Hybrid model using three-stage algorithm for simultaneous load and price forecasting

Abstract Short-term load and price forecasting is an important issue in the optimal operation of restructured electric utilities. This paper presents a new intelligent hybrid three-stage model for simultaneous load and price forecasting. The proposed algorithm uses wavelet and Kalman machines for the first stage load and price forecasting. Each of the load and price data is decomposed into different frequency components, and Kalman machine is used to forecast each frequency components of load and price data. Then a Kohonen Self Organizing Map (SOM) finds similar days of load frequency components and feeds them into the second stage forecasting machine. In addition, mutual information based feature selection is used to find the relevant price data and rank them based on their relevance. The second stage uses Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting of load and price frequency components, respectively. The third stage machine uses the second stage outputs and feeds them into its MLP-ANN and ANFIS machines to improve the load and price forecasting accuracy. The proposed three-stage algorithm is applied to Nordpool and mainland Spain power markets. The obtained results are compared with the recent load and price forecast algorithms, and showed that the three-stage algorithm presents a better performance for day-ahead electricity market load and price forecasting.

[1]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .

[2]  Sunil Kumar Sinha,et al.  Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting , 2010, IEEE Transactions on Power Systems.

[3]  Mansour Sheikhan,et al.  Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection , 2011, Neural Computing and Applications.

[4]  Farshid Keynia,et al.  A new cascade NN based method to short-term load forecast in deregulated electricity market , 2013 .

[5]  Omar Badran,et al.  A fuzzy inference model for short-term load forecasting , 2009 .

[6]  H. Bevrani,et al.  A fuzzy inference model for short-term load forecasting , 2012, 2012 Second Iranian Conference on Renewable Energy and Distributed Generation.

[7]  V. Mendes,et al.  Short-term electricity prices forecasting in a competitive market: A neural network approach , 2007 .

[8]  Luis Neves,et al.  Assessing the relevance of load profiling information in electrical load forecasting based on neural network models , 2012 .

[9]  N. Kumarappan,et al.  Day-ahead deregulated electricity market price classification using neural network input featured by DCT , 2012 .

[10]  Ricardo Cao,et al.  Forecasting next-day electricity demand and price using nonparametric functional methods , 2012 .

[11]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[12]  N. Pindoriya,et al.  An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets , 2008, IEEE Transactions on Power Systems.

[13]  Nima Amjady,et al.  Mixed price and load forecasting of electricity markets by a new iterative prediction method , 2009 .

[14]  W. R. Christiaanse.  Short-Term Load Forecasting Using General Exponential Smoothing , 1971 .

[15]  C. García-Martos,et al.  Mixed Models for Short-Run Forecasting of Electricity Prices: Application for the Spanish Market , 2007, IEEE Transactions on Power Systems.

[16]  Gwo-Ching Liao,et al.  Hybrid Improved Differential Evolution and Wavelet Neural Network with load forecasting problem of air conditioning , 2014 .

[17]  Vitor Nazário Coelho,et al.  A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment , 2016 .

[18]  N. Amjady,et al.  Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm , 2009, IEEE Transactions on Power Systems.

[19]  Haidar Samet,et al.  A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting , 2014, Expert Syst. Appl..

[20]  Julián Moral-Carcedo,et al.  Integrating long-term economic scenarios into peak load forecasting: An application to Spain , 2017 .

[21]  Ioannis P. Panapakidis,et al.  Day-ahead electricity price forecasting via the application of artificial neural network based models , 2016 .

[22]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting , 2011, IEEE Transactions on Power Systems.

[23]  J. Ramos,et al.  Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques , 2007, IEEE Transactions on Power Systems.

[24]  Ying Chen,et al.  Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks , 2010, IEEE Transactions on Power Systems.

[25]  Mohammad Moradzadeh,et al.  A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management , 2016 .

[26]  Richard Weber,et al.  A wrapper method for feature selection using Support Vector Machines , 2009, Inf. Sci..

[27]  Zhou Quan,et al.  RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment , 2008, IEEE Transactions on Power Systems.

[28]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[29]  M. Tahar Kechadi,et al.  A Practical Approach for Electricity Load Forecasting , 2005, WEC.

[30]  Hany M. Hasanien,et al.  Short term load forecasting using ANN technique , 2017, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON).

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  Mehdi Khashei,et al.  A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting , 2017 .

[33]  N. Amjady.  Day-ahead price forecasting of electricity markets by a new fuzzy neural network , 2006, IEEE Transactions on Power Systems.

[34]  Nima Amjady,et al.  Day‐ahead price forecasting of electricity markets by a hybrid intelligent system , 2009 .

[35]  G. Irisarri,et al.  On-Line Load Forecasting for Energy Control Center Application , 1982, IEEE Transactions on Power Apparatus and Systems.

[36]  A. Selakov,et al.  Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank , 2014, Appl. Soft Comput..

[37]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[38]  H. Rajabi Mashhadi,et al.  Market clearing price and load forecasting using cooperative co-evolutionary approach , 2010 .

[39]  Zhihong Gu,et al.  A Short-Term Load Forecasting Model Based on LS-SVM Optimized by Dynamic Inertia Weight Particle Swarm Optimization Algorithm , 2009, ISNN.

[40]  Farshid Keynia.  A new feature selection algorithm and composite neural network for electricity price forecasting , 2012, Eng. Appl. Artif. Intell..

[41]  Song Li,et al.  An ensemble approach for short-term load forecasting by extreme learning machine , 2016 .

[42]  Najeh Chaâbane,et al.  A hybrid ARFIMA and neural network model for electricity price prediction , 2014 .

[43]  Martin T. Hagan,et al.  Neural network design , 1995 .

[44]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[45]  JinXing Che,et al.  Optimal training subset in a support vector regression electric load forecasting model , 2012, Appl. Soft Comput..

[46]  Gavin Brown,et al.  Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection , 2012, J. Mach. Learn. Res..

[47]  Ching-Hsue Cheng,et al.  One step-ahead ANFIS time series model for forecasting electricity loads , 2010 .

[48]  Joao P. S. Catalao,et al.  Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach , 2011 .

[49]  S. A. Soliman,et al.  Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model , 2004 .

[50]  C. Senabre,et al.  Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study , 2012 .

[51]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[52]  Alireza Khotanzad,et al.  A Neuro-Fuzzy Approach to Short-Term Load Forecasting in a Price-Sensitive Environment , 2002, IEEE Power Engineering Review.

[53]  Shuo Wang,et al.  Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory , 2017 .

[54]  Chen Wang,et al.  Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting , 2016 .

[55]  Yuan-Yih Hsu,et al.  Short term load forecasting of Taiwan power system using a knowledge-based expert system , 1990 .

[56]  Y. Shimakura,et al.  Short-term load forecasting using an artificial neural network , 1993, [1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems.

[57]  Yihui Zheng,et al.  Short-Term Load Forecasting Based on Fuzzy Clustering Analysis Similar Days , 2014 .