Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks

Abstract

Electricity plays a key role in human life. This study presents several methods to forecast Indonesian electricity load demand and compares the performance of the methods. The Indonesian hourly and half-hourly load series tend to have multiple seasonal patterns. Singular Spectrum Analysis (SSA) is chosen because of its capability in decomposing the series into two separable components, a combination of cyclist and seasonal series and noise (irregular) components. In this paper we propose to model time series data by obtaining the forecast values with SSA considering the Linear Recurrent Formula (LRF) and, afterwards, to model the irregular component by fuzzy systems and neural networks (NN). The forecast values obtained from SSA-LRF are then compared with the forecast values obtained from the combining methods, i.e. SSA-LRF-Fuzzy and SSA-LRF-NN. Based on RMSE and MAPE, the SSA-LRF-NN is the most appropriate method to predict the future values of electricity load series. Four Indonesian electricity load data sets were considered in this study to validate the effectiveness of the proposed hybrid methods. The results show that the proposed methods, namely the SSA-LRF-NN algorithm can reduce the RMSE for the testing data from that obtained by SSA-LRF up to 83%.