A hybrid intelligent algorithm based short-term load forecasting approach

Abstract In this paper, a new two-step algorithm is proposed for short-term load forecasting (STLF). In the first step of the method, a wavelet transform (WT) and an artificial neural network (ANN) are used for the primary forecasting of the load over the next 24 h. Inputs of this step are weather features (include the daily mean temperature, maximum temperature, mean humidity, and mean wind speed) and previous day load data. In the second step, a WT, the similar-hour method and adaptive neural fuzzy inference system (ANFIS) are used to improve the results of primary load forecasting. In this study, a WT is employed to extract low-order components of the load and weather data. Furthermore, the number of weather data inputs has been reduced by investigating the weather conditions of different cities. To evaluate the performance of the proposed method, it is applied to forecast Iran’s load and New South Wales of Australian’s load. Simulation results in four different cases show that the proposed method increases load forecasting accuracy.

[1]  Sanjay M. Kelo,et al.  A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature , 2012 .

[2]  M Hanmandlu,et al.  Load Forecasting Using Hybrid Models , 2011, IEEE Transactions on Power Systems.

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

[4]  Ismet Erkmen,et al.  Intelligent short-term load forecasting in Turkey , 2006 .

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

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

[7]  Zuyi Li,et al.  Market Operations in Electric Power Systems : Forecasting, Scheduling, and Risk Management , 2002 .

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

[9]  Wei-Chiang Hong,et al.  Hybrid evolutionary algorithms in a SVR-based electric load forecasting model , 2009 .

[10]  Robert J. Schalkoff,et al.  Artificial neural networks , 1997 .

[11]  Jian Wang,et al.  Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .

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

[13]  H. Mori,et al.  Deterministic Annealing Clustering for ANN-Based Short-Term Load Forecasting , 2001, IEEE Power Engineering Review.

[14]  V.H. Hinojosa,et al.  Short-Term Load Forecasting Using Fuzzy Inductive Reasoning and Evolutionary Algorithms , 2010, IEEE Transactions on Power Systems.

[15]  Xiao-Jun Zeng,et al.  Short-Term and Midterm Load Forecasting Using a Bilevel Optimization Model , 2009, IEEE Transactions on Power Systems.

[16]  M. E. El-Hawary,et al.  Fuzzy short-term electric load forecasting , 2004 .

[17]  Zhou Quan,et al.  RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment , 2008, IEEE Transactions on Power Systems.

[18]  Sunil Kumar Sinha,et al.  Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting , 2010, IEEE Transactions on Power Systems.

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

[20]  Nima Amjady,et al.  Short-term hourly load forecasting using time-series modeling with peak load estimation capability , 2001 .

[21]  Joe H. Chow,et al.  Applied mathematics for restructured electric power systems : optimization, control, and computational intelligence , 2005 .