Hybrid methodology for short-term load forecasting

The main objective of this paper is to accurately forecast the short-term loads using Discrete Wavelet Transform (DWT) in combination with Artificial Neural Network/ Support Vector Machine. The complete analysis has been carried out using Temperature, Humidity, Dew Point and Actual loads as features. Here, 8-level DWT decomposition has been done to extract the 8 detailed and approximation coefficients, which are also used as features. Thereafter to enhance the accuracy, four optimal features are selected from the total feature set using Forward Feature Selection Algorithm during the training process during ANN/ SVM. The test data with the optimal features were then fed to the ANN or SVM for load forecasting. Here MAPE has been considered as the performance index. The test results demonstrate that the proposed technique is quite accurate to forecast the loads.

[1]  Pasi Luukka,et al.  Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..

[2]  C. Cañizares,et al.  ANN-based short-term load forecasting in electricity markets , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[3]  M. G. Unde,et al.  SHORT-TERM LOAD FORECASTING USING ANN TECHNIQUE , 2012 .

[4]  Naftali Tishby,et al.  Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity , 2005, NIPS.

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

[6]  Luc Devroye,et al.  The uniform convergence of nearest neighbor regression function estimators and their application in optimization , 1978, IEEE Trans. Inf. Theory.

[7]  Shun-Feng Su,et al.  Robust support vector regression networks for function approximation with outliers , 2002, IEEE Trans. Neural Networks.

[8]  M. Kezunovic,et al.  Transmission Line Boundary Protection Using Wavelet Transform and Neural Network , 2007, IEEE Transactions on Power Delivery.

[9]  Simon Haykin,et al.  An explicit algorithm for training support vector machines , 1999, IEEE Signal Processing Letters.

[10]  Wei Sun,et al.  Short Term Load Forecasting Based on BP Neural Network Trained by PSO , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[11]  Kranthimanoj Nagothu,et al.  Prediction of cloud data center networks loads using stochastic and neural models , 2011, 2011 6th International Conference on System of Systems Engineering.

[12]  Wei Li,et al.  An Improved GM(1,1)-Genetic Algorithm to Short-Term Forcasting in Power System , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[13]  Andrew W. Moore,et al.  The Racing Algorithm: Model Selection for Lazy Learners , 1997, Artificial Intelligence Review.

[14]  G. G. Karady,et al.  An adaptive neural network approach to one-week ahead load forecasting , 1993 .