A comparative study of different machine learning methods for electricity prices forecasting of an electricity market

Generally, it is difficult to accurately forecast electricity prices because they are unpredictable. Yet, accurate price forecasting is expected to provide crucial information, needed by power producers and consumers to bid strategically, thereby decreasing their risks and increasing their profits in the electricity market. In this paper, two models using artificial neural networks (ANN) and support vector machines (SVM) were developed for electricity price forecasting. In addition, ant colony optimization (ACO) was used to reduce the feature space and give the best attribute subset for ANN model. Using ACO for feature selection significantly reduced the training time for ANN-based electricity price forecasting model while the results were almost as accurate as those from ANN model.

[1]  Paras Mandal,et al.  Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[2]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[3]  M. Pazoki,et al.  Investigation of quantum conductance in semiconductor single-wall carbon nanotubes: Effect of strain and impurity , 2011 .

[4]  Kit Po Wong,et al.  Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping , 2012, IEEE Transactions on Power Systems.

[5]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Fernando Villada,et al.  Electricity price forecasting using artificial neural networks , 2008 .

[7]  Desheng Dash Wu,et al.  Power load forecasting using support vector machine and ant colony optimization , 2010, Expert Syst. Appl..

[8]  Ashwani Kumar,et al.  Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market , 2010 .

[9]  S. Fan,et al.  An Integrated Machine Learning Model for Day-Ahead Electricity Price Forecasting , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[10]  T. Soares,et al.  ANN-based LMP forecasting in a distribution network with large penetration of DG , 2012, PES T&D 2012.

[11]  Ashwani Kumar,et al.  Electricity price forecasting in deregulated markets: A review and evaluation , 2009 .

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

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

[14]  Z. Dong,et al.  A Statistical Approach for Interval Forecasting of the Electricity Price , 2008, IEEE Transactions on Power Systems.

[15]  R. Faez,et al.  Magnetization of bilayer graphene with interplay between monovacancy in each layer , 2013 .