Short-term load forecasting by wavelet transform and evolutionary extreme learning machine

Abstract This paper proposes a novel short-term load forecasting (STLF) method based on wavelet transform, extreme learning machine (ELM) and modified artificial bee colony (MABC) algorithm. The wavelet transform is used to decompose the load series for capturing the complicated features at different frequencies. Each component of the load series is then separately forecasted by a hybrid model of ELM and MABC (ELM-MABC). The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM. Compared to the conventional neuro-evolution method, ELM-MABC can improve the learning accuracy with fewer iteration steps. The proposed method is tested on two datasets: ISO New England data and North American electric utility data. Numerical testing shows that the proposed method can obtain superior results as compared to other standard and state-of-the-art methods.

[1]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[3]  Rui Zhang,et al.  Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine , 2013 .

[4]  Tai Nengling,et al.  Techniques of applying wavelet transform into combined model for short-term load forecasting , 2006 .

[5]  Sung-Kwan Joo,et al.  Holiday Load Forecasting Using Fuzzy Polynomial Regression With Weather Feature Selection and Adjustment , 2012, IEEE Transactions on Power Systems.

[6]  S. Fan,et al.  Short-term load forecasting based on an adaptive hybrid method , 2006, IEEE Transactions on Power Systems.

[7]  Quan Chen,et al.  A neural network based very short term load forecaster for the interim ISO New England electricity market system , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

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

[9]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[10]  A. Kai Qin,et al.  Evolutionary extreme learning machine , 2005, Pattern Recognit..

[11]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[12]  J. W. Taylor,et al.  Short-Term Load Forecasting With Exponentially Weighted Methods , 2012, IEEE Transactions on Power Systems.

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

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

[15]  Agnaldo J. R. Reis,et al.  Feature extraction via multiresolution analysis for short-term load forecasting , 2005, IEEE Transactions on Power Systems.

[16]  David Infield,et al.  Optimal smoothing for trend removal in short term electricity demand forecasting , 1998 .

[17]  Shyh-Jier Huang,et al.  Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .

[18]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

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

[20]  Ali Deihimi,et al.  Application of echo state networks in short-term electric load forecasting , 2012 .

[21]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[22]  Farshid Keynia,et al.  Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm , 2009 .

[23]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  George W. Irwin,et al.  A hybrid linear/nonlinear training algorithm for feedforward neural networks , 1998, IEEE Trans. Neural Networks.

[25]  Saifur Rahman,et al.  Input variable selection for ANN-based short-term load forecasting , 1998 .

[26]  Henry Tabe,et al.  Wavelet Transform , 2009, Encyclopedia of Biometrics.

[27]  E. M. Anagnostakis,et al.  Short-term load forecasting based on artificial neural networks parallel implementation , 2002 .

[28]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[29]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[30]  Yuting Wang,et al.  Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering , 2013, IEEE Transactions on Power Systems.

[31]  Vladimir Ceperic,et al.  A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines , 2013, IEEE Transactions on Power Systems.

[32]  Z.A. Bashir,et al.  Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks , 2009, IEEE Transactions on Power Systems.