Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network

Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and Random Vector Functional Link network (RVFL) is presented in this paper. Due to the randomly generated weights between input and hidden layers and the close form solution for parameter tuning, RVFL network is a universal approximator with the advantages of fast training. By introducing ensemble approach via EMD into RVFL network, the performance can be significantly improved. Five electricity load demand datasets from Australian Energy Market Operator (AEMO) were used to evaluate the performance of the proposed method. The attractiveness of the proposed EMD based RVFL network can be demonstrated by the comparison with six benchmark methods.

[1]  Rahmat-Allah Hooshmand,et al.  A hybrid intelligent algorithm based short-term load forecasting approach , 2013 .

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[5]  Chao Chen,et al.  A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks , 2012 .

[6]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[7]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[8]  A. Bachelor.  GLOSSARY OF TERMS GLOSSARY OF TERMS , 2010 .

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

[10]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[11]  C. Holt.  Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  Ponnuthurai N. Suganthan,et al.  Random vector functional link network for short-term electricity load demand forecasting , 2016, Inf. Sci..

[14]  Leo Breiman,et al.  Stacked regressions , 2004, Machine Learning.

[15]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[16]  Ponnuthurai N. Suganthan,et al.  Detecting Wind Power Ramp with Random Vector Functional Link (RVFL) Network , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[17]  Ponnuthurai N. Suganthan,et al.  Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] , 2016, IEEE Computational Intelligence Magazine.

[18]  P. N. Suganthan,et al.  A comprehensive evaluation of random vector functional link networks , 2016, Inf. Sci..

[19]  Dianhui Wang,et al.  Fast decorrelated neural network ensembles with random weights , 2014, Inf. Sci..

[20]  Guoyin Wang,et al.  The approximation set of a vague set in rough approximation space , 2015, Inf. Sci..

[21]  Liu Peng.  Combined Model Based on EMD-SVM for Short-term Wind Power Prediction , 2011 .

[22]  Dejan J. Sobajic,et al.  Neural-net computing and the intelligent control of systems , 1992 .

[23]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[24]  Robert P. W. Duin,et al.  Feedforward neural networks with random weights , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[25]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[26]  Dejan J. Sobajic,et al.  Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.

[27]  R. K. Shyamasundar,et al.  Introduction to algorithms , 1996 .

[28]  Xin Yao,et al.  Short-Term Load Forecasting with Neural Network Ensembles: A Comparative Study [Application Notes] , 2011, IEEE Computational Intelligence Magazine.

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

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

[31]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[32]  Rasmus Berg Palm,et al.  Prediction as a candidate for learning deep hierarchical models of data , 2012 .

[33]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[34]  R. E. Lee,et al.  Distribution-free multiple comparisons between successive treatments , 1995 .

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