型参数的随机时变性问题，提出一种基于支持向量机(support vector machines，SVM)的变电站用电行业负荷构成的预测方法。运用SVM算法预测变电站日负荷曲线，提取变电站日负荷特征量。在此基础上，利用负荷控制系统的用户日负荷曲线，通过模糊 C 均值聚类获得各行业的典型特征量，将其分别投影到变电站日负荷特征量上；然后进一步计算权值，得到各行业负荷比例。根据某地区的用电特点，对该地区某变电站的夏季最大负荷日的行业构成比例进行预测，结果表明该方法符合电网实际运行情况。
She, Jin-Hua; Ishii, Shota; Yokota, Sho; Sakuma, Yuji; Ohyama, Yasuhiro
A previously developed electric cart was improved by installing a knob that allows the driver to continuously vary the pedal load between the strenuous and assisted modes. This paper explains how the pedal load is determined and a design method for the cart control system. First, the largest pedal load is determined from the standpoint of ergonomics on the basis of the rating of perceived exertion and the Karvonen formula with a special focus on the motor function of the elderly. Then, a gain-scheduling cart control system for any pedal load in the allowed range is described, and a stability condition is derived using dynamic parallel distributed compensation. Experimental results demonstrate the validity of the cart control system.
Analysis of thermal characteristics of electrical wiring for load groups in cattle barns.
Kim, Doo Hyun; Yoo, Sang-Ok; Kim, Sung Chul; Hwang, Dong Kyu
2015-01-01
The purpose of the current study is to analyze the thermal characteristics of electrical wirings depending on the number of operating load by connecting four types of electrical wirings that are selected by surveying the conditions for the electric fans, automatic waterers and halogen warm lamps that were installed in cattle barns in different years. The conditions of 64 cattle barns were surveyed and an experimental test was conducted at a cattle barn. The condition-survey covered inappropriate design, construction and misuse of electrical facility, including electrical wiring mostly used, and the mode of load current was evaluated. The survey showed that the mode of load current increased as the installation year of the fans, waterers and halogen lamps became older. Accordingly, the cattle barn manager needed to increase the capacity of the circuit breaker, which promoted the degradation of insulation of the electrical wires' sheath and increased possibility for electrical fires in the long-run. The test showed that the saturation temperature of the wire insulated sheath increased depending on the installation year of the load groups, in case of VCTFK and VFF electric wires, therefore, requiring their careful usage in the cattle barns.
Letter to the Editor: Electric Vehicle Demand Model for Load Flow Studies
Garcia-Valle, Rodrigo; Vlachogiannis, Ioannis (John)
2009-01-01
This paper introduces specific and simple model for electric vehicles suitable for load flow studies. The electric vehicles demand system is modelled as PQ bus with stochastic characteristics based on the concept of queuing theory. All appropriate variables of stochastic PQ buses are given...
Modelling the Load Torques of Electric Drive for Polymerization Process
Andrzej Popenda
2007-01-01
Full Text Available The problems of mathematical modelling the load torques on shaft of driving motor designed for applications in polymerization reactors are presented in the paper. The real load of polymerization drive is determined as a function of angular velocity. Mentioned function results from friction in roll-formed slide bearing as well as from friction of ethylene molecules with mixer arms in polymerization reactor chamber. Application of mathematical formulas concerning the centrifugal ventilator is proposed to describe the mixer in reactor chamber. The analytical formulas describing the real loads of polymerization drive are applied in mathematical modelling the power unit of polymerization reactor with specially designed induction motor. The numerical analysis of transient states was made on the basis of formulated mathematical model. Examples of transient responses and trajectories resulting from analysis are presented in the paper.
Meisenbach, C. [Technische Univ. Dresden (Germany). Inst. fuer Elektroenergieversorgung
1999-07-01
Energy management systems make it possible to cover the electricity demand of local systems with optimal economic efficiency while giving due consideration to all technical side constraints. After a definition of the quality of supply demanded by the consumer the management system automatically adapts the available output capacity to the actual demand of the uninfluenceable consumers. This is achieved by a dual optimisation of the energy system, i.e. optimisation of storage and power plant operation and optimisation of load control, using evolutionary algorithms. These algorithms work on the basis of short-term and ultra-short-term forecasts of power demand and power output which have to be as precise as possible and which are generated by means of artificial neuronal networks. The present paper describes and assesses different ways of creating neuronal forecasting systems. It is possible to derive generally valid rules for the design of such systems. As a preparatory step to setting up generally valid design criteria for the forecasting and management system the author verifies the theoretical research results on the basis of a representative body of data from local energy systems of different size and geographic location. [German] Mit Hilfe eines Energiemanagements laesst sich der Strombedarf lokaler Systeme unter Einhaltung aller technischen Randbedingungen jederzeit oekonomisch optimal decken. Nach Festlegung der geforderten Versorgungsqualitaet der Verbraucher wird mit dem Management automatisch die zur Verfuegung stehende Leistung an den Leistungsbedarf der nicht beeinflussbaren Verbraucher angepasst. Dies wird durch eine beidseitige Optimierung des Energiesystems, d.h. einer Speicher- und Kraftwerkseinsatzoptimierung sowie einer Laststeuerung, realisiert. Fuer diese Aufgabe werden evolutionaere Algorithmen eingesetzt. Grundlage dafuer bilden moeglichst exakte Prognosen des Leistungsbedarfs und der erzeugten Leistung im kuerzest- und kurzfristigen Bereich
Hu, Weihao; Su, Chi; Chen, Zhe;
2011-01-01
Since the hourly spot market price is available one day ahead in Denmark, the price could be transferred to the consumers and they may shift some of their loads from high price periods to the low price periods in order to save their energy costs. The optimal load response to an electricity price...... price is proposed. A 17-bus power system with high wind power penetrations, which resembles the Eastern Danish power system, is chosen as the study case. Simulation results show that the optimal load response to electricity prices is an effective measure to improve the small signal stability of power...
Ivana Semanjski
2016-12-01
Full Text Available Car-sharing practices are introducing electric vehicles (EVs into their fleet. However, the literature suggests that at this point shared EV systems are failing to reach satisfactory commercial viability. A potential reason for this is the effect of higher vehicle usage, which is characteristic of car sharing, and the implications on the battery’s state of health (SoH. In this paper, we forecast the SoH of two identical EVs being used in different car-sharing practices. For this purpose, we use real life transaction data from charging stations and different EV sensors. The results indicate that insight into users’ driving and charging behavior can provide a valuable point of reference for car-sharing system designers. In particular, the forecasting results show that the moment when an EV battery reaches its theoretical end of life can differ in as much as a quarter of the time when vehicles are shared under different conditions.
Malmstroem, B.; Ernfors, P.; Nilsson, Daniel; Vallgren, H. [Chalmers Tekniska Hoegskola, Goeteborg (Sweden). Institutionen foer Energiteknik
1996-10-01
In this report the available methods for forecasting weather and district heating load have been studied. A forecast method based on neural networks has been tested against the more common statistical methods. The accuracy of the weather forecasts from the SMHI (Swedish Meteorological and Hydrological Institute) has been estimated. In connection with these tests, the possibilities of improving the forecasts by using on-line connected computers has been analysed. The most important results from the study are: Energy company staff generally look upon the forecasting of district heating load as a problem of such a magnitude that computer support is needed. At the companies where computer calculated forecasts are in use, their accuracy is regarded as quite satisfactory; The interest in computer produced load forecasts among energy company staff is increasing; At present, a sufficient number of commercial suppliers of weather forecasts as well as load forecasts is available to fulfill the needs of energy companies; Forecasts based on neural networks did not attain any precision improvement in comparison to more traditional statistical methods. There may though be other types of neural networks, not tested in this study, that are possibly capable of improving the forecast precision; Forecasts of outdoor temperature and district heating load can be significantly improved through the use of on-line-connected computers supplied with instantaneous measurements of temperature and load. This study shows that a general reduction of the load prediction errors by approximately 15% is attainable. For short time horizons (less than 5 hours), more extensive load prediction error reductions can be reached. For the 1-hour time horizon, the possible reduction amounts to up to 50%. 21 refs, 4 figs, 7 appendices
Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads
Ergemen, Yunus Emre; Haldrup, Niels; Rodríguez-Caballero, Carlos Vladimir
of the underlying production technology and because the demand is more volatile than the supply, equilibrium prices and loads are argued to identify the periodic power supply curve. The estimated supply elasticities are estimated from fractionally co-integrated relations and range between 0.5 and 1......Equilibrium electricity spot prices and loads are often determined simultaneously in a day-ahead auction market for each hour of the subsequent day. Hence daily observations of hourly prices take the form of a periodic panel rather than a time series of hourly observations. We consider novel panel...... data approaches to analyse the time series and the cross-sectional dependence of hourly Nord Pool electricity spot prices and loads for the period 2000-2013. Hourly electricity prices and loads data are characterized by strong serial long-range dependence in the time series dimension in addition...
Development of mathematical models for forecasting hydraulic loads of water and wastewater networks
Studzinki, Jan [Polish Academy of Sciences, Warsaw (Poland). Systems Research Institute; Bartkiewicz, Lidia [Technical Univ. Kielce (Poland); Stachura, Marcin [Warsaw University of Technology (Poland)
2013-07-01
In municipal waterworks the operation of water and wastewater networks decides about the functioning of the sewage treatment plant that is the last element of the whole water and sewage system. The both networks are connected each other and the work of the water net affects the operation of the wastewater one. The parameters which are important for right leading of all waterworks objects are their hydraulic loads that have to be not exceeded. Too large loads can cause accidents in the wastewater net or the treatment plant and an early knowledge of them is of importance for undertaking some counteractions. In the paper different algorithms to model hydraulic loads of municipal water and wastewater nets are described and compared regarding their computation velocity and accuracy. Some exemplary computations have been done with some real data received from a Polish water company. (orig.)
Electricity's "Disappearing Act": Understanding Energy Consumption and Phantom Loads
Rusk, Bryan; Mahfouz, Tarek; Jones, James
2011-01-01
Energy exists in many forms and can be converted from one form to another. However, this conversion is not 100% efficient, and energy is lost in the form of heat during conversion. In addition, approximately 6% of the monthly consumption of the average American household's electricity is neither lost nor used by its residents. These losses are…
Electricity's "Disappearing Act": Understanding Energy Consumption and Phantom Loads
Rusk, Bryan; Mahfouz, Tarek; Jones, James
2011-01-01
Energy exists in many forms and can be converted from one form to another. However, this conversion is not 100% efficient, and energy is lost in the form of heat during conversion. In addition, approximately 6% of the monthly consumption of the average American household's electricity is neither lost nor used by its residents. These losses are…
Antonio Bello
2016-03-01
Full Text Available One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.
Jiazheng Lu
2016-01-01
Full Text Available Recently, there is a rise in frequency of fires which pose a serious threat to the safety operation of electric transmission lines. Several ultrahigh voltage (UHV electric transmission lines, including Fufeng line, Jinsu line, Longzheng line, and Changnan line, showed many times tripping or bipolar latching caused by fire disasters. Fire disasters have tended to be the biggest threat to the safety operation of electric transmission lines and even can cause power grid collapse in some severe situations. Researchers have made much research on fires forecasting. However, these studies are mainly concentrated on predicting fires based on measured or forecasting meteorological data and do not take into account the effect of human activities. In fact, fire disasters have a very close relationship with human activities. In our research, a fire prediction model is proposed incorporating meteorological data as well as human activities. And this model is applied in Hunan province and Anhui province, which seriously suffer from fire disasters. The results show that the model has good prediction precision and can be a powerful tool for practical application.
A transfer function model for load forecasting based on load decomposition%基于负荷分解的短期负荷预测传递函数模型
刘敏; 万志宏; 文福拴
2012-01-01
短期负荷预测主要用于预测未来几小时、1天甚至几天的负荷,对电力系统运行的安全性和经济性具有重要意义.时间序列模型在电力系统短期负荷预测中得到了广泛应用.然而,这种方法的一个主要缺点是无法将影响负荷预测的主要因素之一即气象因素考虑进去.在此背景下,首先基于历史负荷数据,采用传统的分解方法提取出负荷中的周期分量,得到剔除周期分量后的非周期分量.在此基础上,首先采用逐步回归法筛选出影响负荷非周期分量的主要因素,之后发展了预测负荷非周期分量的传递函数模型.最后,用广东电力系统实际负荷数据对所发展的短期负荷预测模型的准确性进行了验证.%Short-term load forecasting is used to predict the loads in the coming hours, in the next day and even the next several days, and has important impacts on maintaining the security and economics of the power system. The time series forecasting model represents a classical prediction method, and has been widely used for short-term load forecasting in actual power systems around the world. However, one major disadvantage of the time series model lies in the fact that the weather factor cannot be taken into account, while it usually play an important role in short-term load forecasting. Given this background, the periodical component is first decomposed from the overall load data by using the traditional decomposition method based on historical load data. On this basis, some major factors are selected from those factors having impacts on the non-periodic component of loads by using the stepwise regression model. A transfer function model is next developed for forecasting the non-periodic component of loads. Actual load data from Guangdong power system are employed to demonstrate the developed short-term load forecasting model.
Forecasting the daily electricity consumption in the Moscow region using artificial neural networks
Ivanov, V. V.; Kryanev, A. V.; Osetrov, E. S.
2017-07-01
In [1] we demonstrated the possibility in principle for short-term forecasting of daily volumes of passenger traffic in the Moscow metro with the help of artificial neural networks. During training and predicting, a set of the factors that affect the daily passenger traffic in the subway is passed to the input of the neural network. One of these factors is the daily power consumption in the Moscow region. Therefore, to predict the volume of the passenger traffic in the subway, we must first to solve the problem of forecasting the daily energy consumption in the Moscow region.
Study on Impact of Electric Vehicles Charging Models on Power Load
Cheng, Chen; Hui-mei, Yuan
2017-05-01
With the rapid increase in the number of electric vehicles, which will lead the power load on grid increased and have an adversely affect. This paper gives a detailed analysis of the following factors, such as scale of the electric cars, charging mode, initial charging time, initial state of charge, charging power and other factors. Monte Carlo simulation method is used to compare the two charging modes, which are conventional charging and fast charging, and MATLAB is used to model and simulate the electric vehicle charging load. The results show that compared with the conventional charging mode, fast charging mode can meet the requirements of fast charging, but also bring great load to the distribution network which will affect the reliability of power grid.
V. V. Kravchenko
2008-01-01
Full Text Available On the basis of systematic analysis of scientific, statistical and economic data the paper compares modern situation and forecasts of electrical power engineering development in the Republic of Belarus and Russian Federation. The paper carries out an analysis of integrated structure of fuel balances of both countries till 2015. The paper notes the fact that thermal power stations (TPS will remain a main generating source till 2020 and gas will continue to be the main type of fuel in the structure of fuel balances. The paper investigates development of technological structures in the electrical power engineering. It has been revealed that one of the main factors that hinders development of the Belarussian power system is the absence of the required financial mechanisms for obtaining additional investment possibilities. In connection with this fact a special attention should be given to the problems that are directed on improvement of tariff policy and mechanisms of tariff formation.
Load area aggregation considering integration of electric vehicles to the system
L. F. Rodríguez Garcia
2015-11-01
Full Text Available Current electric power systems have an increasing penetration of electric vehicles, and its effect has to be considered in different studies, such as optimal dispatch or voltage stability, among others. Additionally, considering that power system analysis becomes complex when the number of buses increase, this paper presents a methodology for aggregation of load areas that use a measurement-based load modeling approach based on an evolutionary computational technique and a classical reduction method. This aggregate load area model is proposed to reduce areas that ddconsider electric vehicle (EV load models. The proposed method provides a static equivalent load model and an equivalent network that can be used to reduce the computational effort required by power system studies. In order to validate the application of the proposed methodology, a 30-bus power system considering several disturbances and levels of penetration of the electric vehicles was used. The results show that the equivalent network model allows the reproduction of different events with an acceptable accuracy when it is compared to the original system behavior.
On the road performance tests of electric test vehicle for correlation with road load simulator
Dustin, M. O.; Slavik, R. J.
1982-08-01
A dynamometer (road load simulator) is used to test and evaluate electric vehicle propulsion systems. To improve correlation between system tests on the road load simulator and on the road, similar performance tests are conducted using the same vehicle. The results of track tests on the electric propulsion system test vehicle are described. The tests include range at constant speeds and over SAE J227a driving cycles, maximum accelerations, maximum gradability, and tire rolling resistance determination. Road power requirements and energy consumption were also determined from coast down tests.
A SIMPLE CONSTITUTIVE MODEL FOR FERROELECTRIC CERAMICS UNDER ELECTRICAL/MECHANICAL LOADING
Yu Li; Yu Shouwen; Feng Xiqiao
2007-01-01
A simple phenomenological model is developed for describing the macroscopic constitutive response of ferroelectric materials based on consideration of the fact that domain switching is a progressive evolution process with loading. The volume fraction of domain switching is taken as an internal variable, which is derived from the domain nucleation theory. The proposed theory can simulate the dielectric hysteresis, reversed butterfly hysteresis, nonlinear strain-stress hysteresis, as well as electric displacement-stress relation of ferroelectric materials. Its comparison with experimental results and two other theoretical models reveals that the model presented can well predict the nonlinear hysteresis of ferroelectrics under electrical or mechanical loading.
Electrical Response of Cement-Based Piezoelectric Ceramic Composites under Mechanical Loadings
Biqin Dong
2011-01-01
Full Text Available Electrical responses of cement-based piezoelectric ceramic composites under mechanical loadings are studied. A simple high order model is presented to explain the nonlinear phenomena, which is found in the electrical response of the composites under large mechanical loadings. For general situation, this nonlinear piezoelectric effect is quite small, and the composite is suitable for dynamic mechanical sensor as holding high static stability. The experimental results are consistent with the relationship quite well. The study shows that cement-based piezoelectric composite is suitable for potential application as dynamic mechanical sensor with excellent dynamic response and high static stability.
Climate Control Load Reduction Strategies for Electric Drive Vehicles in Warm Weather
Jeffers, M. A.; Chaney, L.; Rugh, J. P.
2015-04-30
Passenger compartment climate control is one of the largest auxiliary loads on a vehicle. Like conventional vehicles, electric vehicles (EVs) require climate control to maintain occupant comfort and safety, but cabin heating and air conditioning have a negative impact on driving range for all electric vehicles. Range reduction caused by climate control and other factors is a barrier to widespread adoption of EVs. Reducing the thermal loads on the climate control system will extend driving range, thereby reducing consumer range anxiety and increasing the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have investigated strategies for vehicle climate control load reduction, with special attention toward EVs. Outdoor vehicle thermal testing was conducted on two 2012 Ford Focus Electric vehicles to evaluate thermal management strategies for warm weather, including solar load reduction and cabin pre-ventilation. An advanced thermal test manikin was used to assess a zonal approach to climate control. In addition, vehicle thermal analysis was used to support testing by exploring thermal load reduction strategies, evaluating occupant thermal comfort, and calculating EV range impacts. Through stationary cooling tests and vehicle simulations, a zonal cooling configuration demonstrated range improvement of 6%-15%, depending on the drive cycle. A combined cooling configuration that incorporated thermal load reduction and zonal cooling strategies showed up to 33% improvement in EV range.
Gabioud, D.
2008-07-01
This illustrated article takes up the problems related to the variation of the load in electricity networks. How to handle the peak load? Different solutions in the energy demand management are discussed. Method based on the price, method based on the reduction of the load by electric utilities. Information systems are presented which gives the consumer the needed data to participate in the local load management.
