Support Vector Regression with Chaotic Hybrid Algorithm in Cyclic Electric Load Forecasting

Abstract

Application of support vector regression (SVR) with chaotic sequence and evolutionary algorithms not only could improve forecasting accuracy performance, but also could effectively avoid converging prematurely. However, the tendency of electric load sometimes reveals cyclic changes due to seasonal economic activities or climate seasonal nature. The applications of SVR model to deal with cyclic electric load forecasting have not been widely explored. This investigation presents a SVR-based electric load forecasting model which applied a novel hybrid algorithm, namely chaotic genetic algorithm-simulated annealing algorithm (CGASA), to improve the forecasting performance. In addition, seasonal adjustment mechanism is also employed to deal with cyclic electric loading tendency. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed seasonal support vector regression with chaotic genetic algorithm, namely SSVRCGASA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-e-SVR-SA models in existed papers. Therefore, the SSVRCGASA model is a promising alternative for electric load forecasting.