A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature

In this paper, a novel combination of wavelet and Elman network as a recurrent neural network is proposed to predict 1-day-ahead electrical power load under the influence of temperature. Using wavelet multi-resolution analysis, the load series are decomposed to different sub-series, showing different frequency characteristics of the load. Elman network (EN) is optimally designed and trained using static back propagation algorithm based on the optimization of performance measures such as mean square error, correlation coefficient and mean absolute percentage error on test prediction dataset. Feasibility of Daubechies wavelet at different scales with suitable number of decomposition levels is investigated to choose the best order for different seasonal load series. The estimated models are evaluated over different temperature and humidity in order to examine their impact on accurate load prediction. The reliability and consistency in prediction by the adopted technique is maintained even in the presence of controlled Gaussian noise to the predicted temperature series. (C) 2012 Elsevier Ltd. All rights reserved.