Venkatesan, Arjun K; Ahmad, Sajjad; Johnson, Walter; Batista, Jacimaria R
2011-06-01
This study evaluates the impact of urban growth in the Las Vegas Valley (LVV), Nevada, USA on salinity of the Colorado River. In the past thirty eight years the LVV population has grown from 273,288 (1970) to 1,986,146 (2008). The wastewater effluents and runoff from the valley are diverted back to the Colorado River through the Las Vegas Wash (LVW). With the growth of the valley, the salinity released from urban areas has increased the level of TDS in the wastewater effluents, ultimately increasing the TDS in the Colorado River. The increased usage of water softeners in residential and commercial locations is a major contributor of TDS in the wastewater effluents. Controlling TDS release to the Colorado River is important because of the 1944 Treaty signed between the USA and Mexico. In addition, the agriculture salinity damage cost for the Colorado River has been estimated to be more than $306 a million per year using 2004 salinity levels. With the expected growth of LVV in coming years the TDS release into Lake Mead will increase over time. For this purpose, it is important to investigate future TDS release into the Colorado in anticipation of potential TDS reducing measures to be adopted. In this research, a dynamic simulation model was developed using system dynamics modeling to carry out water and TDS mass balances over the entire LVV. The dynamic model output agreed with historic data with an average error of 2%. Forecasts revealed that conservation efforts can reduce TDS load by 16% in the year 2035 when compared to the current trend. If total population using water softeners can be limited to 10% in the year 2035, from the current 30% usage, TDS load in the LVW can be reduced by 7%. Copyright © 2011 Elsevier B.V. All rights reserved.
Management of electricity peak load for residential sector in Baghdad city by using solar generation
Afaneen A. Abbood, Mohammed A. Salih, Hasan N. Muslim
2017-01-01
Full Text Available Load management strategies such as peak reduction, load shifting and energy conservation are effective solution to save and optimally usage of electricity. Solar cells - photovoltaic systems (solar PV are one of the modern methods used in the management of peak loads in the electric power system because PV generation coincides with peak load hours in the day. The aim of this work is implementation of management techniques using solar cells for residential sector in Baghdad city. The estimation of solar radiation data and PV system design has been simulated based on MATLAB software. In this study, a 20% efficiency monocrystalline silicon rooftop PV generator of 2kWp with six panels and overall area 10m² has been proposed for each customer in the residential sector of Baghdad. The panels are orientated towards south (azimuth angle equals zero with a tilt angle equals 18° for summery months and 48° for wintery months. The obtained results of demand saving range between 17% for January and 27% for April while 20% for June. The annually demand saving for each consumer is 20%. As well as to the demand saving, this study presents the capability of application the load-shifting technique from high load periods to low load periods, and ability to store the surplus energy produced from PV generator in batteries for usage this energy at a later time.
Albalawi, Ashwaq Nasser
I present results of a search for, and measurement of, gamma ray bursts systems using data collected by the Robotic Optical Transient Search Experiment (ROTSE) III telescope. I performed an analysis with the ROTSE IIIb data by running a relative photometry program RPHOT in IDL. Then, I found that Gamma Ray Burst (GRB) GRB130215A is connected with Super Novae (SN) SN 2013 ez.
Du, Liang; Yang, Yi; Harley, Ronald Gordon; Habetler, Thomas G.; He, Dawei
2016-08-09
A system is for a plurality of different electric load types. The system includes a plurality of sensors structured to sense a voltage signal and a current signal for each of the different electric loads; and a processor. The processor acquires a voltage and current waveform from the sensors for a corresponding one of the different electric load types; calculates a power or current RMS profile of the waveform; quantizes the power or current RMS profile into a set of quantized state-values; evaluates a state-duration for each of the quantized state-values; evaluates a plurality of state-types based on the power or current RMS profile and the quantized state-values; generates a state-sequence that describes a corresponding finite state machine model of a generalized load start-up or transient profile for the corresponding electric load type; and identifies the corresponding electric load type.
Mengnan Liu
2016-01-01
Full Text Available In order to improve the electrical conversion efficiency of an electric tractor motor, a load torque based control strategy (LTCS is designed in this paper by using a particle swarm optimization algorithm (PSO. By mathematically modeling electric-mechanical performance and theoretical energy waste of the electric motor, as well as the transmission characteristics of the drivetrain, the objective function, control relationship, and analytical platform are established. Torque and rotation speed of the motor’s output shaft are defined as manipulated variables. LTCS searches the working points corresponding to the best energy conversion efficiency via PSO to control the running status of the electric motor and uses logic and fuzzy rules to fit the search initialization for load torque fluctuation. After using different plowing forces to imitate all the common tillage forces, the simulation of traction experiment is conducted, which proves that LTCS can make the tractor use electrical power efficiently and maintain agricultural applicability on farmland conditions. It provides a novel method of fabricating a more efficient electric motor used in the traction of an off-road vehicle.
A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting
Francisco Martínez-Álvarez; Alicia Troncoso; Gualberto Asencio-Cortés; Riquelme, José C
2015-01-01
Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of ...
Goldman, Charles A.; Barbose Galen L.; Eto, Joseph H.
2002-05-01
During summer 2001, Californians reduced electricity usage by 6 percent and average monthly peak demand by 8 percent, compared to summer 2000. These load reductions played an important role in avoiding the hundreds of hours of rotating power outages predicted several months prior. Many factors affected electricity use and peak demand in summer 2001, including weather, changes in the State's economy, and deliberate consumer responses to a variety of stimuli associated with the crisis. This paper assesses the roles played by these contributing factors, with a special focus on the extraordinary efforts made by Californians to reduce electricity consumption. We review the role of media coverage and informational campaigns on public awareness and the impact of rate increases and a variety of publicly funded programs in reducing electricity consumption. We also draw lessons for other regions that may be faced with the prospect of electricity shortages.
Linlin Tan
2016-10-01
Full Text Available An opportunity wireless charging system for electric vehicles when they stop and wait at traffic lights is proposed in this paper. In order to solve the serious power fluctuation caused by random access loads, this study presents a power stabilization strategy based on counting the number of electric vehicles in a designated area, including counting method, power source voltage adjustment strategy and choice of counting points. Firstly, the circuit model of a wireless power system with multi-loads is built and the equation of each load is obtained. Secondly, after the counting method of electric vehicles is stated, the voltage adjustment strategy, based on the number of electric vehicles when the system is at a steady state, is set out. Then, the counting points are chosen according to power curves when the voltage adjustment strategy is adopted. Finally, an experimental prototype is implemented to verify the power stabilization strategy. The experimental results show that, with the application of this strategy, the charging power is stabilized with the fluctuation of no more than 5% when loads access randomly.
Kim, Oleksiy S.
2016-01-01
A new technique for estimating the impedance frequency bandwidth of electrically small antennas loaded with magneto-dielectric material from a single-frequency simulation in a surface integral equation solver is presented. The estimate is based on the inverse of the radiation Q computed using newly...
Saltas, V.; Fitilis, I.; Vallianatos, F.
2014-12-01
In the present work, complex electrical impedance measurements in the frequency range of 10 mHz to 1 MHz were carried out in conjunction with acoustic emission monitoring in limestone samples subjected to linear and stepped-like uniaxial loading, up to ultimate failure. Cole-Cole plots of the complex impedance during the stepped loading of limestone have been used to discriminate the contributions of grains interior, grain boundaries and electrode polarization effects to the overall electrical behavior. The latter is well-described with an equivalent-circuit model which comprises components of constant phase elements and resistances in parallel connection. Electrical conductivity increases upon uniaxial loading giving rise to negative values of effective activation volume. This is a strong experimental evidence for the generation of transient electric signals recorded prior to seismic events and may be attributed to charge transfer (proton conduction) due to cracks generation and propagation as a result of the applied stress. The time-series of ac-conductivity at two distinct frequencies (10 kHz, 200 kHz) during linear loading of limestone samples exhibits a strong correlation with the acoustic emission activity obeying the same general self-similar law for critical phenomena that has been reported for the energy release before materials fracture.
Lo, K.L.; Wu, Y.K. [University of Strathclyde, Glasgow (United Kingdom). Power Systems Research Group
2004-07-01
Risk management in the electric power industry involves measuring the risk for all instruments owned by a company. The value of many of these instruments depends directly on electricity prices. In theory, the wholesale price in a real-time market should reflect the short-run marginal cost. However, most markets are not perfectly competitive, therefore by understanding the degree of correlation between price and physical drivers, electric traders and consumers can manage their risk more effectively and efficiently. Market data from two power-pool architectures, both pre-2003 ISO-NE and Australia's NEM, have been studied. The dynamic character of electricity price is mean-reverting, and consists of intra-day and weekly variations, seasonal fluctuations, and instant jumps. Parts of them are affected by load demands. Hourly signals on both price and load are divided into deterministic and random components with a discrete Fourier transform algorithm. Next, the real-time price-load relationship for periodic and random signals is examined. In addition, time-varying volatility models are constructed on random price and random load with the GARCH model, and the correlation between them analysed. Volatility plays a critical role on evaluating option pricing and risk management. (author)
Effect of blade flutter and electrical loading on small wind turbine noise
The effect of blade flutter and electrical loading on the noise level of two different size wind turbines was investigated at the Conservation and Production Research Laboratory (CPRL) near Bushland, TX. Noise and performance data were collected on two blade designs tested on a wind turbine rated a...
Vlachogiannis, Ioannis (John)
2009-01-01
A new formulation and solution of probabilistic constrained load flow (PCLF) problem suitable for modern power systems with wind power generation and electric vehicles (EV) demand or supply is represented. The developed stochastic model of EV demand/supply and the wind power generation model...
Fluctuation analysis of high frequency electric power load in the Czech Republic
Kracík, Jiří
2016-01-01
We analyze the electric power load in the Czech Republic (CR) which exhibits a seasonality as well as other oscillations typical for European countries. Moreover, we detect 1/f noise property of electrical power load with extra additional peaks that allows to separate it into a deterministic and stochastic part. We then focus on the analysis of the stochastic part using improved Multi-fractal Detrended Fluctuation Analysis method (MFDFA) to investigate power load datasets with a minute resolution. Extracting the noise part of the signal by using Fourier transform allows us to apply this method to obtain the fluctuation function and to estimate the generalized Hurst exponent together with the correlated Hurst exponent, its improvement for the non-Gaussian datasets. The results exhibit a strong presence of persistent behaviour and the dataset is characterized by a non-Gaussian skewed distribution. There are also indications for the presence of the probability distribution that has heavier tail than the Gaussian...
Technical development of electric power storage system. Development of load conditioner
Tanaka, Toshikatsu
1988-07-01
The load conditioner heightens the utilization ratio of facilities by the stardardization of load for the suppliers and has the consumers advantageously utilize cheap electric power. The domestic facilities are basically composed of charging rectifier, discharging self-exciting inverter and secondary battery. As there presently exists lead battery as a secondary battery, the Electric Power Research Center developed a load conditioner system by using lead battery and is making its operational study. As a secondary lithium battery satisfying the compactness of further particular importance, there are high molecule battery using high molecule compounds such as polyaniline and polyacene, intercaration type battery using vanadium oxide or molybdenum disulfide, etc. Then, in order to materialize the enlargement and life lengthening of those batteries, the steady study of all of negative electrode, positive electrode, electrolyte, battery structure. (3 figs, 2 tabs)
基于贝叶斯定理的支持向量机短期负荷预测%Short-term load forecasting based on Bayes'theorem Support vector machine
王欣; 刘俊杰
2014-01-01
电力负荷受天气原因、突发事件、随机变化等因素影响较大，诸如在某些时期由于某些工厂的投停产、大型设备的超载等将影响负荷的变化，具有非常复杂的非线性关系。对负荷预测中存在的不确定性和非线性关系，提出基于贝叶斯支持向量机回归预测方法。建立基于先验概率分布的正太分布的贝叶斯支持向量机模型，描述不确定性的影响因素信息，采用求极大似然估计和迭代算法求解模型最佳参数，将每一步迭代求解后得到的关联向量机作为随机变量输入，最终通过建立的时间序列预测模型获得负荷的预测结果。通过实例进行仿真预测，验证了基于贝叶斯准则的支持向量机预测效果更加符合短期负荷预测的要求，与实际负荷趋势十分接近，克服了不确定性信息对负荷预测的影响，具有良好的预测精度。%Electric load was affected by weather, emergencies, and other factors of the larger ran-dom variations, such as some factories shut down vote in certain periods, overloading and other large equipment will affect the load changes, it has a very complex nonlinear relationship. For the uncertainty and nonlinear relationship exists in load forecasting, We proposed Bayesian regression forecasting meth-od based on support vector machine. Based on prior probability distribution of fall of the bayesian model of support vector machine (SVM), describes the influence factors of uncertainty information, using maxi-mum likelihood estimation and optimal parameters, iterative algorithm to solve the model will be every step of the iteration after relevance vector machine (SVM) as a random variable input, ultimately through the establishment of time series prediction model for load forecasting results. It was verified by example simulation prediction, based on the bayesian criterion of support vector machine (SVM) to predict the ef-fect is more accord with
Modelling Load Shifing Using Electric Vehicles in a Smart Grid Environment
NONE
2010-07-01
Electric vehicles (EVs) represent both a new demand for electricity and a possible storage medium that could supply power to utilities. The 'load shifting' and 'vehicle-to-grid' concepts could help cut electricity demand during peak periods and prove especially helpful in smoothing variations in power generation introduced to the grid by variable renewable resources such as wind and solar power. This paper proposes a method for simulating the potential benefits of using EVs in load shifting and 'vehicle-to-grid' applications for four different regions -- the United States, Western Europe, China and Japan -- that are expected to have large numbers of EVs by 2050.
Yang Shuai
2016-01-01
Full Text Available Firstly, using the Monte Carlo method and simulation analysis, this paper builds models for the behaviour of electric vehicles, the conventional charging model and the fast charging model. Secondly, this paper studies the impact that the number of electric vehicles which get access to power grid has on the daily load curve. Then, the paper put forwards a dynamic pricing mechanism of electricity, and studies how this dynamic pricing mechanism guides the electric vehicles to charge orderly. Last but not the least, the paper presents a V2G mechanism. Under this mechanism, electric vehicles can charge orderly and take part in the peak shaving. Research finds that massive electric vehicles’ access to the power grid will increase the peak-valley difference of daily load curve. Dynamic pricing mechanism and V2G mechanism can effectively lead the electric vehicles to take part in peak-shaving, and optimize the daily load curve.
A. M. Afanasov
2014-05-01
Full Text Available Purpose. Loading-back systems are used for post-maintenance acceptance tests of electric traction motors of electric rolling stock (ERS in mainline and industrial transport. The aim of the research is justification of choice method of the rational loading-back parameters of traction electric motors during the tests for heating. Methodology. At the heart of the choice method justification of the rational loading-back parameters is a theory of heating the homogeneous solid body and well-known methods of heating calculations of traction electric machines. Findings. Expediency of heating tests for electric traction motors at load currents equal to the starting current (for ERS of mainline transport or fifteen minutes current (for ERS of industrial transport was substantiated. It was shown that this increase of load current will reduce the electricity costs of the tests for 20–30% and shorten the tests duration for three–four times while ensuring the corrected power minimum of energy sources in loading-back system. Originality. It is shown that the energy costs for heating tests of the traction motors can be reduced by both the increasing the energy efficiency of the loading-back system and by the optimizing of loading mode of traction electric machines. Choice technique of rational modes of loading-back for electric traction motors of rolling stock in mainline and industrial transport was grounded. Practical value. Load current increase of traction electric motors during their tests for heating will reduce the total electricity consumption for the acceptance tests, reduce test time and total material costs for repair of the electric traction motors of rolling stock in mainline and industrial transport.
刘涵; 刘丁; 郑岗; 梁炎明
2004-01-01
Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost.Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice.
NONE
2006-07-01
The development of the future prices for electric power calls for a continuous analysis of the market. The quantities to be sold are purchased directly on the wholesale-trading market or via models indicating the development of the wholesale trading prices. A professional and risk-adjusted control of the portfolio is the central module in order to get the right position in the increasing competition. Within the options trading, forecasting the electric power prices is of central importance.
Application of high-resolution domestic electricity load profiles in network modelling
Marszal, Anna Joanna; Mendaza, Iker Diaz de Cerio; Heiselberg, Per Kvols
2016-01-01
% of buildings, the loading of the transformer and power lines is reduced in the summer time to 58% and 51%, respectively. However, the power lines are stress with bi-directional power flow. The results indicate that the business-as-usual approach to network modeling is not sufficient to capture......The ongoing development towards electrification of the energy consumption together with large deployment of renewable energy sources creates new challenges of variability and fluctuation of the electricity supply and increases complexity of the network operation. In order to capture all...... the particularities of electricity demand and on-site generation, e.g. the short-term spikes due use of high electricity consumption appliances such like electric kettle, and get a full picture of network performance, a high-resolution input data are needed. This paper compares the business-as-usual network modeling...
Factor Analysis of the Aggregated Electric Vehicle Load Based on Data Mining
Yao Wang
2012-06-01
Full Text Available Electric vehicles (EVs and the related infrastructure are being developed rapidly. In order to evaluate the impact of factors on the aggregated EV load and to coordinate charging, a model is established to capture the relationship between the charging load and important factors based on data mining. The factors can be categorized as internal and external. The internal factors include the EV battery size, charging rate at different places, penetration of the charging infrastructure, and charging habits. The external factor is the time-of-use pricing (TOU policy. As a massive input data is necessary for data mining, an algorithm is implemented to generate a massive sample as input data which considers real-world travel patterns based on a historical travel dataset. With the input data, linear regression was used to build a linear model whose inputs were the internal factors. The impact of the internal factors on the EV load can be quantified by analyzing the sign, value, and temporal distribution of the model coefficients. The results showed that when no TOU policy is implemented, the rate of charging at home and range anxiety exerts the greatest influence on EV load. For the external factor, a support vector regression technique was used to build a relationship between the TOU policy and EV load. Then, an optimization model based on the relationship was proposed to devise a TOU policy that levels the load. The results suggest that implementing a TOU policy reduces the difference between the peak and valley loads remarkably.
Climate Control Load Reduction Strategies for Electric Drive Vehicles in Cold Weather
Jeffers, Matthew A.; Chaney, Larry; Rugh, John P.
2016-04-05
When operated, the climate control system is the largest auxiliary load on a vehicle. This load has significant impact on fuel economy for conventional and hybrid vehicles, and it drastically reduces the driving range of all electric vehicles (EVs). Heating is even more detrimental to EV range than cooling because no engine waste heat is available. Reducing the thermal loads on the heating, ventilating, and air conditioning system will extend driving range and increase the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have evaluated strategies for vehicle climate control load reduction with special attention toward grid connected electric vehicles. Outdoor vehicle thermal testing and computational modeling were used to assess potential strategies for improved thermal management and to evaluate the effectiveness of thermal load reduction technologies. A human physiology model was also used to evaluate the impact on occupant thermal comfort. Experimental evaluations of zonal heating strategies demonstrated a 5.5% to 28.5% reduction in cabin heating energy over a 20-minute warm-up. Vehicle simulations over various drive cycles show a 6.9% to 18.7% improvement in EV range over baseline heating using the most promising zonal heating strategy investigated. A national-level analysis was conducted to determine the overall national impact. If all vehicles used the best zonal strategy, the range would be improved by 7.1% over the baseline heating range. This is a 33% reduction in the range penalty for heating.
