Hybrid evolutionary algorithms in a SVR-based electric load forecasting model

Accurately electric load forecasting has become the most important issue in energy management; however, electric load often presents nonlinear data patterns. Therefore, looking for a novel forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) reveals superior nonlinear modeling capabilities by applying the structural risk minimization principle to minimize an upper bound of the generalization errors, it is quite different with ANNs model that minimizing the training errors. The purpose of this paper is to present a SVR model with a hybrid evolutionary algorithm (chaotic genetic algorithm, CGA) to forecast the electric loads, CGA is applied to the parameter determine of SVR model. With the increase of the complexity and the larger problem scale of electric loads, genetic algorithms (GAs) are often faced with the problems of premature convergence, slowly reaching the global optimal solution or trapping into a local optimum. The proposed CGA based on the chaos optimization algorithm and GAs, which employs internal randomness of chaos iterations, is used to overcome premature local optimum in determining three parameters of a SVR model. The empirical results indicate that the SVR model with CGA (SVRCGA) results in better forecasting performance than the other methods, namely SVMG (SVM model with GAs), regression model, and ANN model.

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

[2]  Emile H. L. Aarts,et al.  Global optimization and simulated annealing , 1991, Math. Program..

[3]  Robert Grover Brown,et al.  Introduction to random signal analysis and Kalman filtering , 1983 .

[4]  R. Adapa,et al.  Risk due to load forecast uncertainty in short term power system planning , 1998 .

[5]  Guo-Li Shen,et al.  A chaotic approach to maintain the population diversity of genetic algorithm in network training , 2003, Comput. Biol. Chem..

[6]  M. Ohya.  Complexities and Their Applications to Characterization of Chaos , 1998 .

[7]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[8]  G. Gross,et al.  Short-term load forecasting , 1987, Proceedings of the IEEE.

[9]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[10]  Chao-Ming Huang,et al.  Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting , 1995 .

[11]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[12]  R. Adapa,et al.  The impacts of temperature forecast uncertainty on Bayesian load forecasting , 1998 .

[13]  Maarouf Saad,et al.  An efficient approach for short term load forecasting using artificial neural networks , 2006 .

[14]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[15]  Yanbin Yuan,et al.  A hybrid chaotic genetic algorithm for short-term hydro system scheduling , 2002, Math. Comput. Simul..

[16]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[17]  Chih-Chou Chiu,et al.  Combining a neural network with a rule-based expert system approach for short-term power load forecasting in Taiwan , 1997 .

[18]  E. Lorenz.  Deterministic nonperiodic flow , 1963 .

[19]  Lijuan Cao,et al.  Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[20]  Lijuan Cao,et al.  Dynamic support vector machines for non-stationary time series forecasting , 2002, Intell. Data Anal..

[21]  Bing Li,et al.  Optimizing Complex Functions by Chaos Search , 1998, Cybern. Syst..

[22]  Wei-Chiang Hong,et al.  Forecasting Tourism Demand Using a Multifactor Support Vector Machine Model , 2005, CIS.

[23]  Saifur Rahman,et al.  Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.

[24]  R.E. Abdel-Aal,et al.  Short-term hourly load forecasting using abductive networks , 2004, IEEE Transactions on Power Systems.

[25]  Gwo-Ching Liao.  Hybrid chaos search genetic algorithm and meta-heuristics method for short-term load forecasting , 2006 .

[26]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[27]  Ming-Wei Chang,et al.  Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[28]  D.W. Bunn,et al.  Forecasting loads and prices in competitive power markets , 2000, Proceedings of the IEEE.

[29]  Maarouf Saad,et al.  Non-parametric short-term load forecasting , 2007 .

[30]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[31]  Xuefeng F. Yan,et al.  Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model , 2003, Comput. Chem. Eng..

[32]  Robert M. May,et al.  Simple mathematical models with very complicated dynamics , 1976, Nature.

[33]  Ping-Feng Pai,et al.  Predicting engine reliability by support vector machines , 2006 .

[34]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[35]  Bojan Novak,et al.  Superfast autoconfiguring artificial neural networks and their application to power systems , 1995 .

[36]  Jae Hong Park,et al.  Composite modeling for adaptive short-term load forecasting , 1991 .

[37]  Saifur Rahman,et al.  An expert system based algorithm for short term load forecast , 1988 .

[38]  Georges A. Darbellay,et al.  Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .

[39]  M. El-Hawary,et al.  Application of least absolute value parameter estimation based on linear programming to short-term load forecasting , 1997 .

[40]  Saifur Rahman,et al.  A generalized knowledge-based short-term load-forecasting technique , 1993 .

[41]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[42]  Tharam S. Dillon,et al.  Short term load forecasting using an adaptive neural network , 1991 .

[43]  Wang Ling.  Survey on Chaotic Optimization Methods , 2001 .

[44]  Chia-Yon Chen,et al.  Regional load forecasting in Taiwanapplications of artificial neural networks , 2003 .

[45]  C.E. Asbury.  Weather load model for electric demand and energy forecasting , 1975, IEEE Transactions on Power Apparatus and Systems.

[46]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

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