Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting

Forecasting price has now become essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increasing the utility’s profit and energy efficiency as well. The main challenge in forecasting electricity price is when dealing with non-stationary and high volatile price series. Some of the factors influencing this volatility are load behavior, weather, fuel price and transaction of import and export due to long term contract. This paper proposes the use of Least Square Support Vector Machine (LSSVM) with Genetic Algorithm (GA) optimization technique to predict daily electricity prices in Ontario. The selection of input data and LSSVM’s parameter held by GA are proven to improve accuracy as well as efficiency of prediction. A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. However, future study should emphasize on improving forecast accuracy during spike event since Ontario power market is reported as among the most volatile market worldwide.

[1]  Desheng Dash Wu,et al.  A soft computing system for day-ahead electricity price forecasting , 2010, Appl. Soft Comput..

[2]  Z. Tan,et al.  Day-ahead electricity price forecasting using WT, CLSSVM and EGARCH model , 2013 .

[3]  Li Jinying,et al.  Next-day electricity price forecasting based on support vector machines and data mining technology , 2008, 2008 27th Chinese Control Conference.

[4]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[5]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[6]  Takashi Hiyama,et al.  Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference , 2011 .

[7]  Ting Wang,et al.  Application of SVM based on rough set in electricity prices forecasting , 2010, 2010 The 2nd Conference on Environmental Science and Information Application Technology.

[8]  R. A. Swief,et al.  Support vector machines (SVM) based short term electricity load-price forecasting , 2009, 2009 IEEE Bucharest PowerTech.

[9]  Xing Yan,et al.  Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach , 2013 .

[10]  Ashwani Kumar,et al.  Electricity Price Forecasting in Ontario Electricity Market Using Wavelet Transform in Artificial Neural Network Based Model , 2008 .

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

[12]  Nima Amjady,et al.  Design of input vector for day-ahead price forecasting of electricity markets , 2009, Expert Syst. Appl..

[13]  Thomas Weise,et al.  Global Optimization Algorithms -- Theory and Application , 2009 .

[14]  Ma Guangwen,et al.  Electricity Price Forecasting Based on Support Vector Machine Trained by Genetic Algorithm , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[15]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[17]  Deepa Singhal,et al.  Electricity price forecasting using artificial neural networks , 2011 .

[18]  Esin Dogantekin,et al.  A new intelligent hepatitis diagnosis system: PCA-LSSVM , 2011, Expert Syst. Appl..

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

[20]  Yi Wang,et al.  Price forecasting by ICA-SVM in the competitive electricity market , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[21]  Ping-Feng Pai,et al.  Time series forecasting by a seasonal support vector regression model , 2010, Expert Syst. Appl..

[22]  Yan Lin,et al.  Short-Term Electricity Price Forecasting Based on Rough Sets and Improved SVM , 2009, 2009 Second International Workshop on Knowledge Discovery and Data Mining.

[23]  Jinliang Zhang,et al.  Day-ahead electricity price forecasting by a new hybrid method , 2012, Comput. Ind. Eng..

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

[25]  Nitin Anand Shrivastava,et al.  Price forecasting using computational intelligence techniques: A comparative analysis , 2011, 2011 International Conference on Energy, Automation and Signal.

[26]  Tomonobu Senjyu,et al.  Neural-wavelet Approach for Short Term Price Forecasting in Deregulated Power Market , 2011 .

[27]  M.J. Mahjoob,et al.  GA based optimized LS-SVM forecasting of short term electricity price in competitive power markets , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[28]  Xing Yan,et al.  A comparison between SVM and LSSVM in mid-term electricity market clearing price forecasting , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[29]  Shu Fan,et al.  Next-day electricity-price forecasting using a hybrid network , 2007 .

[30]  Zuyi Li,et al.  Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets , 2004 .

[31]  Tomonobu Senjyu,et al.  A neural network based several-hour-ahead electric load forecasting using similar days approach , 2006 .

[32]  Dong-shan Gong,et al.  Short-Term Electricity Price Forecasting Based on Novel SVM Using Artificial Fish Swarm Algorithm under Deregulated Power , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[33]  T. Downs,et al.  Support vector machine based electricity price forecasting for electricity markets utilising projected assessment of system adequacy data , 2003 .

[34]  Sheng Li,et al.  Classification of gasoline brand and origin by Raman spectroscopy and a novel R-weighted LSSVM algorithm , 2012 .

[35]  T. Niimura,et al.  A day-ahead electricity price prediction based on a fuzzy-neuro autoregressive model in a deregulated electricity market , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[36]  Chien-Hung Chen,et al.  A forecasting system of electric price using the refined Back propagation Neural Network , 2010, 2010 International Conference on Power System Technology.

[37]  Z. Zakaria,et al.  Short term electricity price forecasting using neural network , 2013 .

[38]  IsmailShuhaida,et al.  A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting , 2011 .

[39]  P. Luh,et al.  Selecting input factors for clusters of Gaussian radial basis function networks to improve market clearing price prediction , 2003 .

[40]  Qinghai Bai,et al.  Analysis of Particle Swarm Optimization Algorithm , 2010, Comput. Inf. Sci..

[41]  Wei Sun,et al.  Application of Time Series Based SVM Model on Next-Day Electricity Price Forecasting Under Deregulated Power Market , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[42]  Wei Sun,et al.  Forecasting Day Ahead Spot Electricity Prices Based on GASVM , 2008, 2008 International Conference on Internet Computing in Science and Engineering.

[43]  H. Shayeghi,et al.  Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme , 2013 .

[44]  Tom Downs,et al.  Evaluation of support vector machine based forecasting tool in electricity price forecasting for Australian national electricity market participants , 2002 .