Climate Control Load Reduction Strategies for Electric Drive Vehicles in Cold Weather: Preprint
Jeffers, Matthew; Chaney, Lawrence; Rugh, John
2016-03-31
When operated, the climate control system is the largest auxiliary load on a vehicle. This load has significant impact on fuel economy for conventional and hybrid vehicles, and it drastically reduces the driving range of all electric vehicles (EVs). Heating is even more detrimental to EV range than cooling because no engine waste heat is available. Reducing the thermal loads on the heating, ventilating, and air conditioning system will extend driving range and increase the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have evaluated strategies for vehicle climate control load reduction with special attention toward grid connected electric vehicles. Outdoor vehicle thermal testing and computational modeling were used to assess potential strategies for improved thermal management and to evaluate the effectiveness of thermal load reduction technologies. A human physiology model was also used to evaluate the impact on occupant thermal comfort. Experimental evaluations of zonal heating strategies demonstrated a 5.5% to 28.5% reduction in cabin heating energy over a 20-minute warm-up. Vehicle simulations over various drive cycles show a 6.9% to 18.7% improvement in EV range over baseline heating using the most promising zonal heating strategy investigated. A national-level analysis was conducted to determine the overall national impact. If all vehicles used the best zonal strategy, the range would be improved by 7.1% over the baseline heating range. This is a 33% reduction in the range penalty for heating.
Developing long-term scenario forecasts to support electricity generation investment decisions
Koen, Renée
2014-09-01
Full Text Available Many decisions regarding capital investment in electricity generation technologies need to be made well in advance, usually when there is still a large amount of uncertainty regarding the favourability of future conditions. There may be uncertainty...
Forecasting electricity demand based on data mining%基于数据挖掘的电力需求预测探究
李其军
2015-01-01
电力需求的预测是电力服务企业制定供电、购电的重要依据，因此，做好对电力需求的预测，对提高电力企业的经济运行能力具有重要的作用。本文结合原始预测系统中存在的问题，提出采用数据挖掘技术对原始数据的采集、预处理等，从而实现对电力需求预测的客观性和准确性，更好的服务与电力企业和社会。%Electricity demand forecasting electricity supply service companies develop,purchase an important basis for electricity and, therefore, do a good job of forecasting electricity demand,the ability to improve the economic operation of power enterprises play an important role.In this paper,the original prediction system problems using data mining techniques proposed acquisition of the raw data,pre-processing , etc.,in order to achieve objectivity and accuracy of forecasts of electricity demand,better service and electricity business and society .
Sayed Mahdi Mostafavi
2016-07-01
Full Text Available Electrical energy is as one of the important effective factors on economic growth and development. In recent decades, numerous studies in different countries to estimate and forecast electricity demand in different parts of the economy have been made. In this paper, using the method ARDL, estimation and forecasting of electricity demand in the services sector of Iran are determined for the time period from 1983 to 2012. Estimated equations show that the added value of the services sector and a significant positive impact on the demand for electricity in this sector. The price elasticity for services sector is smaller than 1 due to low electricity prices and subsidized electricity. Hence, electricity prices have little impact on the demand for electricity. The results of the estimate represents a long-term relationship between the variables in the services sector. In this paper, based on amendments to the law on subsidies and estimated values, anticipated electricity demand until the end of the fifth development plan was carried out. The results indicate an increase in power consumption in the services sector.
Shahab, S.; Gray, M.; Erturk, A.
2015-04-01
This paper investigates analytical modeling and experimental validation of Ultrasonic Acoustic Energy Transfer (UAET) for low-power electricity transfer to exploit in wireless applications ranging from medical implants to underwater sensor systems. A piezoelectric receiver bar is excited by incident acoustic waves originating from a source of known strength located at a specific distance from the receiver. The receiver is a free-free piezoelectric cylinder operating in the 33- mode of piezoelectricity with a fundamental resonance frequency above the audible frequency range. In order to extract the electrical power output, the piezoelectric receiver bar is shunted to a generalized resistive-reactive circuit. The goal is to quantify the electrical power delivered to the load (connected to the receiver) in terms of the source strength. Experimental validations are presented along with parameter optimization studies. Sensitivity of the electrical power output to the excitation frequency in the neighborhood of the receiver's underwater resonance frequency, source-to-receiver distance, and source-strength level are reported. Resistive and resistive-reactive electrical loading cases are discussed for performance enhancement and frequency-wise robustness. Simulations and experiments reveal that the presented multiphysics analytical model for UAET can be used to predict the coupled system dynamics with very good accuracy.
Static deflection analysis of non prismatic multilayer p-NEMS cantilevers under electrical load
Pavithra, M., E-mail: pavithramasi78@gmail.com [PhD Research Scholar, Department of Electronics and Instrumentation, Bharathiar University, Coimbatore-46 (India); Muruganand, S. [Assistant professor, Department of Electronics and Instrumentation, Bharathiar University, Coimbatore-46 (India)
2016-04-13
Deflection of Euler-Bernoulli non prismatic multilayer piezoelectric nano electromechanical (p-NEMS) cantilever beams have been studied theoretically for various profiles of p-NEMS cantilevers by applying the electrical load. This problem has been answered by applying the boundary conditions derived by simple polynomials. This method is applied for various profiles like rectangular and trapezoidal by varying the thickness of the piezoelectric layer as well as the material. The obtained results provide the better deflection for trapezoidal profile with ZnO piezo electric layer of suitable nano cantilevers for nano scale applications.
YANG Duo-he; AN Wei-guang; ZHU Rong-rong; MIAO Han
2006-01-01
Based on the finite element method(FEM) for the dynamical analysis of piezoelectric truss structures, the expressions of safety margins of strength fracture and damage electric field in the structure element are given considering electromechanical coupling effect under the joint action of electric and mechanical load. By importing the stochastic FEM,reliability of piezoelectric truss structures is analyzed by solving for partial derivative in the process of solving dynamical response of structure system with mode-superposition method. The influence of electromechanical coupling effect to reliability index is then analyzed through an example.
Research on Propeller Dynamic Load Simulation System of Electric Propulsion Ship
HUANG Hui; SHEN Ai-di; CHU Jian-xin
2013-01-01
A dynamic marine propeller simulation system was developed,which is utilized for meeting the experimental requirement of theory research and engineering design of marine electric propulsion system.By applying an actual ship parameter and its accurate propeller J'～KT' and J'～Kp' curve data,functional experiments based on the simulation system were carried out.The experiment results showed that the system can correctly emulate the propeller characteristics,produce the dynamic and steady performances of the propeller under different navigation modes,and present actual load torque for electric propulsion motor.
Zhou, B H; Katz, S R; Baratta, R V; Solomonow, M; D'Ambrosia, R D
1997-07-01
Muscle coactivation strategies that produce ankle dorsiflexion and plantar flexion were elicited by electrical stimulation of the tibialis anterior (TA) and soleus (SOL) muscles of the cat, and examined under several loading conditions. Four different load types were used: free-limb motion (no load), fly-wheel, and two pendulums, each with a different lever arm. Three types of coactivation strategies were considered. The first coactivation strategy consisted of antagonist activity that decreased as the agonist activity increased. The second strategy consisted of increasing antagonist activity with increasing agonist activity. And, in the third strategy, antagonist coactivation decreased at low force levels, then increased at high force levels. The three strategies were evaluated based on the joint angle's peak-to-peak movement and its ability to track a linear input command given by the correlation coefficient of the output signal versus linear input. Results showed that increasing antagonist activity resulted in decreasing peak-to-peak angle and a decreased signal tracking capability for each load condition. The latter, however, was not as obvious in the flywheel load (as compared with free-moving and pendulum conditions). A decreasing peak-to-peak torque for pendulum loads was also observed with increasing antagonist activity. In all loading conditions, maximal peak-to-peak angle and torque were present when a moderate degree of antagonist activity was engaged, and signal tracking capability improved with earlier engagement of the antagonist muscles. It is suggested that strategies using a combination of low-level coactivation, as described in the physiological literature and previous functional electrical stimulation (FES) studies, could satisfactorily address the issues of controllability and efficiency while maintaining long-term joint integrity.
Kaijian He
2016-11-01
Full Text Available The electricity market has experienced an increasing level of deregulation and reform over the years. There is an increasing level of electricity price fluctuation, uncertainty, and risk exposure in the marketplace. Traditional risk measurement models based on the homogeneous and efficient market assumption no longer suffice, facing the increasing level of accuracy and reliability requirements. In this paper, we propose a new Empirical Mode Decomposition (EMD-based Value at Risk (VaR model to estimate the downside risk measure in the electricity market. The proposed model investigates and models the inherent multiscale market risk structure. The EMD model is introduced to decompose the electricity time series into several Intrinsic Mode Functions (IMF with distinct multiscale characteristics. The Exponential Weighted Moving Average (EWMA model is used to model the individual risk factors across different scales. Experimental results using different models in the Australian electricity markets show that EMD-EWMA models based on Student’s t distribution achieves the best performance, and outperforms the benchmark EWMA model significantly in terms of model reliability and predictive accuracy.
Wang, Yaojin, E-mail: wangyaojin@hotmail.co [Key Laboratory of Inorganic Functional Material and Device, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 215 Chengbei Road, Jiading, Shanghai 201800 (China); Graduate University of the Chinese Academy of Sciences, Beijing 100049 (China); Zhao, Xiangyong [Key Laboratory of Inorganic Functional Material and Device, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 215 Chengbei Road, Jiading, Shanghai 201800 (China); Jiao, Jie; Liu, Linhua [Key Laboratory of Inorganic Functional Material and Device, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 215 Chengbei Road, Jiading, Shanghai 201800 (China); Graduate University of the Chinese Academy of Sciences, Beijing 100049 (China); Di, Wenning; Luo, Haosu [Key Laboratory of Inorganic Functional Material and Device, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 215 Chengbei Road, Jiading, Shanghai 201800 (China); Or, Siu Wing, E-mail: eeswor@polyu.edu.h [Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong)
2010-06-25
The effect of electrical resistance load on the magnetoelectric (ME) coupling of laminated composite of Tb{sub 0.3}Dy{sub 0.7}Fe{sub 1.92} (Terfenol-D) magnetostrictive alloy and 0.7Pb (Mg{sub 1/3}Nb{sub 2/3})O{sub 3}-0.3PbTiO{sub 3} (PMN-PT) piezoelectric single crystal is investigated at both non-resonance and resonance frequencies. The results show that (i) the ME coefficient and ME resonance frequency increase with the increase in electrical resistance load, and (ii) the maximum ME power occurs in open-circuit condition. The present study provides the basis for the design of ME sensors and their signal-processing and electronic circuits.
Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads
Parlos, Alexander G.; Toliyat, Hamid A.
2005-01-01
An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.
Analysis of the blasting effect on the electric shove loading efficiency of the open pit
FU Tian-guang; SUN Ying
2008-01-01
The connection between blasting cost and comprehensive cost is the main concern. Some blasting effect factors (such as unit explosive consumption, uniformity of blockness, shape and porosity of blasting heap), which had an influence on electric shove loading efficiency, were analyzed. In the end a project to properly increase in blasting cost to decrease the comprehensive cost was put forward. At the same time, the hole-by-hole blasting is effective technology to improve blasting effect.
Development of an Internet of Things based Electricity Load Management System
Dr. D. O. Dike
2016-08-01
Full Text Available Continuous overload to a power system is a problem as it reduces the life span of the generators. Load management is very vital in optimizing the performance of generating plants by properly managing the generated energy. During peak demand times, the energy used by consumers are expensive compared to that used during off peak demand time; this is because utility companies need to engage bigger generators and other infrastructures in order to supply the demanded energy. To prevent the need for the procurement of bigger generators and other infrastructures needed to augment electrical power needed by consumers during peak demand time, an Internet of Things (IoT based Electricity Load Management System is developed in this paper. The Arduino mega 2560 board and the Arduino WiFi Shield 101 are used in the controller and connectivity elements respectively, ACS712 module is employed in the sensory block to measure current; in order to determine the power being used by loads while Solid State Relays are used for actuation purposes. The entire blocks were integrated to form a functional system whose mode of operation is based on IoT technology that can be employed for effective management electrical energy.
Neto, Joao C. do L, E-mail: jcaldas@ufam.edu.br [Group of Optimization and Fuzzy Systems, Federal University of Amazonas, General Rodrigo Octavio Jordao Ramos Avenue, 3000, Academic Campus, 69077-000 Manaus, Amazonas (Brazil); Costa Junior, Carlos T. da [Postgraduate Program in Electrical Engineering, Institute of Technology, Federal University of Para, Augusto Correa Street, 1, Guama, 66075-900 Belem, Para (Brazil); Bitar, Sandro D.B. [Group of Optimization and Fuzzy Systems, Federal University of Amazonas, General Rodrigo Octavio Jordao Ramos Avenue, 3000, Academic Campus, 69077-000 Manaus, Amazonas (Brazil); Junior, Walter B. [Postgraduate Program in Electrical Engineering, Institute of Technology, Federal University of Para, Augusto Correa Street, 1, Guama, 66075-900 Belem, Para (Brazil)
2011-09-15
Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the {alpha}-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs. - Highlights: > A decision support system has been developed using SARIMA with fuzzy systems and optimizations tools. > It assists the decision-making process for planning the expansion in isolated thermoelectric systems. > The {alpha}-cut concept facilitated the evaluation of risks for the cost of electricity production. > Provides decisions using various forecasted interval for this cost with different membership values.
Dynamic additional loads influencing the fatigue life of gears in an electric vehicle transmission
G.Belingardi
2014-10-01
Full Text Available In recent years the implementation of the electric engine in the automotive industries has been increasingly marked. The speed of the electric motors is much higher than the combustion engine ones, bringing transmission gears to be subjected to high dynamic loads. For this reason the dynamic effects on fatigue life of these components have be taken into account in a more careful way respect to what is done with the usual gears. In the present work the overload effects due to both speed and meshing in a gear couple of an electric vehicle transmission have been analyzed. The electric vehicle is designed for urban people mobility and presents all the requirements to be certified as M1 vehicle (a weight less than 600 kg and a maximum speed more than 90 Km/h. To investigate the overload effects of teeth in contact, the reference gear design Standards (ISO 6336 introduce a specific multiplicative factor to the applied load called Internal Dynamic Factor (Kv. Aim of this work is to evaluate how dynamic overloads may influence the fatigue life of the above quoted gears in term of durability. To this goal, Kv values have been calculated by means of the analytical equations (ISO 6336 Methods B and C and then they have been compared with the results coming from multibody simulations, involving full rigid and rigid-flexible models.
Thermal Energy Storage for Building Load Management: Application to Electrically Heated Floor
Hélène Thieblemont
2016-07-01
Full Text Available In cold climates, electrical power demand for space conditioning becomes a critical issue for utility companies during certain periods of the day. Shifting a portion or all of it to off-peak periods can help reduce peak demand and reduce stress on the electrical grid. Sensible thermal energy storage (TES systems, and particularly electrically heated floors (EHF, can store thermal energy in buildings during the off-peak periods and release it during the peak periods while maintaining occupants’ thermal comfort. However, choosing the type of storage system and/or its configuration may be difficult. In this paper, the performance of an EHF for load management is studied. First, a methodology is developed to integrate EHF in TRNSYS program in order to investigate the impact of floor assembly on the EHF performance. Then, the thermal comfort (TC of the night-running EHF is studied. Finally, indicators are defined, allowing the comparison of different EHF. Results show that an EHF is able to shift 84% of building loads to the night while maintaining acceptable TC in cold climate. Moreover, this system is able to provide savings for the customer and supplier if there is a significant difference between off-peak and peak period electricity prices.
Comparison and Analysis of Magnetic-Geared Permanent Magnet Electrical Machine at No-Load
Liu Xiping
2014-12-01
Full Text Available Magnetic-geared permanent magnet (MGPM electrical machine is a new type of machine by incorporating magnetic gear into PM electrical machine, and it may be in operation with low-speed, high-torque and direct-driven. In this paper, three types of MGPM machines are present, and a quantitative comparison among them is performed by finite element analysis (FEA. The magnetic field distribution, stable torque and back EMF are obtained at no-load. The results show that three types of MGPM machine are suitable for different application fields respectively according to their own advantages, such as high torque and back EMF, which form an important foundation for MGPM electrical machine research.
I Ketut Wijaya
2015-12-01
Full Text Available Usage Electric power is very easy to do, because the infrastructure for connecting already available and widely sold. Consumption electric power is not accompanied by the ability to recognize electric power. The average increase of electricity power in Bali in extreme weather reaches 10% in years 2014, so that Bali suffered power shortages and PLN as the manager of electric power to perform scheduling on of electric power usage. Scheduling is done because many people use electric power as the load of fan and Air Conditioner exceeding the previous time. Load of fan, air conditioning, and computers including non-linear loads which can add heat on the conductor of electricity. Non-linear load and hot weather can lead to heat on conductor so insulation damaged and cause electrical short circuit. Data of electric power obtained through questionnaires, surveys, measurement and retrieve data from various parties. Fires that occurred in 2014, namely 109 events, 44 is event caused by an electric short circuit (approximately 40%. Decrease power factors can cause losses of electricity and hot. Heat can cause and adds heat on the conductor electric. The analysis showed understanding electric power of the average is 27,700 with value between 20 to 40. So an understanding of the electrical power away from the understand so that many errors because of the act own. Installation tool ELCB very necessary but very necessary provide counseling of electricity to the community.
Naoto Fujita; Shinichiro Murakami; Hidemi Fujino
2011-01-01
High-load isometric exercise is considered an effective countermeasure against muscle atrophy, but therapeutic electrical stimulation for muscle atrophy is often performed without loading. In the present study, we investigated the combined effectiveness of electrical stimulation and high-load isometric contraction in preventing muscle atrophy induced by hindlimb unloading. Electrical stimulation without loading resulted in slight attenuation of muscle atrophy. Moreover, combining electrical s...
Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts
2013-12-01
geothermal energy, the DoD’s wind power production is still insignificant, despite the fact that wind power is one of the most abundant and promising...million American homes, or the same amount of electricity as 10 nuclear power plants [16]. This 60 GW wind power capacity reduces carbon dioxide (CO2...thermal power plants [16]. 8 Figure 4. Global cumulative installed wind capacity 1996-2012 (from [18]) Despite its abundance, wind energy is
New approaches to provide ride-through for critical loads in electric power distribution systems
Montero-Hernandez, Oscar C.
2001-07-01
The extensive use of electronic circuits has enabled modernization, automation, miniaturization, high quality, low cost, and other achievements regarding electric loads in the last decades. However, modern electronic circuits and systems are extremely sensitive to disturbances from the electric power supply. In fact, the rate at which these disturbances happen is considerable as has been documented in recent years. In response to the power quality concerns presented previously, this dissertation is proposing new approaches to provide ride-through for critical loads during voltage disturbances with emphasis on voltage sags. In this dissertation, a new approach based on an AC-DC-AC system is proposed to provide ride-through for critical loads connected in buildings and/or an industrial system. In this approach, a three-phase IGBT inverter with a built in Dc-link voltage regulator is suitably controlled along with static by-pass switches to provide continuous power to critical loads. During a disturbance, the input utility source is disconnected and the power from the inverter is connected to the load. The remaining voltage in the AC supply is converted to DC and compensated before being applied to the inverter and the load. After detecting normal utility conditions, power from the utility is restored to the critical load. In order to achieve an extended ride-through capability a second approach is introduced. In this case, the Dc-link voltage regulator is performed by a DC-DC Buck-Boost converter. This new approach has the capability to mitigate voltage variations below and above the nominal value. In the third approach presented in this dissertation, a three-phase AC to AC boost converter is investigated. This converter provides a boosting action for the utility input voltages, right before they are applied to the load. The proposed Pulse Width Modulation (PWM) control strategy ensures independent control of each phase and compensates for both single-phase or poly
Fluctuation analysis of high frequency electric power load in the Czech Republic
Kracík, Jiří; Lavička, Hynek
2016-11-01
We analyze the electric power load in the Czech Republic (CR) which exhibits a seasonality as well as other oscillations typical for European countries. Moreover, we detect the 1/f noise property of electrical power load with extra additional peaks that allows to separate it into a deterministic and stochastic part. We then focus on the analysis of the stochastic part using improved Multi-fractal Detrended Fluctuation Analysis method (MFDFA) to investigate power load datasets with a minute resolution. Extracting the noise part of the signal by using Fourier transform allows us to apply this method to obtain the fluctuation function and to estimate the generalized Hurst exponent together with the correlated Hurst exponent, its improvement for the non-Gaussian datasets. The results exhibit a strong presence of persistent behavior or strong anti-persistent behavior for the differences and the dataset is characterized by a non-Gaussian skewed distribution. There are also indications for the presence of the probability distribution that has heavier tail than the Gaussian distribution.
