Support vector machines with simulated annealing algorithms in electricity load forecasting

• Published in: Energy conversion and management Vol. 46; no. 17; pp. 2669 - 2688
• Main Authors: Pai, Ping-Feng, Hong, Wei-Chiang
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2005; Elsevier
• Subjects: Applied sciences; Autoregressive integrated moving average (ARIMA); Electrical engineering. Electrical power engineering; Electrical power engineering; Electricity load forecasting; Exact sciences and technology; General regression neural networks (GRNN); Power networks and lines; Simulated annealing algorithms (SA); Support vector machines (SVMs); Theory. Simulation; Asia; Taiwan; Support vector machines (SVMs); General regression neural networks (GRNN); Autoregressive integrated moving average (ARIMA); Electricity load forecasting; Simulated annealing algorithms (SA); Industry; Electricity; Simulated annealing; Time series; Feasibility; Models; Neural network; Algorithm; Electric power industry
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2005.02.004

Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. Because of the general nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting electricity load. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. However, SVMs have rarely been applied to forecast electricity load. This investigation elucidates the feasibility of using SVMs to forecast electricity load. Moreover, simulated annealing (SA) algorithms were employed to choose the parameters of a SVM model. Subsequently, examples of electricity load data from Taiwan were used to illustrate the proposed SVMSA (support vector machines with simulated annealing) model. The empirical results reveal that the proposed model outperforms the other two models, namely the autoregressive integrated moving average (ARIMA) model and the general regression neural networks (GRNN) model. Consequently, the SVMSA model provides a promising alternative for forecasting electricity load.