An Electrical Energy Consumer Load Monitoring and Control System Through SMS Based
J. Tsado
2012-05-01
Full Text Available This study presents an SMS based Consumer Load Monitoring and Control System (CLMCS incorporating the widely used GSM network to facilitate the communication of electrical energy consumption by the user to his mobile phone. Its operation is centered on an AT89C52 microcontroller programmed in assembly language. A dedicated GSM modem with a SIM card is interfaced to the ports of the microcontroller through a PNP transistor (BC557 and a Normally Closed (NC relay to send SMS notification alert to user’s mobile phone when power supply is restored to his premises and whenever the energy consumed exceeds the maximum value set by the end user in this case 1 kW. This enables the consumer to respond promptly by cutting off power supply to his load unit when not needed. With this, a great deal of energy is saved and the consumer enjoys maximum satisfaction of the electrical energy paid for; hence an improvement, stability and utilization of electrical energy are achievable.
Monitoring and Characterization of Miscellaneous Electrical Loads in a Large Retail Environment
Gentile-Polese, L.; Frank, S.; Sheppy, M.; Lobato, C.; Rader, E.; Smith, J.; Long, N.
2014-02-01
Buildings account for 40% of primary energy consumption in the United States (residential 22%; commercial 18%). Most (70% residential and 79% commercial) is used as electricity. Thus, almost 30% of U.S. primary energy is used to provide electricity to buildings. Plug loads play an increasingly critical role in reducing energy use in new buildings (because of their increased efficiency requirements), and in existing buildings (as a significant energy savings opportunity). If all installed commercial building miscellaneous electrical loads (CMELs) were replaced with energy-efficient equipment, a potential annual energy saving of 175 TWh, or 35% of the 504 TWh annual energy use devoted to MELs, could be achieved. This energy saving is equivalent to the annual energy production of 14 average-sized nuclear power plants. To meet DOE's long-term goals of reducing commercial building energy use and carbon emissions, the energy efficiency community must better understand the components and drivers of CMEL energy use, and develop effective reduction strategies. These goals can be facilitated through improved data collection and monitoring methodologies, and evaluation of CMELs energy-saving techniques.
Determining the Interruptible Load with Strategic Behavior in a Competitive Electricity Market
Tae Hyun Yoo
2014-12-01
Full Text Available In a deregulated market, independent system operators meet power balance based on supply and demand bids to maximize social welfare. Since electricity markets are typically oligopolies, players with market power may withhold capacity to maximize profit. Such exercise of market power can lead to various problems, including increased electricity prices, and hence lower social welfare. Here we propose an approach to maximize social welfare and prevent the exercising of market power by means of interruptible loads in a competitive market environment. Our approach enables management of the market power by analyzing the benefit to the companies of capacity withdrawal and scheduling resources with interruptible loads. Our formulation shows that we can prevent power companies and demand-resource owners from exercising market powers. The oligopolistic conditions are described using the Cournot model to reflect the capacity withdrawal in electricity markets. The numerical results confirm the effectiveness of proposed method, via a comparison of perfect competition and oligopoly scenarios. Our approach provides reductions in market-clearing prices, increases in social welfare, and more equal distribution of surpluses between players.
Morales, Ricardo; Badesa, Francisco J; García-Aracil, Nicolas; Perez-Vidal, Carlos; Sabater, Jose María
2012-01-01
This paper presents a microdevice for monitoring, control and management of electric loads at home. The key idea is to compact the electronic design as much as possible in order to install it inside a Schuko socket. Moreover, the electronic Schuko socket (electronic microdevice + Schuko socket) has the feature of communicating with a central unit and with other microdevices over the existing powerlines. Using the existing power lines, the proposed device can be installed in new buildings or in old ones. The main use of this device is to monitor, control and manage electric loads to save energy and prevent accidents produced by different kind of devices (e.g., iron) used in domestic tasks. The developed smart device is based on a single phase multifunction energy meter manufactured by Analog Devices (ADE7753) to measure the consumption of electrical energy and then to transmit it using a serial interface. To provide current measurement information to the ADE7753, an ultra flat SMD open loop integrated circuit current transducer based on the Hall effect principle manufactured by Lem (FHS-40P/SP600) has been used. Moreover, each smart device has a PL-3120 smart transceiver manufactured by LonWorks to execute the user's program, to communicate with the ADE7753 via serial interface and to transmit information to the central unit via powerline communication. Experimental results show the exactitude of the measurements made using the developed smart device.
Gillenwater, Michael [Science, Technology and Environmental Policy Program, Woodrow Wilson School of Public and International Affairs, Robertson Hall, Princeton University, Princeton, NJ 08540 (United States); Breidenich, Clare [Independent consultant, Seattle, WA (United States)
2009-01-15
Several western states have considered developing a regulatory approach to reduce greenhouse gas (GHG) emissions from the electric power industry, referred to as a load-based (LB) cap-and-trade scheme. A LB approach differs from the traditional source-based (SB) cap-and-trade approach in that the emission reduction obligation is placed upon Load Serving Entities (LSEs), rather than electric generators. The LB approach can potentially reduce the problem of emissions leakage, relative to a SB system. For any of these proposed LB schemes to be effective, they must be compatible with modern, and increasingly competitive, wholesale electricity markets. LSE's are unlikely to know the emissions associated with their power purchases. Therefore, a key challenge for a LB scheme is how to assign emissions to each LSE. This paper discusses the problems with one model for assigning emissions under a LB scheme and proposes an alternative, using unbundled Generation Emission Attribute Certificates. By providing a mechanism to internalize an emissions price signal at the generator dispatch level, the tradable certificate model addresses both these problems and provides incentives identical to a SB scheme. (author)
Gillenwater, Michael [Science, Technology and Environmental Policy Program, Woodrow Wilson School of Public and International Affairs, Robertson Hall, Princeton University, Princeton, NJ 08540 (United States)], E-mail: gillenwater@alum.mit.edu; Breidenich, Clare [Independent consultant, Seattle, WA (United States)], E-mail: cbreidenich@yahoo.com
2009-01-15
Several western states have considered developing a regulatory approach to reduce greenhouse gas (GHG) emissions from the electric power industry, referred to as a load-based (LB) cap-and-trade scheme. A LB approach differs from the traditional source-based (SB) cap-and-trade approach in that the emission reduction obligation is placed upon Load Serving Entities (LSEs), rather than electric generators. The LB approach can potentially reduce the problem of emissions leakage, relative to a SB system. For any of these proposed LB schemes to be effective, they must be compatible with modern, and increasingly competitive, wholesale electricity markets. LSE's are unlikely to know the emissions associated with their power purchases. Therefore, a key challenge for a LB scheme is how to assign emissions to each LSE. This paper discusses the problems with one model for assigning emissions under a LB scheme and proposes an alternative, using unbundled Generation Emission Attribute Certificates. By providing a mechanism to internalize an emissions price signal at the generator dispatch level, the tradable certificate model addresses both these problems and provides incentives identical to a SB scheme.
Carlos Perez-Vidal
2012-04-01
Full Text Available This paper presents a microdevice for monitoring, control and management of electric loads at home. The key idea is to compact the electronic design as much as possible in order to install it inside a Schuko socket. Moreover, the electronic Schuko socket (electronic microdevice + Schuko socket has the feature of communicating with a central unit and with other microdevices over the existing powerlines. Using the existing power lines, the proposed device can be installed in new buildings or in old ones. The main use of this device is to monitor, control and manage electric loads to save energy and prevent accidents produced by different kind of devices (e.g., iron used in domestic tasks. The developed smart device is based on a single phase multifunction energy meter manufactured by Analog Devices (ADE7753 to measure the consumption of electrical energy and thento transmit it using a serial interface. To provide current measurement information to the ADE7753, an ultra flat SMD open loop integrated circuit current transducer based on the Hall effect principle manufactured by Lem (FHS-40P/SP600 has been used. Moreover, each smart device has a PL-3120 smart transceiver manufactured by LonWorks to execute the user’s program, to communicate with the ADE7753 via serial interface and to transmit information to the central unit via powerline communication. Experimental results show the exactitude of the measurements made using the developed smart device.
Load profile analysis tool for electrical appliances in households assisted by CPS
Rodrigues, F.; Cardeira, C.; Calado, J.M.F.; Melício, R.
2016-01-01
This paper presents a methodology to forecast the hourly and daily consumption in households assisted by cyber physical systems. The methodology was validated using a database of consumption of a set of 93 domestic consumers. Forecast tools used were based on Fast Fourier Series and Generalized Reduced Gradient. Both tools were tested and their forecast results were compared. The paper shows that both tools allow obtaining satisfactory results for energy consumption forecasting. (C) 2016 The ...
2009-07-30
Investigation of Control Algorithms for Tracked Vehicle Mobility Load Emulation for a Combat Hybrid Electric Power System Jarrett Goodell and...TITLE AND SUBTITLE Investigation of Control Algorithms for Tracked Vehicle Mobility Load Emulation for a Combat Hybrid Electric Power System 5a...for ~ 22 ton tracked vehicle • Tested and Developed: – Motors, Generators, Batteries, Inverters, DC-DC Converters , Thermal Management, Pulse Power
1991 Pacific Northwest Loads and Resources Study, Technical Appendix: Volume 1.
United States. Bonneville Power Administration.
1992-03-01
This publication provides detailed documentation of the load forecast scenarios and assumptions used in preparing BPA's 1991 Pacific Northwest Loads and Resources Study (the Study). This is one of two technical appendices to the Study; the other appendix details the utility-specific loads and resources used in the Study. The load forecasts and assumption were developed jointly by Bonneville Power Administration (BPA) and Northwest Power Planning Council (Council) staff. This forecast is also used in the Council's 1991 Northwest Conservation and Electric Power Plan (1991 Plan).
Medium and long term load forecasting considering data uncertainty%考虑数据不确定性的中长期电力负荷预测
郑志杰; 李磊; 赵兰明
2011-01-01
中长期负荷预测时间跨度较长,其基础数据受诸多因素影响,具有不确定性和不可控性.引入蒙特卡罗算法和区间算法处理中长期负荷预测中的数据不确定性问题.根据历史年度实际情况,假定基础数据在某一范围内存在不确定性,采用蒙特卡罗算法构建了计算流程,可以得到界于某一区间的负荷预测值;采用区间算法描述基础数据的不确定性,针对区间算法固有的过估计问题,通过推导适合的公式,可以避免产生过度保守的结果,只需一次计算,就可以严格分析数据不确定性对预测结果的影响,具有节省计算时间的优点.在考虑基础数据存在不确定性情况下,通过某省电网负荷预测实例计算并与传统预测方法相比较,验证了两种负荷预测方法可以评估数据不确定性对负荷结果的影响,避免得到过度保守的负荷预测值.%Mid-long term load forecasting goes through a long time and the base data are influenced by a number of factors, most of which are beyond the control and with uncertainty. In order to deal with the data uncertainty problem in mid-long term load forecasting, Monte Carlo method and interval arithmetic are introduced. According to actual situation in history, it is assumed that the base data have uncertainty in a certain range. Using Monte Carlo method to construct the calculation process can obtain the load prediction result in a certain interval. Using interval algorithm to describe the uncertainty of base data, and for reducing overestimation of interval arithmetic, suitable formula is deduced to avoid the conservative results, with the advantages of saving computing time because only once computation can seriously analyze the effect of data uncertainty on prediction results. The proposed two methods have been tested on the load forecasting of one province. In consideration of the base data with uncertainty,by comparing with conventional load forecasting
Christopher Bennett
2014-04-01
Full Text Available This paper set out to identify the significant variables which affect residential low voltage (LV network demand and develop next day total energy use (NDTEU and next day peak demand (NDPD forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX and neural network (NN techniques. The data used for this research was collected from a LV transformer serving 128 residential customers. It was observed that temperature accounted for half of the residential LV network demand. The inclusion of the double exponential smoothing algorithm, autoregressive terms, relative humidity and day of the week dummy variables increased model accuracy. In terms of R2 and for each modelling technique and phase, NDTEU hindcast accuracy ranged from 0.77 to 0.87 and forecast accuracy ranged from 0.74 to 0.84. NDPD hindcast accuracy ranged from 0.68 to 0.74 and forecast accuracy ranged from 0.56 to 0.67. The NDTEU models were more accurate than the NDPD models due to the peak demand time series being more variable in nature. The NN models had slight accuracy gains over the ARIMAX models. A hybrid model was developed which combined the best traits of the ARIMAX and NN techniques, resulting in improved hindcast and forecast fits across the all three phases.
基于两重门限GARCH模型的短期负荷预测%Short term load forecasting based on double-threshold GARCH models
王玉荣; 万秋兰; 陈昊
2011-01-01
针对负荷时间序列的非线性和波动性特征,在研究负荷时间序列波动性门限特征的基础上,引入冲量门限的概念,提出了一种基于两重门限GARCH模型的短期负荷预测新方法.利用条件极大似然估计方法,估计了模型参数.同时,考虑到负荷时间序列波动的厚尾效应,将模型推广为服从非高斯分布假设下的情形,建立了2种基于厚尾假设的两重门限GARCH类负荷预测模型.利用所提出的混合信息冲击曲面,分析了不同性质的冲击和冲量对负荷时间序列波动性的影响.实际算例基于南京地区日用电量数据进行了短期负荷预测,验证了模型及方法的可行性和有效性.算例结果表明,服从广义误差分布的两重门限GARCH模型预测效果满意.%Considering the nonlinearity and volatility of load time series, threshold characteristics in load time series are analyzed. The concept of momentum threshold is employed and a novel double-threshold generalized auto-regressive conditional heteroskedasticity (DT-GARCH) model is proposed for short term load forecasting. By using the conditional maximum likelihood estimation (CMLE) , the parameters are estimated. In addition, with fat-tail effect in volatility, the proposed models with non-Gaussian distributions are highlighted and estimated. Furthermore, the hybrid news impact surface is proposed to help analyze the impact of different shocks and momentums to the load time series. In case study, short term load forecasting is carried out based on the historical daily power consumption data of Nanjing, which validates the feasibility and effectiveness of the proposed model. Numerical results indicate that the DT-GARCH model with generalized error distribution provides satisfying forecasting results.
Utilization of Electric Vehicles and Their Used Batteries for Peak-Load Shifting
Muhammad Aziz
2015-04-01
Full Text Available The utilization of electric vehicles (EV and their used batteries in supporting small-scale energy management systems were studied. Both theoretical study and practical demonstration were performed to measure the feasibility of the developed system. Each five EVs and used EV batteries were used along with 20 kW photovoltaic (PV panels as a renewable energy source. The main objective of the developed system is performing a peak-load shifting by utilizing EVs, used EV batteries and PV panels. The planning of load leveling was performed 24 h ahead for each 30 min period. The studies showed that the application of EVs and used EV batteries in supporting certain small-scale energy management systems is feasible. In addition, some findings during the demonstration test were listed and analyzed for the purpose of further system development and deployment.
Crack density and electrical resistance in indium-tin-oxide/polymer thin films under cyclic loading
Mora Cordova, Angel
2014-11-01
Here, we propose a damage model that describes the degradation of the material properties of indium-tin-oxide (ITO) thin films deposited on polymer substrates under cyclic loading. We base this model on our earlier tensile test model and show that the new model is suitable for cyclic loading. After calibration with experimental data, we are able to capture the stress-strain behavior and changes in electrical resistance of ITO thin films. We are also able to predict the crack density using calibrations from our previous model. Finally, we demonstrate the capabilities of our model based on simulations using material properties reported in the literature. Our model is implemented in the commercially available finite element software ABAQUS using a user subroutine UMAT.[Figure not available: see fulltext.].
Ultrabroadband Microwave Metamaterial Absorber Based on Electric SRR Loaded with Lumped Resistors
Zhao, Jingcheng; Cheng, Yongzhi
2016-10-01
An ultrabroadband microwave metamaterial absorber (MMA) based on an electric split-ring resonator (ESRR) loaded with lumped resistors is presented. Compared with an ESRR MMA, the composite MMA (CMMA) loaded with lumped resistors offers stronger absorption over an extremely extended bandwidth. The reflectance simulated under different substrate loss conditions indicates that incident electromagnetic (EM) wave energy is mainly consumed by the lumped resistors. The simulated surface current and power loss density distributions further illustrate the mechanism underlying the observed absorption. Further simulation results indicate that the performance of the CMMA can be tuned by adjusting structural parameters of the ESRR and lumped resistor parameters. We fabricated and measured MMA and CMMA samples. The CMMA yielded below -10 dB reflectance from 4.4 GHz to 18 GHz experimentally, with absorption bandwidth and relative bandwidth of 13.6 GHz and 121.4%, respectively. This ultrabroadband microwave absorber has potential applications in the electromagnetic energy harvesting and stealth fields.
基于回声状态网络的电力市场电价预测%Echo-state-network based electricity price forecasting in electric power market
任远
2016-01-01
Traditional neural network based electricity price forecasting algorithm fails to meet current demands by future electric power market, with low accuracy and long computation time when the electric power price changes greatly. Using the method based on Echo-State-Network (ESN), an electricity power price short-term forecasting approach is proposed. Firstly, the principle of ESN is introduced and discussed. On this basis, the electricity power price short-term forecasting approach is proposed, including parameter selection, sampling data pre-processing and ESN training and forecast process. Then, the short-term electricity price forecasting is performed by ESN and BP neural network. The simulation results show that using ESN the short-term electricity price can be forecasted more quickly and steadily.%传统的神经网络算法在电价变化剧烈的情况下，精度较低并且所耗费的时间较长，难以满足电力市场发展的需求。为解决该问题，提出了一种基于回声状态网络(ESN)的短期电价预测方法。所提方法介绍了基于回声状态网络的预测原理，提出了电力市场短期电价的预测机制，包括参数选取、采样数据预处理和 ESN 训练及预测过程；并分别采用回声状态网络和反向传播算法(BP)神经网络进行短期电价预测。经过仿真验证，所提出的基于回声状态网络的电价预测具有较好的准确率和可行性。
Effects of interruptible load program on equilibrium outcomes of electricity markets with wind power
An, Xuena; Zhang, Shaohua; Li, Xue [Shanghai Univ. (China). Key Lab. of Power Station Automation Technology
2013-07-01
High wind power penetration presents a lot of challenges to the flexibility and reliability of power system operation. In this environment, various demand response (DR) programs have got much attention. As an effective measure of demand response programs, interruptible load (IL) programs have been widely used in electricity markets. This paper addresses the problem of impacts of the IL programs on the equilibrium outcomes of electricity wholesale markets with wind power. A Cournot equilibrium model of wholesale markets with wind power is presented, in which IL programs is included by a market demand model. The introduction of the IL programs leads to a non-smooth equilibrium problem. To solve this equilibrium problem, a novel solution method is proposed. Numerical examples show that IL programs can lower market price and its volatility significantly, facilitate the integration of wind power.
Analysis of electrical circuits with variable load regime parameters projective geometry method
Penin, A
2015-01-01
This book introduces electric circuits with variable loads and voltage regulators. It allows to define invariant relationships for various parameters of regime and circuit sections and to prove the concepts characterizing these circuits. Generalized equivalent circuits are introduced. Projective geometry is used for the interpretation of changes of operating regime parameters. Expressions of normalized regime parameters and their changes are presented. Convenient formulas for the calculation of currents are given. Parallel voltage sources and the cascade connection of multi-port networks are d
Analysis of the blasting effect on the electric shove loading efficiency of the open pit
FU Tian-guang; SUN Ying
2008-01-01
The connection between blasting cost and comprehensive cost is the main concern.Some blasting effect factors (such as unit explosive consumption,uniformity of blockness,shape and porosity of blasting heap),which had an influence on electric shove loading efficiency,were analyzed.In the end a project to properly increase in blasting cost to decrease the comprehensive cost was put forward.At the same time,the hole-by-hole blasting is effective technology to improve blasting effect.
Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
Yuefei Wang
2016-10-01
Full Text Available As a result of severe environmental pressure and stringent government regulations, refined energy management for vehicles has become inevitable. To improve vehicle fuel economy, this paper presents a bus-based energy management system for the electrical system of internal combustion engine vehicles. Both the model of an intelligent alternator and the model of a lead-acid battery are discussed. According to these models, the energy management for a vehicular electrical system is formulated as a global optimal control problem which aims to minimize fuel consumption. Pontryagin’s minimum principle is applied to solve the optimal control problem to realize a real-time control strategy for electrical energy management in vehicles. The control strategy can change the output of the intelligent alternator and the battery with the changes of electrical load and driving conditions in real-time. Experimental results demonstrate that, compared to the traditional open-loop control strategy, the proposed control strategy for vehicle energy management can effectively reduce fuel consumption and the fuel consumption per 100 km is decreased by approximately 1.7%.
Cogo, Joao Roberto [Escola Federal de Engenharia de Itajuba, MG (Brazil)
1994-12-31
The non linear electrical loads can give rise to a number of disturbances in electrical power networks. Among them, the high consumption of relative power is to be noted and so is the several harmonic components which may be injected in the industry system and very often in the utility system. So, by using appropriate technical considerations, as well as measurements in typical special electrical loads, such negative effects are analyzed and ways of minimizing them are suggested. (author) 3 refs., 11 figs., 6 tabs.
Real-Time Load-Side Control of Electric Power Systems
Zhao, Changhong
Two trends are emerging from modern electric power systems: the growth of renewable (e.g., solar and wind) generation, and the integration of information technologies and advanced power electronics. The former introduces large, rapid, and random fluctuations in power supply, demand, frequency, and voltage, which become a major challenge for real-time operation of power systems. The latter creates a tremendous number of controllable intelligent endpoints such as smart buildings and appliances, electric vehicles, energy storage devices, and power electronic devices that can sense, compute, communicate, and actuate. Most of these endpoints are distributed on the load side of power systems, in contrast to traditional control resources such as centralized bulk generators. This thesis focuses on controlling power systems in real time, using these load side resources. Specifically, it studies two problems. (1) Distributed load-side frequency control: We establish a mathematical framework to design distributed frequency control algorithms for flexible electric loads. In this framework, we formulate a category of optimization problems, called optimal load control (OLC), to incorporate the goals of frequency control, such as balancing power supply and demand, restoring frequency to its nominal value, restoring inter-area power flows, etc., in a way that minimizes total disutility for the loads to participate in frequency control by deviating from their nominal power usage. By exploiting distributed algorithms to solve OLC and analyzing convergence of these algorithms, we design distributed load-side controllers and prove stability of closed-loop power systems governed by these controllers. This general framework is adapted and applied to different types of power systems described by different models, or to achieve different levels of control goals under different operation scenarios. We first consider a dynamically coherent power system which can be equivalently modeled with
NONE
1996-08-01
Hokuriku Electric Power Co., Inc. aims at an around 2% improvement of the load factor up to 2005, by which the quick and proper service and the proposal of load leveling menu are planned. This paper describes an outline of the investigation of load leveling. Various programs have been proposed so that the customers can further shift the load by their consideration. Proposed systems include the time-of-day electricity rate system, the load regulation contract system for industries, the seasonal time-of-day rate system, the electric power system for snowmelt in which the load is dumped at the peak, and the secondary electric power system for snowmelt. Accompanying with the revision of electric utility law, the enlargement of its available time, the price reduction, and the discount rate system for the ice regenerative air conditioners have been provided. For the business activities, a demonstration model house was exhibited to indicate a proper house with local characteristics in Hokuriku district. Furthermore, the spreading activities of regenerative systems and the consulting activities have been positively promoted. 4 figs., 1 tab.
Weber, Andrew; Lanzisera, Steven; Liao, Anna; Meier, Alan
2014-08-11
Plug loads represent 30percent of total electricity use in residential buildings. Significant energy savings would result from an accurate understanding of which miscellaneous electric devices are using energy, at what time, and in what quantity. Commercially available plug load monitoring and control solutions replace or limit the attached device's native controls - forcing the user to adapt to a separate set of controls associated with the monitoring and control hardware. A better solution is integration of these capabilities at the power supply level. In this paper, we demonstrate a method achieving this integration. Our solution allows unobtrusive power monitoring and control while retaining native device control features. Further, our prototype enables intelligent behaviors by allowing devices to respond to the state of one another automatically. The CPS enables energy savings while demonstrating an added level of functionality to the user. If CPS technology became widespread in devices, a combination of automated and human interactive solutions would enable high levels of energy savings in buildings.
Pǎcurar, Cristina; Hepuť, Teodor; Ardelean, Marius
2016-06-01
As the basic units in the preparation of steel, in industrial practice is used oxygen converters and electric arc furnaces. In research carried out has been taken into account structure analysis load electric arc furnaces of the specific consumption of electricity (kWh/t). Data to be achieved for a number of 96 batches, have been taken into account load holding metal of each assortment of scrap metal, these varieties being considered as independent parameters, and electricity consumption is considered dependent parameter. By processing the data in the EXCEL spreadsheet programs and MATLAB have been obtained correlations between parameters analyze, analytical results being presented and the graph. On the basis of an analysis of these correlations to choose optimal structure of the load in order to obtain an acceptable energy consumption from technical and economic point of view.
ANALYSIS OF ENERGY EFFICIENCY OF OPERATING MODES OF ELECTRICAL SYSTEMS WITH THE TRACTION LOADS
V. E. Bondarenko
2017-03-01
Full Text Available Innovative scenarios of reliable energy supply of transportation process aimed at reducing the specific energy consumption and increase energy efficiency of the systems of electric traction. The paper suggests innovative energy saving directions in traction networks of railways and new circuit solutions accessing traction substations in energy systems networks, ensure energy security of the transportation process. To ensure the energy security of rail transport special schemes were developed to propose the concept of external power traction substations, which would increase the number of connections to the networks of 220 – 330 kV, as well as the creation of transport and energy corridors, development of its own supply of electric networks of 110 kV substations and mobile RP-110 kV of next generation. Therefore, the investment program of the structures owned by the Ukrainian Railways (Ukrzaliznytsia need to be synchronized in their technological characteristics, as well as the criteria of reliability and quality of power supply with the same external energy investment programs. It is found that without any load on left or right supplying arm one of two less loaded phases of traction transformer begins generating specific modes in the supplying three-phase line. Thus, modes of mobile substation cause leakage in one of the phases of the supply line of traction transformers of active-capacitive current, and as a result generating energy in the main power line of 154 kV, which is fixed and calculated by electricity meters. For these three phase mode supply network is necessary to use 1st algorithm, i.e. taking into account the amount of electricity as the energy in all phases. For effective application of reactive power compensation devices in the AC traction power supply systems it is proposed to develop regulatory documentation on necessity of application and the order of choice of parameters and placement of compensation systems taking into
Hendron, R.; Eastment, M.
2006-08-01
In order to meet whole-house energy savings targets beyond 50% in residential buildings, it will be essential that new technologies and systems approaches be developed to address miscellaneous electric loads (MELs). These MELs are comprised of the small and diverse collection of energy-consuming devices found in homes, including what are commonly known as plug loads (televisions, stereos, microwaves), along with all hard-wired loads that do not fit into other major end-use categories (doorbells, security systems, garage door openers). MELs present special challenges because their purchase and operation are largely under the control of the occupants. If no steps are taken to address MELs, they can constitute 40-50% of the remaining source energy use in homes that achieve 60-70% whole-house energy savings, and this percentage is likely to increase in the future as home electronics become even more sophisticated and their use becomes more widespread. Building America (BA), a U.S. Department of Energy research program that targets 50% energy savings by 2015 and 90% savings by 2025, has begun to identify and develop advanced solutions that can reduce MELs.
Application of high-resolution domestic electricity load profiles in network modelling
Marszal, Anna Joanna; Mendaza, Iker Diaz de Cerio; Heiselberg, Per Kvols
2016-01-01
The ongoing development towards electrification of the energy consumption together with large deployment of renewable energy sources creates new challenges of variability and fluctuation of the electricity supply and increases complexity of the network operation. In order to capture all the parti......The ongoing development towards electrification of the energy consumption together with large deployment of renewable energy sources creates new challenges of variability and fluctuation of the electricity supply and increases complexity of the network operation. In order to capture all......-minute resolution. The load profiles of the household appliances are created using a bottom-up model, which uses the 1-minute cycle power use characteristics of a single appliance as the main building block. The profiles of heavy electric appliances, such as heat pump, are not included in the above......-mentioned model, as they are closely related to the thermal properties of a building. Therefore, two type of single family houses equipped with heat pump are simulated in EnergyPlus with 1-minute time step. The PV generation profile is obtained from a model developed in Matlab environment. In the second part...
无
2010-01-01
To reduce data variance caused by individual differences of different samples,a new experimental method is proposed by loading and unloading the axial forces with different frequencies and different waves of a certain load amplitude to the same rock sample without damaging it.Lag time segments are defined and fractionized into segments I and Ⅱ.Criterions for seg-mentation,definitions of relevant parameters and empirical analysis are also offered afterwards.In the course of sinusoidal loading,the serious peak value dislocation of the displacement variation rate and the loading rate is defined as peak dislocation.Meanwhile,the definition of the apparent tangent modulus is put forward and the linear relation between it and the vertical force in the frequency of 0.1,0.2,0.5 Hz sinusoidal loading segment is confirmed to be ever-present on the basis of the test data.Then the calculating formula of the deformation rate in non-lag time is deduced.It is thus suggested that the deformation rate should be codetermined by the loading rate df/dt and instant load f(t),which well explains the peak dislocation of the time-variable curve peak value of dl/dt and deformation rate of df/dt.Finally the lag time derivation model is established and by comparing the calculated values with the measured ones,it is demonstrated that the above formula offers a better simulation of the sandstone deformation rate in the sinusoidal loading segment,with the load amplitude being 96 kN and the frequency ranging from 0.1 Hz to 0.5 Hz.
NONE
1996-04-01
The Hokkaido Electric Power Co., Inc., aiming to enhance power generation efficiency through power load levelling, strives to expand and substantiate its electricity billing menu and to popularize and encourage the use of levelling-oriented apparatuses and systems most of which are designed for utilizing midnight power. The billing menu has in it a snow-melting power which is cut off for load levelling during the peak demand time zone. For domestic use, a time zone-specified lighting system named Dream Eight is created, which is one of the billing systems dependent upon time zone. Introduced therein for industrial use is a demand/supply adjustment contract system. Furthermore, in compliance with the amended Electricity Business Law that came into force in 1995, efforts are under way for revising the period wherein power is to be supplied for melting snow, expanding the scope of application of the power supply system dependent upon time zone, and newly introducing a heat accumulation assisted peak adjustment contract system and an operation adjustment contract system. As for business efforts in relation to load levelling, the company proposes household electrical systems centering about 200V high-efficiency apparatuses, electric water warmer contributing to the enhancement of year-round load levelling, popularization and reinforcement of electric snow melting systems, and power utilizing technologies capable of meeting local demands raised for example by agriculture and fishery.
Intelligent energy demand forecasting
Hong, Wei-Chiang
2013-01-01
This book offers approaches and methods to calculate optimal electric energy allocation, using evolutionary algorithms and intelligent analytical tools to improve the accuracy of demand forecasting. Focuses on improving the drawbacks of existing algorithms.
A survey on wind power ramp forecasting.
Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J. (Decision and Information Sciences); (INESC Porto)
2011-02-23
The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.
Global Energy Forecasting Competition 2012
Hong, Tao; Pinson, Pierre; Fan, Shu
2014-01-01
The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details on the asp......The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details...... on the aspects of the problem, the data, and a summary of the methods used by selected top entries. We also discuss the lessons learned from this competition from the organizers’ perspective. The complete data set, including the solution data, is published along with this paper, in an effort to establish...
Training Load Monitoring Algorithms on Highly Sub-Metered Home Electricity Consumption Data
Mario Berges; Ethan Goldman; H. Scott Matthews; Lucio Soibelman
2008-01-01
The growing interest in energy-efficient buildings is driving changes in investment, design, and occupant behavior. To better focus cost and resource conservation efforts, electricity consumption feedback can be used to provide motivation, guidance, and verification. Disaggregating by end-use helps both con-sumers and producers to identify targets for conservation. While hardware-based sub-metering is costly and labor-intensive, non-intrusive load monitoring (NILM) is capable of gathering detailed energy-use data with minimal equipment cost and installation time. However, variations in measurements between metering de-vices complicate the process of compiling the necessary appliance profiles. Future work involves the devel-opment of NILM algorithms using sensor fusion and detailed appliance-level data gathered from a highly-sensed house currently being constructed near Pittsburgh, Pennsylvania.
The influence of the structure of the metal load removal from liquid steel in electric arc furnaces
Pǎcurar, Cristina; Hepuť, Teodor; Crisan, Eugen
2016-06-01
One of the main technical and economic indicators in the steel industry and steel respectively the development it is the removal of liquid steel. This indicator depends on several factors, namely technology: the structure and the quality metal load, the degree of preparedness of it, and the content of non-metallic material accompanying the unit of drawing up, the technology for the elaboration, etc. research has been taken into account in drawing up steel electric arc furnace type spring EBT (Electric Bottom taping), seeking to load and removing components of liquid steel. Metal load has been composed of eight metal grades, in some cases with great differences in terms of quality. Data obtained were processed in the EXCEL spreadsheet programs and MATLAB, the results obtained being presented both graphically and analytically. On the basis of the results obtained may opt for a load optimal structure metal.
Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte
2010-01-01
and may represent the future of electricity markets in some ways, is chosen as the studied power system in this paper. A distribution system where wind power capacity is 126% of maximum loads is chosen as the study case. This paper presents a nonlinear load optimization method to real-time power price...... for demand side management in order to save the energy costs as much as possible. Simulation results show that the optimal load response to a real-time electricity price has some good impacts on power system constraints in a distribution system with high wind power penetrations....... for demand side management generates different load profiles and may have some impacts on power system constraints, such as voltage limits and capacity limits. The western Danish power system, which is currently the grid area in the world that has the largest share of wind power in its generation profiles...
Forsberg, Charles W [ORNL; Conklin, Jim [ORNL
2007-09-01
A combined-cycle power plant is described that uses (1) heat from a high-temperature nuclear reactor to meet base-load electrical demands and (2) heat from the same high-temperature reactor and burning natural gas, jet fuel, or hydrogen to meet peak-load electrical demands. For base-load electricity production, fresh air is compressed; then flows through a heat exchanger, where it is heated to between 700 and 900 C by heat provided by a high-temperature nuclear reactor via an intermediate heat-transport loop; and finally exits through a high-temperature gas turbine to produce electricity. The hot exhaust from the Brayton-cycle gas turbine is then fed to a heat recovery steam generator that provides steam to a steam turbine for added electrical power production. To meet peak electricity demand, the air is first compressed and then heated with the heat from a high-temperature reactor. Natural gas, jet fuel, or hydrogen is then injected into the hot air in a combustion chamber, combusts, and heats the air to 1300 C-the operating conditions for a standard natural-gas-fired combined-cycle plant. The hot gas then flows through a gas turbine and a heat recovery steam generator before being sent to the exhaust stack. The higher temperatures increase the plant efficiency and power output. If hydrogen is used, it can be produced at night using energy from the nuclear reactor and stored until needed. With hydrogen serving as the auxiliary fuel for peak power production, the electricity output to the electric grid can vary from zero (i.e., when hydrogen is being produced) to the maximum peak power while the nuclear reactor operates at constant load. Because nuclear heat raises air temperatures above the auto-ignition temperatures of the various fuels and powers the air compressor, the power output can be varied rapidly (compared with the capabilities of fossil-fired turbines) to meet spinning reserve requirements and stabilize the electric grid. This combined cycle uses the
A. M. Afanasov
2014-12-01
Full Text Available Purpose. The research data are aimed to identify the regulatory principles of unbalanced electromagnetic moment of mutually loaded electric machines of traction rolling stock and multiple unit of main and industrial transport. The purpose of this study is energy efficiency increase of the testing of traction electric machines of direct and pulse current using the improvement methods of their mutual loading, including the principles of automatic regulation of mutual loading system. Methodology. The general theoretical provisions and principles of system approach to the theoretical electric engineering, the theory of electric machines and theoretical mechanics are the methodological basis of this research. The known methods of analysis of electromagnetic and electromechanical processes in electrical machines of direct and pulse current are used in the study. Methods analysis of loading modes regulation of traction electric machines was conducted using the generalized scheme of mutual loading. It is universal for all known methods to cover the losses of idling using the electric power. Findings. The general management principles of mutual loading modes of the traction electric machines of direct and pulse current by regulating their unbalanced electric magnetic moment were developed. Regulatory options of unbalanced electromagnetic moment are examined by changing the difference of the magnetic fluxes of mutually loaded electric machines, the current difference of electric machines anchors, the difference of the angular velocities of electric machines shafts. Originality. It was obtained the scientific basis development to improve the energy efficiency test methods of traction electric machines of direct and pulse current. The management principles of mutual loading modes of traction electric machines were formulated. For the first time it is introduced the concept and developed the principles of regulation of unbalanced electromagnetic moment in
Murray, David; Stankovic, Lina; Stankovic, Vladimir
2017-01-01
Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data.
Indira Nayra Paz Santos
Full Text Available Introduction Muscle activity in the aquatic environment was investigated using electromyographic analyses. The physical properties of water and the resistance used may influence the response of the muscle during exercise. The objective of this study was to evaluate the electrical activity in water and on the floor during flexion and knee extension exercises with and without load and aimed at understanding the muscular response while performing resistance exercises in water. Methods The sample consisted of 14 volunteers between 18 and 35 years old who were subjected to active exercises involving knee flexion and extension with and without load on the floor and in water. Electromyography was performed during the movement. Results A significant increase was found in the electrical activity of the rectus femoris muscle during exercises on the floor. The biceps femoris muscle showed increased electromyographic activity when resistance was used. A significant increase was found in the electrical activity of the rectus femoris muscle compared with exercises with and without load and the moment of rest in immersion. The electrical activity of the rectus and biceps femoris muscles was reduced in exercises with load and without load in a therapy pool compared with on the floor. Conclusion There was a reduction of the electromyographic activity in the aquatic environment compared with that on the ground, which could be attributed to the effects from hot water. Therefore, it is believed that resistance exercises can be performed early in a therapy pool, which will facilitate the prevention and treatment of musculoskeletal disorders.
Field data collection of miscellaneous electrical loads in Northern California: Initial results
Greenblatt, Jeffery B. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.; Pratt, Stacy [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.; Willem, Henry [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.; Claybaugh, Erin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.; Desroches, Louis-Benoit [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.; Beraki, Bereket [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.; Nagaraju, Mythri [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.; Price, Sarah K. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.; Young, Scott J. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy Technologies Division. Energy Analysis and Environmental Impacts Dept.
2013-02-25
This report describes efforts to measure energy use of miscellaneous electrical loads (MELs) in 880 San Francisco Bay Area homes during the summer of 2012. Ten regions were selected for metering: Antioch, Berkeley, Fremont, Livermore, Marin County (San Rafael, Novato, Fairfax, and Mill Valley), Oakland/Emeryville, Pleasanton, Richmond, San Leandro, and Union City. The project focused on three major categories of devices: entertainment (game consoles, set-top boxes, televisions and video players), home office (computers, monitors and network equipment), and kitchen plug-loads (coffee/espresso makers, microwave ovens/toaster ovens/toasters, rice/slow cookers and wine chillers). These categories were important to meter because they either dominated the estimated overall energy use of MELs, are rapidly changing, or there are very little energy consumption data published. A total of 1,176 energy meters and 143 other sensors were deployed, and 90% of these meters and sensors were retrieved. After data cleaning, we obtained 711 valid device energy use measurements, which were used to estimate, for a number of device subcategories, the average time spent in high power, low power and “off” modes, the average energy use in each mode, and the average overall energy use. Consistent with observations made in previous studies, we find on average that information technology (IT) devices (home entertainment and home office equipment) consume more energy (15.0 and 13.0 W, respectively) than non-IT devices (kitchen plug-loads; 4.9 W). Opportunities for energy savings were identified in almost every device category, based on the time spent in various modes and/or the power levels consumed in those modes. Future reports will analyze the collected data in detail by device category and compare results to those obtained from prior studies.
Electric Heating Property from Butyl Rubber-Loaded Boron Carbide Composites
MENG Dechuan; WANG Ninghui; LI Guofeng
2014-01-01
We researched the electric heating property from butyl rubber-loaded boron carbide composite. The effects of boron carbide content on bulk resistivity, voltage-current characteristic, thermal conductivity and thermal stability of boron carbide/butyl rubber (IIR) polymer composite were introduced. The analysis results indicated that the bulk resistivity decreased greatly with increasing boron carbide content, and when boron carbide content reached to 60%, the bulk resistivity achieved the minimum. Accordingly, electric heating behavior of the composite is strongly dependent on boron carbide content as well as applied voltage. The content of boron carbide was found to be effective in achieving high thermal conductivity in composite systems. The thermal conductivity of the composite material with added boron carbide was improved nearly 20 times than that of the pure IIR. The thermal stability test showed that, compared with pure IIR, the thermal stable time of composites was markedly extended, which indicated that the boron carbide can significantly improve the thermal stability of boron carbide/IIR composite.
Shigenaga, Y. [Daikin Industries Ltd., Osaka (Japan)
1998-08-15
Thermal storage air conditioning system is the one to use energy stored into thermal storing materials by using night electric power and to operate effective air conditioning. Therefore, as load can be treated by the stored energy, volume of the apparatus can be reduced. And, by reduction of the consumed power at day time, it can contribute to leveling of electric power demand. In general, there are two types in the thermal storage method: one is a method to store as thermal energy, and the other is that to store as chemical energy. For conditions required for the storing materials, important elements on their actual uses are not only physical properties such as large thermal storage per unit and easy thermal in- and out-puts, but also safety, long-term reliability, and easy receiving and economics containing future. The ice thermal storage air conditioning system is classified at the viewpoint of type of ice, kind of thermal storing medium, melting method on using cooling and heating, kinds of thermal medium on cooling and heating. 3 refs., 5 figs., 2 tabs.
A Power Load Distribution Algorithm to Optimize Data Center Electrical Flow
Paulo Maciel
2013-07-01
Full Text Available Energy consumption is a matter of common concern in the world today. Research demonstrates that as a consequence of the constantly evolving and expanding field of information technology, data centers are now major consumers of electrical energy. Such high electrical energy consumption emphasizes the issues of sustainability and cost. Against this background, the present paper proposes a power load distribution algorithm (PLDA to optimize energy distribution of data center power infrastructures. The PLDA, which is based on the Ford-Fulkerson algorithm, is supported by an environment called ASTRO, capable of performing the integrated evaluation of dependability, cost and sustainability. More specifically, the PLDA optimizes the flow distribution of the energy flow model (EFM. EFMs are responsible for estimating sustainability and cost issues of data center infrastructures without crossing the restrictions of the power capacity that each device can provide (power system or extract (cooling system. Additionally, a case study is presented that analyzed seven data center power architectures. Significant results were observed, achieving a reduction in power consumption of up to 15.5%.
Jon Cheetham
Full Text Available Bilateral vocal fold paralysis (BVCP is a life threatening condition and appears to be a good candidate for therapy using functional electrical stimulation (FES. Developing a working FES system has been technically difficult due to the inaccessible location and small size of the sole arytenoid abductor, the posterior cricoarytenoid (PCA muscle. A naturally-occurring disease in horses shares many functional and etiological features with BVCP. In this study, the feasibility of FES for equine vocal fold paralysis was explored by testing arytenoid abduction evoked by electrical stimulation of the PCA muscle. Rheobase and chronaxie were determined for innervated PCA muscle. We then tested the hypothesis that direct muscle stimulation can maintain airway patency during strenuous exercise in horses with induced transient conduction block of the laryngeal motor nerve. Six adult horses were instrumented with a single bipolar intra-muscular electrode in the left PCA muscle. Rheobase and chronaxie were within the normal range for innervated muscle at 0.55±0.38 v and 0.38±0.19 ms respectively. Intramuscular stimulation of the PCA muscle significantly improved arytenoid abduction at all levels of exercise intensity and there was no significant difference between the level of abduction achieved with stimulation and control values under moderate loads. The equine larynx may provide a useful model for the study of bilateral fold paralysis.
何耀耀; 许启发; 杨善林; 余本功
2013-01-01
According to the problem of short-term load forecasting in the power system, this paper proposed a probability density forecasting method using radical basis function (RBF) neural network quantile regression based on the existed researches on combination forecasting and probability interval prediction. The probability density function of load at any period in a day was evaluated. The proposed method can obtain more useful information than point prediction and interval prediction, and can implement the whole probability distribution forecasting for future load. The practical data of a city in China show that the proposed probability density forecasting method can gain more accurate result of point prediction and obtain the forecasting results of integrated probability density function of short-term load.%针对电力系统短期负荷预测问题,在现有的组合预测和概率性区间预测的基础上,提出了基于RBF神经网络分位数回归的概率密度预测方法,得出未来一天中任意时期负荷的概率密度函数,可以得到比点预测和区间预测更多的有用信息,实现了对未来负荷完整概率分布的预测.中国某市实际数据的预测结果表明,提出的概率密度预测方法不仅能得出较为精确的点预测结果,而且能够获得短期负荷完整的概率密度函数预测结果.
Dar, Zamiyad
most turbines is quite close to 1/3 and yaw angle acts as the dominant optimization variable. In the next part of this dissertation, a system comprising of a windfarm and energy storage operating in real-time electricity markets is studied. An Energy-balancing Threshold Price (ETP) policy is proposed to maximize the revenue of a windfarm with on-site storage. We propose and analyze a scheme for a windfarm to store or sell energy based on a threshold price. The threshold price is calculated based on long-term distributions of the electricity price and wind power generation processes, and is chosen so as to balance the energy flows in and out of the storage-equipped windfarm. It is also shown mathematically that the proposed policy is optimal in terms of the long-term revenue generated. Comparing it with the optimal policy that has knowledge of the future, we observe that the revenue obtained by the proposed ETP policy is approximately 90% of the maximum attainable revenue at a storage capacity of 10-15 times the power rating of the windfarm. The intermittent nature of wind power is a hindrance to the efficient participation of windfarms in the day-ahead and forward electricity markets. In this regard, a flexible forward contract is proposed in this dissertation which allows the windfarms to enter into a forward contract with flexible load with an option to deviate from the contracted amount of power. Using such a flexible contract would allow the windfarms to supply more or less than the contracted amount of power in case of unexpected wind conditions or real-time prices. We also propose models for forecasting wind power and real-time electricity prices. The comparison between the proposed contracting framework and a simple fixed contract (currently existing in the market) for different levels of flexibility and load shows that there is a net gain in windfarm revenues, if the transaction price of the two contracts are set equal. Lastly, we present and analyze
Purohit, G. P.; Leising, C. J.
1984-01-01
The power train performance of load leveled electric vehicles can be compared with that of nonload leveled systems by use of a simple mathematical model. This method of measurement involves a number of parameters including the degree of load leveling and regeneration, the flywheel mechanical to electrical energy fraction, and efficiencies of the motor, generator, flywheel, and transmission. Basic efficiency terms are defined and representative comparisons of a variety of systems are presented. Results of the study indicate that mechanical transfer of energy into and out of the flywheel is more advantageous than electrical transfer. An optimum degree of load leveling may be achieved in terms of the driving cycle, battery characteristics, mode of mechanization, and the efficiency of the components. For state of the art mechanically coupled flyheel systems, load leveling losses can be held to a reasonable 10%; electrically coupled systems can have losses that are up to six times larger. Propulsion system efficiencies for mechanically coupled flywheel systems are predicted to be approximately the 60% achieved on conventional nonload leveled systems.
Stephen Treado
2015-10-01
Full Text Available This paper investigates the energy performance of off-grid residential hybrid renewable electric power systems, particularly the effect of electric load profiles on the ability to harvest available solar energy and avoid the consumption of auxiliary energy in the form of propane. The concepts are illustrated by an analysis of the energy performance of electric and propane-fired refrigerators. Off-grid electric power systems frequently incorporate a renewable source, such as wind or solar photovoltaic (PV, with a back-up power provided by a propane fueled motor/generator. Among other design decisions, residential consumers face the choice of employing an electric refrigerator with a conventional vapor compression refrigeration system, or a fuel-fired refrigerator operating as an absorption refrigeration system. One interesting question is whether it is more advantageous from an energy perspective to use electricity to run the refrigerator, which might be provided by some combination of the PV and propane motor/generator, thereby taking advantage of the relatively higher electric refrigerator Coefficient of Performance (COP and free solar energy but having to accept a low electrical conversion efficiency of the motor/generator, or use thermal energy from the combustion of propane to produce the refrigeration effect via an absorption system, albeit with a much lower COP. The analysis is complicated by the fact that most off-grid renewable electrical power systems utilize a battery bank to provide electrical power when it is not available from the wind turbine or PV system, so the state of charge of the battery bank will have a noticeable impact on what energy source is available at any moment in time. Daily electric load profiles combined with variable solar energy input determine the state of charge of the battery bank, with the degree of synchronization between the two being a critical factor in determining performance. The annual energy usage
NONE
1996-11-01
For the electric power companies, the activity of pushing forward the load levelling contributes greatly to the curtailment of cost by means of effective formation and utilization of facilities. It is planned to take up more positive activities with a target of 3% improvement in the load factor in ten years. Concrete measures for further promotion of load levelling are the diversity of the rate menu of appraising the load levelling effort of the customers and the promotion of the spread of equipment and systems relating to load levelling such as electric water heating apparatuses and thermal energy storage air conditioning systems. The rate system has been reviewed taking the opportunity of rate revision made in January, 1996. Particularly, further load shift to midnight time zone is expected as a result of better rate incentive for customers which is brought about by large reduction of the rate level due to the cost reduction effect accompanying the improvement in atomic power generation ratio. Outlines are given on the fields relating to general households, business, dwellings, buildings, and industry. 3 figs., 1 tab.
What day-ahead reserves are needed in electric grids with high levels of wind power?
Mauch, Brandon; Apt, Jay; Carvalho, Pedro M. S.; Jaramillo, Paulina
2013-09-01
Day-ahead load and wind power forecasts provide useful information for operational decision making, but they are imperfect and forecast errors must be offset with operational reserves and balancing of (real time) energy. Procurement of these reserves is of great operational and financial importance in integrating large-scale wind power. We present a probabilistic method to determine net load forecast uncertainty for day-ahead wind and load forecasts. Our analysis uses data from two different electric grids in the US with similar levels of installed wind capacity but with large differences in wind and load forecast accuracy, due to geographic characteristics. We demonstrate that the day-ahead capacity requirements can be computed based on forecasts of wind and load. For 95% day-ahead reliability, this required capacity ranges from 2100 to 5700 MW for ERCOT, and 1900 to 4500 MW for MISO (with 10 GW of installed wind capacity), depending on the wind and load forecast values. We also show that for each MW of additional wind power capacity for ERCOT, 0.16-0.30 MW of dispatchable capacity will be used to compensate for wind uncertainty based on day-ahead forecasts. For MISO (with its more accurate forecasts), the requirement is 0.07-0.13 MW of dispatchable capacity for each MW of additional wind capacity.
Tests of an alternating current propulsion subsystem for electric vehicles on a road load simulator
Stenger, F. J.
1982-01-01
The test results of a breadboard version of an ac electric-vehicle propulsion subsystem are presented. The breadboard was installed in the NASA Lewis Research Center Road Load Simulator facility and tested under steady-state and transient conditions. Steady-state tests were run to characterize the system and component efficiencies over the complete speed-torque range within the capability of the propulsion subsystem in the motoring mode of operation. Transient tests were performed to determine the energy consumption of the breadboard over the acceleration and cruise portions of SAE J227 and driving schedules B, C, and D. Tests in the regenerative mode were limited to the low-gear-speed range of the two speed transaxle used in the subsystem. The maximum steady-state subsystem efficiency observed for the breadboard was 81.5 percent in the high-gear-speed range in the motoring mode, and 76 percent in the regenerative braking mode (low gear). The subsystem energy efficiency during the transient tests ranged from 49.2 percent for schedule B to 68.4 percent for Schedule D.
Bus Load Forecasting Model Based on Stacked Generalization%基于层叠泛化策略的母线负荷预测模型
黄帅栋; 卫志农; 丁恰; 沈茂亚; 孙国强; 孙永辉
2013-01-01
A novel method for bus load forecasting was proposed based on stacked generalization.The proposed approach includes two learning level spaces.The first one is for the original bus load data space,after the cross-validation training and testing on a set of SVMs,a new space,composing of the output of the SVMs and the corresponding original data,is obtained and named as “level 1 space”.Then,in the “level 2 space”,the original output series and corresponding output weights are taken as the observations and states of Kalman filter,respectively.Finally,simulation results demonstrate that higher generalization accuracy can be obtained by using the proposed hybrid method,thus the forecasting accuracy can be improved greatly.%基于层叠泛化策略SG (stacked generalization)提出一种新的母线负荷预测方法.该方法包含两级学习层,第1层针对原始母线负荷样本空间,对一组支持向量机SVM (support vector machine)进行交互验证式训练,训练完成后得到新的特征空间,该特征空间由这些支持向量机的输出和对应的真实值组成；第2层对输出进行线性组合,将新特征空间中的输出序列作为观测,对应的输出权值作为状态,使用卡尔曼滤波对权值进行递推估计.实例仿真证明,采用所提方法模型的泛化能力得到改善,从而提高母线负荷的预测精度.
Hong-Min Lee
2013-05-01
Full Text Available This paper presents investigations into the resonant mode behavior of a lumped-resistor-loaded electric-inductive-capacitive (ELC resonator, which is illuminated with a parallel polarization external electromagnetic wave. An ELC resonator exhibits a negative effective permittivity for both parallel and perpendicular polarizations. In contrast to a common ELC resonator, the lumped-resistor-loaded ELC resonator exhibits a switchable resonant mode behavior, thereby revealing a negative effective permeability. In addition, this resonator exhibits a low quality factor owing to the loaded lumped resistors. A metamaterial absorber, which consists of a lumped-resistor-loaded ELC resonator and a cut-wire strip, is designed to confirm the effectiveness of the resonator.
Modeling spot markets for electricity and pricing electricity derivatives
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
Roldán Blay, Carlos; Escrivá-Escrivá, Guillermo; Álvarez Bel, Carlos María; Roldán Porta, Carlos; Rodriguez-Garcia, Javier
2013-01-01
This paper presents the upgrading of a method for predicting short-term building energy consumption that was previously developed by the authors (EUs method). The upgrade uses a time temperature curve (TTC) forecast model. The EUs method involves the use of artificial neural networks (ANNs) for predicting each independent process end-uses (EUs). End-uses consume energy with a specific behaviour in function of certain external variables. The EUs method obtains the total consumption by the ad...
Fetene, Gebeyehu Manie
such as electricity, transport (con- gestion), water and telecommunication. Linear and non-linear peak load pricing alternatives have been suggested to curb this problem, particularly when demand is cyclical (Mohsenian-Rad and Leon-Garcia, 2010; Tan and Varaiya, 1993; Chao et al., 1986; Finsinger; Roberts, 1979...... of electricity. The electric vehicle (EV) users choice of time of charging problem under PLP is different from that of general households using energy for house appliances since there is uncertain cost to the former as- sociated with likelihood occurrence of unanticipated trips such as visiting hospital...... and commuting to lately informed social events, etc. In this paper, we consider EV user’s choice of time of charging problem when there is PLP of electricity used for charging the battery of EVs. Specifically, this paper aims to present a model of optimal time of charging when EV users have to trade-of between...
嵇灵; 牛东晓; 吴焕苗
2012-01-01
To overcome the pseudo-regression in neural network, the standard echo state network is improved and generalization ability of standard echo state network is enhanced by Bayesian framework. To verify the availability and adaptability of the proposed method, in the analysis on empirical example actual load data and related climatic data of a certain region are used as input variables to forecast the daily peak load of this region. Comparison of the forecasted daily load curve with actual daily load curve shows that the forecasted load curve by improved echo state network is more accurate than those forecasted by BP neural network and standard echo state network, and the generalization ability of the improved echo state network is more stronger.%为克服神经网络中的伪回归问题,对标准的回声状态网络进行改进,用贝叶斯理论提高网络的泛化能力.在实证算例分析中,采用某地区的实际负荷数据和相关气候数据,对该地区的日最大负荷进行预测,验证所提方法的有效性和适用性.对比试验的预测结果表明,改进的回声状态网络比标准回声状态网络和前馈神经网络预测效果更精确,网络泛化能力更强.
Hosseini-Bioki, M. M.; Rashidinejad, M.; Abdollahi, A.
2013-11-01
Load shedding is a crucial issue in power systems especially under restructured electricity environment. Market-driven load shedding in reregulated power systems associated with security as well as reliability is investigated in this paper. A technoeconomic multi-objective function is introduced to reveal an optimal load shedding scheme considering maximum social welfare. The proposed optimization problem includes maximum GENCOs and loads' profits as well as maximum loadability limit under normal and contingency conditions. Particle swarm optimization (PSO) as a heuristic optimization technique, is utilized to find an optimal load shedding scheme. In a market-driven structure, generators offer their bidding blocks while the dispatchable loads will bid their price-responsive demands. An independent system operator (ISO) derives a market clearing price (MCP) while rescheduling the amount of generating power in both pre-contingency and post-contingency conditions. The proposed methodology is developed on a 3-bus system and then is applied to a modified IEEE 30-bus test system. The obtained results show the effectiveness of the proposed methodology in implementing the optimal load shedding satisfying social welfare by maintaining voltage stability margin (VSM) through technoeconomic analyses.
NONE
1996-07-01
Measures for leveling the electric loads relying upon two facets - tariff system and business activities - in Chubu Electric Power Co., Inc. are presented. Firstly, the existing pricing systems for midnight electric power and electric light according to the period of time for the household as well as those of seasonal and time zone electric power, modifiable contract with the time zone and so forth for large industry users, together with a similar contract for the owner of regenerating installation are outlined. In addition, a price reduction system is indicated in favor of a scheduled shutdown of large industry operations as a means of avoiding electric consumption during weekdays in summer and between 13h and 16h when large demands exist. Further, a selective contract system put in service in 1995 and the price reduction for ice regenerating air conditioning system are cited. As for business facet, 1995 is regarded as the first year of the ice regenerator for its generalization through exhibition and promotive campaign to the foods processing industry, and the application of the latent heat regenerative system to perishable foods is recommended. Moreover, installation of energy saving type automatic vending machine, so-called eco-vendor is encouraged for its merit of peak-cut and energy price lowering. Lastly, activities to extend the use of electric calorifiers and the regenerative floor heating are mentioned. 2 figs., 1 tab.
无
2009-01-01
The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.
Wang Ting
2009-01-01
@@ The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.
陈民铀; 朱博; 徐瑞林; 徐鑫
2012-01-01
As the accurate forecasting of distributed generation and short-term load is the basis of operational control and energy management of microgrid, the concept and calculation method of microgrid surplus load are proposed and the features and influencing factors of its ultra-short-term forecasting are discussed. With the consideration of historical power outputs,loads and weather data,an ultra-short-term forecasting model of microgrid surplus load is developed, which integrates k-means clustering analysis, genetic algorithm and artificial neural network. A simulation model of microgrid with wind farms,micro-turbines and fuel cells is established and results show that,the forecasting outputs of distributed generators and the forecasting load of microgrid are very close to the measured data,which verifies the accuracy of the proposed forecasting model.%对微电网中分布式电源发电量和短期负荷的准确预报是微电网运行控制和能量管理的重要基础.提出了微电网剩余负荷的概念和计算方法,分析了微电网剩余负荷超短期预测的特点和影响因素.在考虑微电源历史输出功率、微电网历史负荷以及本地气象因素的同时,综合运用k均值聚类分析、遗传算法和人工神经网络建立了微电网剩余负荷超短期预测模型.搭建了一个含有风电、燃气轮机和燃料电池的微电网仿真模型,仿真结果表明,模型中分布式电源发电量和微电网负荷的预测结果与实测数据非常吻合,验证了模型的预测精度.
Method to Estimate Long-term Change of Heat and Electric Power Daily Load Curves in Japan
Oda, Takuya; Akisawa, Atushi; Kashiwagi, Takao
The rapid spread of CHP systems will put pressure on the regional power system to requiring an examination of the power and heat output of CHP systems. When considering the country-wide potential of the CHP system one should examine such system in coordination with the grid power system. It is essential to calculate the heat and power demand at end-use level. In the paper, annual heat and power demands of end-use sectors are forecast to the year 2025 based on 20 year data. Regression analysis is used. Estimated annual demands are divided into the seasonal hourly demands considering demand characteristics. Daily load curves of heat and power demands are determined for the Japanese end-use sectors, and the annual changes of such demands are shown by duration curves of heat to power ratios. Moreover, the grid power daily load curves are computed numerically from the estimated heat and power demands at manufacturing, residential and commercial sectors. Such load curves also consider self-generated power at manufacturing industry and own consumption of the grid power. Estimating heat and power demands allow for a joint analysis between the power system and the future phasing in of CHP systems.
麦琪
2016-01-01
准确预测用户电量需求对于市场竞争环境下的电网公司、工商业、居民用户来说具有重要意义。简单综述了国内外电量预测理论，包括灰色理论、人工神经网络理论等，阐述了GM（1，1）模型和BP模型预测电量需求的原理。详细介绍了电力市场电量需求预测的几种实际经常使用的方法，包括经济模型法、综合分析法、分析预测法及其他方法等。最后，以电力弹性系数法为例，基于广州市2000年~2008年的市用电量历史数据，对其2009年用电量需求进行了预测分析。同时，简要给出了提高电量预测准确率的一些措施，建议将近年来发展的机器学习算法等运用到电量需求预测中，对于“电网-用户-售电商-负荷集成商”等多主体的用电供需友好互动将具有重要的指导和参考意义。%It’s of great significance to accurately predict users’ power demand for the grid corporations, the industry and commerce enterprises and the inhabitant users in market competition environment. A simple review was made on home and abroad power forecasting theory, including grey theory, artificial neural network theory, etc. expounded the power forecasting principle of GM(1, 1) model and BP model. Several actual and regular used methods of electricity market power demand prediction were introduced, including the economic model method, the comprehensive analytical method, the predication parsing method, and other methods. Finally, set the electricity elasticity coefficient method as an example and based on the historical city electricity consumption data of Guangzhou from year of 2000 to 2008, its power demand in 2009 was forecasted. Meanwhile, some measures to improve power prediction accuracy were given, and suggested that the newly developed machine learning algorithms apply in power demand prediction, which will have provide certain guidance and reference for multi-agents supply and
Mora Cordova, Angel
2014-06-11
We present unified predictions for the crack onset strain, evolution of crack density, and changes in electrical resistance in indium tin oxide/polymer thin films under tensile loading. We propose a damage mechanics model to quantify and predict such changes as an alternative to fracture mechanics formulations. Our predictions are obtained by assuming that there are no flaws at the onset of loading as opposed to the assumptions of fracture mechanics approaches. We calibrate the crack onset strain and the damage model based on experimental data reported in the literature. We predict crack density and changes in electrical resistance as a function of the damage induced in the films. We implement our model in the commercial finite element software ABAQUS using a user subroutine UMAT. We obtain fair to good agreement with experiments. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
Electricity Price Forecasting Using Wavelet Neural Networks Optimized by GA%小波神经网络预测电价的新改进
涂启玉; 张茂林
2011-01-01
预测市场边际电价对于电力市场的参与者有十分重要的意义.该文首先分析了BP神经网络在电价预测方面的优劣势,然后基于小波分析,即用母小波取代Sigmoid函数建立了小波神经网络的电价预测模型,并用遗传算法优化神经网络的拓扑结构和各权重系数,从而避免BP神经网络的预测电价陷入局部极小值.实际计算表明,改进后的预测模型有效地提高了预测精度.%Forecasting the future electricity price is very important for every participators in the power market.This paper analyses the advantages and disadvantages of BP neural networks, then provides a wavelet-improved neural network model base on wavelet analysis and the topology structure and weight coefficient optimized by GA. Application to the real system shows that this model can improve the forecasting precision and avoid the limitation of the BP neural networks.
Shunxi Li
2017-01-01
Full Text Available The potential demand of battery electric vehicle (BEV is the base of the decision-making to the government policy formulation, enterprise manufacture capacity expansion, and charging infrastructure construction. How to predict the future amount of BEV accurately is very important to the development of BEV both in practice and in theory. The present paper tries to compare the short-term accuracy of a proposed modified Bass model and Lotka-Volterra (LV model, by taking China’s BEV development as the case study. Using the statistics data of China’s BEV amount of 21 months from Jan 2015 to Sep 2016, we compare the simulation accuracy based on the value of mean absolute percentage error (MAPE and discuss the forecasting capacity of the two models according to China’s government expectation. According to the MAPE value, the two models have good prediction accuracy, but the Bass model is more accurate than LV model. Bass model has only one dimension and focuses on the diffusion trend, while LV model has two dimensions and mainly describes the relationship and competing process between the two populations. In future research, the forecasting advantages of Bass model and LV model should be combined to get more accurate predicting effect.
TOMA R.
2016-09-01
Full Text Available The paper presents a comparative study between the effects on voltage stability of the integration of a wind farm into the electricity grid with or without voltage dependent loads in the context of different locations of a synchronous compensator from the grid. The P-V curves are built by using the PowerFactory DigSilent 15.2.2 and a DPL script that implements a simplified form of the Continuation Power Flow method.
2016-12-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS ANALYSIS OF THE PERFORMANCE OF AN OPTIMIZATION MODEL FOR TIME-SHIFTABLE ELECTRICAL LOAD...REPORT TYPE AND DATES COVERED Master’s Thesis 01-04-2016 to 09-23-2016 4. TITLE AND SUBTITLE ANALYSIS OF THE PERFORMANCE OF AN OPTIMIZATIONMODEL FOR...INTENTIONALLY LEFT BLANK ii Approved for public release. Distribution is unlimited. ANALYSIS OF THE PERFORMANCE OF AN OPTIMIZATION MODEL FOR TIME-SHIFTABLE
Application of Power Sales Analysis & Load Forecast System in Henan Province%电力营销分析与预测系统在河南的应用
毛大澎; 李文启; 杨保灿
2002-01-01
The paper overall analyzes traits of Power Sales Analysis & Load Forecast System (TH-PSLF) developed byTsinghua University. Also through its practical application, it deeply studies the effect of its application in Henan ProvincialPower Company and 18 local power companies. As a mightily theoretical practical system, the software largely improves ourwork efficiency and supplies powerful supporting implement for better power sales.
DURSUN, B.
2014-02-01
Full Text Available In this study, the electricity load demand, between 2012 and 2021, has been estimated using the load demand of the electricity generated from hydroelectric power plants in Turkey between 1970 and 2011. Among machine learning algorithms, Multilayer Perceptron, Locally Weighted Learning, Additive Regression, M5Rules and ZeroR classifiers are used to estimate the electricity load demand. Among them, M5Rules and Multilayer Perceptron classifiers are observed to have better performance than the others. ZeroR classifier is a kind of majority classifier used to compare the performances of other classifiers. Locally Weighted Learning and Additive Regression classifiers are Meta classifiers. In the training period conducted by Locally Weighted Learning and Additive Regression classifiers, when Multilayer Perceptron and M5Rules classifiers are chosen respectively, it is possible to obtain models with the highest performance. As a result of the experiments performed using M5Rules and Multilayer Perceptron classifiers, correlation coefficient values of 0.948 and 0.9933 are obtained respectively. And, Mean Absolute Error and Root Mean Squared Error value of Multilayer Perceptron classifier are closer to zero than that of M5Rules classifier. Therefore, it can be said the model performed by Multilayer Perceptron classifier has the best performance compared to the models of other classifiers.
Makhrojan, Agus; Suprihadi, Agus; Budi, Sigit Setijo; Jamari, J.; Ismail, Rifky
2017-01-01
The electric car is transportation which growing and constantly put through improvisation vehicle design. One of the structural components of the electric car which holds a major role is a frame. The purpose of this study is to get monocoque frame design which lightweight and powerful for a city car with two passengers that was able to improve the efficiency of the battery voltage source. Monocoque frame should be able to accept the normal loads such as the weight of batteries, passenger, and body. The most important thing, monocoque frame should also be able to protect the driver and passengers in the event of a collision. Mild steel was chosen for the design because it is easy to obtain and reasonable price as well as easy to shaped for two-seater electric car. FEM (finite element method) was used to determine stress determination and rigidity of the monocoque frame when receiving a static load. The results show that the monocoque frame was still able to withstand the required loads with minimal deflection.
Ninagawa, Takako; Kawamura, Yukio; Konishi, Tadashi; Narumi, Akira
2016-08-01
Cryopreservation techniques are expected to evolve further to preserve biomaterials and foods in a fresh state for extended periods of time. Long-term cryopreservation of living materials such as food and biological tissue is generally achieved by freezing; thus, intracellular freezing occurs. Intracellular freezing injures the cells and leads to cell death. Therefore, a dream cryopreservation technique would preserve the living materials without internal ice crystal formation at a temperature low enough to prevent bacterial activity. This study was performed to investigate the effect of micro electrical current loading during cooling as a new cryopreservation technique. The behavior of intracellular ice crystal formation in plant tissues with or without an electric current load was evaluated using the degree of supercooling, degree of cell deformation, and grain size and growing rate of intracellular ice crystal. Moreover, the transition of intracellular pH during plant tissue cooling with or without electric current loading was also examined using the fluorescence intensity ratio to comprehend cell activity at lower temperatures. The results indicated that micro electric current load did not only decrease the degree of cell deformation and grain size of intracellular ice crystal but also reduced the decline in intracellular pH due to temperature lowering, compared with tissues subjected to the same cooling rate without an electric current load. Thus, the effect of electric current load on cryopreservation and the potential of a new cryopreservation technique using electric current load were discussed based on these results.
Aguiar Santos, Susana; Schlebusch, Thomas; Leonhardt, Steffen
2013-01-01
An accurate current source is one of the keys in the hardware of Electrical impedance Tomography systems. Limitations appear mainly at higher frequencies and for non-simple resistive loads. In this paper, we simulate an improved Howland current source with a Cole-Cole load. Simulations comparing two different op-amps (THS4021 and OPA843) were performed at 1 kHz to 1 MHz. Results show that the THS4021 performed better than the OPA843. The current source with THS4021 reaches an output impedance of 20 MΩ at 1 kHz and above 320 kΩ at 1 MHz, it provides a constant and stable output current up to 4 mA, in the complete range of frequencies, and for Cole-Cole (resistive and capacitive) load.
Berkey, D.M.; Balaban, H.S.
1982-10-01
This report examines the relationship between design complexity and reliability and availability performance for fossil-fueled electric-power-generating units. Multivariate regression analysis was applied to design complexity and reliability and availability performance data gathered from a representative sample of electric-power-generating units. Twelve predictive relationships or equations were developed as a result of employing this statistical procedure. Each equation was verified and assessed. Guidelines for applying the predictive relationships, including confidence limits, were also developed and are presented in this report. A major result of this examination is a quantitative predictive tool that should be useful to the electric-power industry.
Silva, Filipe Miguel Faria da; Bak, Claus Leth; Davidsen, Troels
2014-01-01
The intermittence of some renewable energy sources, mainly wind and solar, leads to rapid power variations, which have to be controlled in order to maintain the system stable. Controllable loads, like MW range electric boilers, are a good solution for this problem, as the warmed water can be reused...... and trigger the ground fault protection. This paper uses field measurements made in a boiler installed in a power plant for the analysis of this issue. The measurements are used to design a simulation model of the electric arc, which is used to study different energisation scenarios: With/without isolating......, minimising energy losses. The boilers under analysis in this paper operate by increasing/decreasing the water level. Electric arcs appear between the electrodes and the water surface for a period of approximately 2s during the energisation. These arcs can be seen as faults to ground by the protections relays...
Weller, G.H.
2001-07-15
Utility load management programs--including direct load control and interruptible load programs--were employed by utilities in the past as system reliability resources. With electricity industry restructuring, the context for these programs has changed; the market that was once controlled by vertically integrated utilities has become competitive, raising the question: can existing load management programs be modified so that they can effectively participate in competitive energy markets? In the short run, modified and/or improved operation of load management programs may be the most effective form of demand-side response available to the electricity system today. However, in light of recent technological advances in metering, communication, and load control, utility load management programs must be carefully reviewed in order to determine appropriate investments to support this transition. This report investigates the feasibility of and options for modifying an existing utility load management system so that it might provide reliability services (i.e. ancillary services) in the competitive markets that have resulted from electricity industry restructuring. The report is a case study of Southern California Edison's (SCE) load management programs. SCE was chosen because it operates one of the largest load management programs in the country and it operates them within a competitive wholesale electricity market. The report describes a wide range of existing and soon-to-be-available communication, control, and metering technologies that could be used to facilitate the evolution of SCE's load management programs and systems to provision of reliability services. The fundamental finding of this report is that, with modifications, SCE's load management infrastructure could be transitioned to provide critical ancillary services in competitive electricity markets, employing currently or soon-to-be available load control technologies.
Mechano-electric Effect of Hardened Cement Paste During Quasi-static Loading
无
2001-01-01
Mechano-electric effect of cement paste was investigated in this paper. As compressive stress was applied on the specimen, an electrical current was observed. The intensity of the electrical current increased with stress increasing, and decreased with stress decreasing. Different measurement methods were also discussed in this paper. This phenomenon was related to the electrokinetic phenomenon of solid/liquid interface in cement paste. The study on mechano-electric effect of hardened cement paste provides a new method for making smart concrete structures.
Marnay, Chris; Hamachi, Kristina S.; Khavkin, Mark; Siddiqui, Afzal S.
2001-04-01
California's restructured electricity markets opened on 1 April 1998. The former investor-owned utilities were functionally divided into generation, transmission, and distribution activities, all of their gas-fired generating capacity was divested, and the retail market was opened to competition. To ensure that small customers shared in the expected benefit of lower prices, the enabling legislation mandated a 10% rate cut for all customers, which was implemented in a simplistic way that fossilized 1996 tariff structures. Rising fuel and environmental compliance costs, together with a reduced ability to import electricity, numerous plant outages, and exercise of market power by generators drove up wholesale electricity prices steeply in 2000, while retail tariffs remained unchanged. One of the distribution/supply companies entered bankruptcy in April 2001, and another was insolvent. During this period, two sets of interruptible load programs were in place, longstanding ones organized as special tariffs by the distribution/supply companies and hastily established ones run directly by the California Independent System Operator (CAISO). The distribution/supply company programs were effective at reducing load during the summer of 2000, but because of the high frequency of outages required by a system on the brink of failure, customer response declined and many left the tariff. The CAISO programs failed to attract enough participation to make a significant difference to the California supply demand imbalance. The poor performance of direct load participation in California's markets reinforces the argument for accurate pricing of electricity as a stimulus to energy efficiency investment and as a constraint on market volatility.
华静; 艾莉; 程加堂
2013-01-01
In order to improve the accuracy of short-term load forecasting, combination prediction method is proposed by combining evidence theory with ant colony algorithm-neural network. According to the actual load data of Chongqing City, ant colony algorithm-neural network as single model is used to its initial forecast. Then the BP neural networks is selected to get the credibility of each model by forecasting errors and the main environmental influencing factors. And the evidence theory was applied to obtain the combination weight. So, short-term load forecast was fulfilled. Examples show that the fitting error of the method is small with high prediction accuracy and it has a certain application value.%为提高短期负荷预测的精度,引入了证据理论融合蚁群神经网络的组合预测方法,根据重庆市负荷的实际数据,采用蚁群神经网络作为单一模型对其进行初步预测,由BP神经网络对预测误差及主要外界影响因素进行分析建模,获得了每个模型的可信度,并用证据理论对可信度进行合成得到组合权值,进而实现对短期电力负荷的组合预测.结果表明,该方法拟合误差小、预测精度高,具有一定的应用价值.
NONE
2009-07-15
The road transportation sector in Switzerland accounts for 44% of the whole greenhouse gas emissions of the country (around 52 million tons of CO{sub 2} equivalent, of which around 44 million tons of CO{sub 2}). The share of private cars is 72% of the road transportation emissions. The efficiency of electric vehicles is near 40% (useful energy/primary energy) in comparison to that of fossil fuel vehicles (15-20%). The European Union (EU) market average of CO{sub 2} emissions from passenger cars was about 160 g CO{sub 2}/km in 2005 and the average EU mix of electricity production had specific emissions of 410 g CO{sub 2}/kWh in the same year. In comparison the Swiss production mix was 34 CO{sub 2}/kWh in 2005, but the relevant Swiss consumption mix was 112 g CO{sub 2}/kWh, due to imports of electricity (with around 21% of the demand covered by imports). Hence a typical electric car will produce CO{sub 2} emissions of around 80 g CO{sub 2}/km in Europe, what is already twice better (in Switzerland: 23 g CO{sub 2}/km with the present consumption mix). By 2030 it is assumed that the EU electricity production mix will diminish to 130 g CO{sub 2}/kWh, and in Switzerland the consumption mix would be around 55 g CO{sub 2}/kWh (still calculated with 21% imports and with the same Swiss production mix), resulting in emissions from electric car in Europe of less than 30 g CO{sub 2}/km, and in Switzerland less than 13 g CO{sub 2}/km (all calculations made with a specific electric demand of 18-20 kWh/100 km). In summary, electrical vehicles retain a tremendous comparative advantage with respect to internal combustion engine vehicles. If 15% of the Swiss cars (i.e. 720,000 units) would be replaced by electrical vehicles, yearly CO{sub 2} emissions would decrease by about 1.2 million tons. This figure must be compared with the international commitment of Switzerland concerning its reduction of the global greenhouse gas emissions by 20%, i.e. 10.5 million tons of CO{sub 2
IoT-based electricity load optimisation: initial results of a monitor and control system
Smith, Andrew C
2012-11-01
Full Text Available The demand for electrical energy is increasing globally. Both the average as well as the anticipated peak demand must be considered by the supplier when designing an electrical supply. We present an Internet of Things – based system, and a concept...
Econometrics 101: forecasting demystified
Crow, R.T.
1980-05-01
Forecasting by econometric modeling is described in a commonsense way which omits much of the technical jargon. A trend of continuous growth is no longer an adequate forecasting tool. Today's forecasters must consider rapid changes in price, policies, regulations, capital availability, and the cost of being wrong. A forecasting model is designed by identifying future influences on electricity purchases and quantifying their relationships to each other. A record is produced which can be evaluated and used to make corrections in the models. Residential consumption is used to illustrate how this works and to demonstrate how power consumption is also related to the purchase and use of equipment. While models can quantify behavioral relationships, they cannot account for the impacts of non-price factors because of limited data. (DCK)
NONE
1996-05-01
This paper describes efforts of load leveling in Tohoku Electric Power Co., Inc. from the viewpoint of the rate system and business development. For the approach from the rate system, various rate menus for the peak shift have been arranged for domestic and large industry customers. To create the midnight demand, spread and expansion of electric hot water service are promoted. Consequently, the contract of midnight power has reached 914,000 kW at the end of 1995, which was 774,000 kW in 1985. To spread the ice regenerative air conditioning systems, the grant of incentive to the manufacturers and special discount of electricity rate have been conducted. These systems have been introduced in the eight places of Tohoku Electric Power`s business. The spread incident system has been created to introduce the automatic vending machines for beverages with the power peak cut function. Unused energy, such as exhaust heat from the non-treated sewage water and substations has been utilized as a heat supply project for city redevelopment. Multi-functional heat pump with regenerative function has been developed. This system aims at the peak shift by combining multi-functional heat pump and electric hot water service. 2 figs., 2 tabs.
1981-01-01
indicated that forecasting experience has little relationship to forecasting performance. In the latter three studies, neophyte forecasters became... Europe . Within a few months after a new commander was assigned, this unit’s performance rose to first place in the theater and remained there
B. M. Khroustalev
2010-01-01
Full Text Available The paper considers a possibility to use co-generated complexes having heat technological industrial load for operation in accordance with the requirements of irregularity of electric power generation schedule.
赵敏; 尤冬梅
2015-01-01
To meet the requirement of the load forecasting efficiency and accuracy introduced by the construction and development of microgrid, according to the characteristics of microgrid load: small base load, high intermittent and big randomness, etc., a microgrid short-term load forecasting model based on Elman neural network optimized by fruit fly optimization algorithm (FOA) is proposed. Considering that the microgrid load is influenced by meteorological factors accumulative effect, the human body amenity index is introduced to reduce the input vector dimensions. To overcome the defects of conventional learning algorithm such as slow convergence speed, local optimal solution and complex programming, the fruit fly optimization algorithm possessing global optimization performance is utilized to the optimization for the structure, weights and threshold of Elman neural network. And taking a domestic microgrid trial project for example, the FOA_Elman neural network is used for microgrid short-term load forecasting. The simulation results show that the proposed forecasting model provides greater application value and is superior to the conventional Elman neural network model.%为适应微网的建设和发展对其负荷预测效率及精度的要求,针对微网负荷基数小、间歇性、随机性大等特点,提出一种基于果蝇优化算法(fruit fly optimization algorithm,FOA)优化Elman神经网络的微网短期负荷预测模型.考虑到微网负荷受气象因素累计效应的影响,引入人体舒适度指数以降低输入向量维数.为克服常规学习算法收敛速度慢、易陷入局部最优解、编程复杂等缺陷,利用具有全局寻优性能的FOA对Elman神经网络的结构、权值和阈值进行优化,并以国内某微网示范工程项目为例,将FOA_Elman神经网络用于微网短期负荷预测.仿真结果表明,所提出的预测模型优于常规Elman神经网络模型,更具应用价值.
Short-term load forecasting based on BA-SVM with fuzzy combined weight%基于模糊组合权重的BA-SVM短期负荷预测
沈渊彬; 刘庆珍; 李友军; 苏申
2015-01-01
针对支持向量机（ SVM）内部参数优化和输入量大、时间长效率低和相似日选取的问题，提出一种模糊组合权重下相似日选取的蝙蝠算法（ BA）优化的支持向量机（ SVM）短期负荷预测模型。相似日的选取上主要利用熵权法和加权欧氏距离的k-均值算法对影响负荷变化的因素、负荷各时刻的变化特性进行区别对待，求取二者在相似日下集合的交集，从而得到与待预测日相似度高的相似日。同时，利用BA优化后的SVM进行负荷预测，提高内部参数的选取精度和效率。将该模型与常用的PSO-SVM、GA-SVM进行比较，证明了该模型能有效提高预测精度和计算效率。%In view of the defects in the load forecasting based on support vector machine( SVM),such as high dimension of input data,internal parameters optimization and the problem of selecting similar days,an fuzzy com-bined weigh load forecasting method of BA-SVM for similar days is proposed. During the choosing of similar days, factors influencing the load change and characteristics of each moment was considered based on the entropy weight method,the k-means algorithm of weighting Euclidean distance and obtain both sets. Then,the intersection in the both sets were calculate and the final set of similar days were get. At the same time,Bat Algorithm was used to opti-mize SVM and improved the internal parameter selecting efficiency. Applying this method to short-term load forecas-ting and comparing the forecasting results with GA-SVM and PSO-SVM,it was proved that the forecasting accu-racy was evidently improved.
电动负载模拟系统仿真研究%Electric load simulation system simulation research
陈家新; 张筑亚; 杨达勇
2016-01-01
电动负载模拟器是一种能精确控制输出转矩的系统，是科学试验、工业生产中用来模拟机械负载的重要设备之一。论文分析了以永磁同步电机为加载电机的负载模拟器的工作原理，推导、构建了系统的数学模型。采用经典PID算法对转矩、电流进行闭环控制，并引入前馈补偿，抑制由被加载对象主动运动引起的多余力矩，实现系统的快响应、高精度控制。最后借助MATLAB/Simulink验证系统可行性，参照负载模拟器的评价指标体系讨论系统性能。%Electric load simulator is a precise control of the output torque of the system, is a scientific experiment, industrial production equipment used to simulate one of the important mechanical load. This paper analyzes the permanent magnet synchronous motor is loaded with the motor load simulator works derived construct a mathematical model of the system. Classic PID algorithm torque, current loop control, and before the introduction of feed-forward compensation, inhibit the excess torque load objects from being active movement caused by the fast response of the system to achieve high-precision control. Finally With MATLAB/Simulink to verify the feasibility of the system, the reference load simulator evaluation system discussion system performance.
Hartmann, Frank
2013-10-15
If one would like to get the heating load of a building by using peripheral generated electrical energy from photovoltaics or small wind power, one must deal with both the specific building, as well as the heating load, the heating temperature limit and the differentiation of specific heating period for the building. Here, a ground source heat pump with an intelligent energy storage system seems to be the first choice. [German] Moechte man mit dezentral erzeugter elektrischer Energie aus Photovoltaik oder Kleinst-Windkraft die Heizlast eines Gebaeudes besorgen, muss man sich sowohl mit dem spezifischen Gebaeude, als auch mit der Heizlast, der Heizgrenztemperatur und der Differenzierung der spezifischen Heizperiode fuer das Gebaeude auseinandersetzen. Dabei scheint eine erdgekoppelte Waermepumpe mit einem intelligenten Speichersystem die erste Wahl.
Jun Yang
2015-03-01
Full Text Available In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G technique, electric vehicles (EVs can act as mobile energy storage units, which could be a solution for load frequency control (LFC in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.
Sun, Bo; Dragicevic, Tomislav; Andrade, Fabio
2015-01-01
Electrical vehicle (EV) chargers are going to occupy a considerable portion of total energy consumption in the future smart grid. Fast charging stations (FCS), as the most demanding representatives of charging infrastructure, will be requested to provide some ancillary services to the power system...... in order to support basic electrical operation. This paper proposes a local implementation of a hysteresis-based aggregation algorithm for coordinated control of multiple stations that can provide functions such as peak shaving, spinning reserves, frequency control, regulation and load following. Local...... stability. Finally, corresponding hardware in the loop results based on dSPACE1006 platform have been reported in order to verify the validity of proposed approach....
Vânia Emerich Bucco de Campos
2010-11-01
Full Text Available Vânia Emerich Bucco de Campos1, Cesar Augusto Antunes Teixeira1, Venicio Feo da Veiga2, Eduardo Ricci Júnior1, Carla Holandino11Departamento de Medicamentos, Faculdade de Farmácia, 2Instituto de Microbiologia Professor Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, BrazilAbstract: Inhibition of tumor growth induced by treatment with direct electric current (DC has been reported in several models. One of the mechanisms responsible for the antitumoral activity of DC is the generation of oxidative species, known as chloramines. With the aim of increasing chloramine production in the electrolytic medium and optimizing the antitumoral effects of DC, poly(e-caprolactone (PCL nanoparticles (NPs loaded with the amino acid tyrosine were obtained. The physical–chemical characterization showed that the NPs presented size in nanometric range and monomodal distribution. A slightly negative electrokinetic potential was also found in both blank NPs and L-tyrosine-loaded PCL NPs. The yield of the loading process was approximately 50%. Within 3 h of dissolution assay, a burst release of about 80% L-tyrosine was obtained. The in vitro cytotoxicity of DC was significantly increased when associated with L-tyrosine-loaded NPs, using a murine multidrug-resistant melanoma cell line model. This study showed that the use of the combination of nanotechnology and DC has a promising antineoplastic potential and opens a new perspective in cancer therapy.Keywords: direct electric current, nanotechnology, cancer therapy, L-tyrosine, B16F10 cells
Hochgraf, C.G.; Ryan, M.J.; Wiegman, H.L. [Univ. of Wisconsin, Madison, WI (United States)
1996-09-01
This paper identifies important engine, alternator and battery characteristics needed for determining an appropriate engine control strategy for a series hybrid electric vehicle. Examination of these characteristics indicates that a load-leveling strategy applied to the small engine will provide better fuel economy than a power-tracking scheme. An automatic energy management strategy is devised whereby a computer controller determines the engine-alternator turn-on and turn-off conditions and controls the engine-alternator autonomously. Battery state of charge is determined from battery voltage and current measurements. Experimental results of the system`s performance in a test vehicle during city driving are presented.
NONE
1996-09-01
This paper presents an electric hot-water heater and ice storage air conditioning system as systems to be recommended for load leveling. Electric hot-water heater is featured by safety, cleanliness, silence and convenience because of no use of fire. Its electricity charge is only 7.15 yen/kWh less than 1/3 of that for ordinary homes because of use of midnight power. Mainly used MPU-control type electric hot-water heater is more economical because of a 15% discount system. Ice storage air conditioning system is operated in the daytime using ice made by midnight power. It is featured by reduction of facility and installation costs due to the small capacity of heat source equipment, use of inexpensive midnight power, and reduction of running cost due to small contract demand. However, since an ice storage air conditioning system is in the initial stage of diffusion, its initial cost is expensive as compared with conventional non-heat storage air conditioning systems, remaining the issue of cost reduction. 3 figs., 1 tab.
Paetz, Alexandra-Gwyn; Kaschub, Thomas; Kopp, Martin; Jochem, Patrick; Fichtner, Wolf [Karlsruher Institut fuer Technologie, Karlsruhe (Germany). Inst. fuer Industriebetriebslehre und Industrielle Produktion
2013-03-15
Electric mobility is supposed to contribute to climate policy targets by reducing CO{sub 2}-emissions in the transportation sector. Increasing penetration rates of electric vehicles (EV) can lead to new challenges in the electricity sector, especially with regard to local distribution networks. Thus the management of charging loads is discussed as a key issue in energy economics. Due to their long parking times, high electricity and power demand, EV seem to be predestined for load management. Monetary incentives as dynamic pricing can be suitable for that: They reflect the current supply situation, pass the information to the consumers and can thus lead to a corresponding charging behaviour. In this article we analyse this interaction between dynamic pricing and charging loads. For this reason we have developed the optimization model DS-Opt+. It models a total number of 4,000 households in two residential areas of a major city with regard to its electricity demand, its mobility behaviour and its equipment of photovoltaic systems. Four different pricing models are tested for their effects on charging behaviour and thus the total load of the residential area. The results illustrate that only fairly high penetration rates of EV lead to remarkably higher electricity demand and require some load management. The tested dynamic pricing models are suitable for influencing charging loads; load-based tariffs are best in achieving a balanced load curve. In our analysis uncontrolled charging strategies are superior regarding a balanced load curve than controlled strategies by time-varying tariffs. Our results lead to several implications relevant for the energy industry and further research.
Zivi, S. M.; Pollack, I.; Kacinskas, H.; Chilenskas, A. A.; Barney, D. L.; Sudar, S.; Goldstein, I.; Grieve, W.
1979-01-01
The design of a lithium/iron sulfide battery for utility load leveling is strongly dependent on the energy capacity selected for the cells. Battery hardware costs are minimized by the selection of large cells, with 30-kWh cells being the largest that would be consistent with system constraints in a 100-MWh load leveling plant. However, it is anticipated that such large cells may be precluded by system reliability and maintainability considerations, and cell capacities on the order of 1 kWh may be needed to satisfy those requirements. Large cells can be protected against overcharge by electronically controlled charge equalization systems that have been developed for experimental eV batteries. The economics of electronically controlled equalization becomes unfavorable for small load-leveling cells; and if small cells are selected, it will be necessary to develop inherent protective means within each cell, with respect to overcharge.
于蕾
2014-01-01
Based on the population projections,Anhui,Anhui amount of waste products recycling electrical and electronic products and electrical and electronic waste produced in Anhui Province and processing capabilities forecast demand forecast study electrical and electronic products recycling outlets Anhui abandoned building demand forecasting,order Anhui Province to establish waste electrical and electronic recycling centers to reference.%文中通过对安徽省人口预测、安徽省废弃电子电器产品的产品回收量和安徽省废弃电子电器产生量预测及处理能力需求预测，探讨了安徽省废弃电子电器产品回收网点建设需求预测，以期对安徽省废旧电子电器回收处理中心的建立起到参考作用。
Ultra-Short-Term Wind Power Load Forecast Based on Least Squares SVM%基于最小二乘支持向量机的超短期风电负荷预测
崔杨; 李莉; 陈德荣
2014-01-01
Strong intermittency and fluctuation of wind leads to difficulties in wind power load forecast,such as slow forecast and calculation, short predictable future and low prediction accuracy.To overcome these difficulties,the least squares support vector machine (LS-SVM)method is used in the ultra-short-term wind power load forecast.The improved LS-SVMcalculation simplifies the computational complexity,raises computation speed remarkably,and improves prediction accuracy.Results of simulation made with actual data show that the method based on LS-SVM can further improve prediction accuracy of ultra-short-term wind power forecast and raise calculation and prediction speed,showing advantages both in prediction accuracy and calculation speed as compared with other methods.This method is feasible and effective when used for ultra-short-term wind power load forecast.%风力具有很强的间歇性和波动性，导致风电负荷预测困难，主要表现在预测计算速度慢，可预测的未来时间短，预测精度不高。为了解决这些预测困难，将最小二乘支持向量机（LS-SVM）的方法运用在超短期风电负荷预测中。最小二乘支持向量机通过改进算法，简化了计算的复杂性，使计算速度明显增快，也进一步提高了预测的精度。用实际数据进行仿真，实验结果表明，基于LS-SVM的方法可以进一步提高超短期风电负荷预测的精度，加快计算和预测的速度，与其他方法相比预测精度和运算速度都有优势，用于超短期风电负荷预测是有效可行的。
Leuning, N.; Steentjes, S.; Schulte, M.; Bleck, W.; Hameyer, K.
2016-11-01
The magnetic properties of non-grain-oriented (NGO) electrical steels are highly susceptible to mechanical stresses, i.e., residual, external or thermal ones. For rotating electrical machines, mechanical stresses are inevitable and originate from different sources, e.g., material processing, machine manufacturing and operating conditions. The efficiency and specific losses are largely altered by different mechanical stress states. In this paper the effect of tensile stresses and plastic deformations on the magnetic properties of a 2.9 wt% Si electrical steel are studied. Particular attention is paid to the effect of magnetic anisotropy, i.e., the influence of the direction of applied mechanical stress with respect to the rolling direction. Due to mechanical stress, the induced anisotropy has to be evaluated as it is related to the stress-dependent magnetostriction constant and the grain alignment.
Probabilistic modeling of nodal electric vehicle load due to fast charging stations
Tang, Difei; Wang, Peng; Wu, Qiuwei
2016-01-01
station into consideration. Fuzzy logic inference system is applied to simulate the charging decision of EV drivers at fast charging station. Due to increasing EV loads in power system, the potential traffic congestion in fast charging stations is modeled and evaluated by queuing theory with spatial...
Lefieux, V
2007-10-15
Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)
Matuzok N. V.
2016-02-01
Full Text Available The article presents the material of forecasting for grape yield of next year and establishing the optimal loading if cutting of bushes. The material includes 14 varieties of grapes, 11 of them are technical and 3 are table ones. For each year of stable high yield of grapes, it is necessary to pre-set the optimum length of fruit cutting of shoots and optimum load on the bush healthy eyes. To do this for each variety on the eve of trimming bushes we perform optimum productivity analysis of wintering buds of fruit along the length of shoots, i.e. we implement forecasting of grape yield for next year. We have a plan of forecasting for yields of vineyards by microscopy of wintering buds on one-year shoots of fruit ripened grapes in order to establish the potential of embryonic establishment of inflorescences in the central holes of buds. Based on the analysis of buds, the indices were calculated for wintering fruiting buds and their degree of damage during the growing season. It was revealed, that the majority of grape varieties under study shows high tab embryonic inflorescences in central buds in overwintering buds for next year yield. Higher rates at a rate of fruiting buds were wintering in the varieties: Moldova (section 27. - 1.66; Bianca (section 6. - 1.83; Kunlean (section 15. - 1.71; Merlot (section 14. - 1.64; Saperavi (section 56. - 1.76. The lowest rates of fructification - the varieties Muscat Hamburg (section 21 and Augustine (section 11 and were respectively 1.20 and 1.24. As a planned productivity, we offered the optimal loading model of cutting bushes buds. As a result of productivity analyzes of buds along the length of the fruit shoots in 2016 we recommended to carry out pruning of fruit annual shoots 3-4 buds of the form of AZOS-1 and the form of cordon - 5-6 buds