Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting

Accurate monthly electricity consumption forecasting can provide the reliable guidance for better energy planning and administration. However, it has been found that the monthly electricity consumption demonstrates a complex nonlinear characteristic and an obvious seasonal tendency. Support vector regression has been widely applied to handle nonlinear time series prediction, but it suffers from the key parameters selection and the influence of seasonal tendency. This paper proposes a novel approach, which hybridizes support vector regression model with fruit fly optimization algorithm and the seasonal index adjustment to forecast monthly electricity consumption. Besides, in order to comprehensively evaluate the forecasting performance of the hybrid model, a small sample of monthly electricity consumption of China and a large sample of monthly electricity retail sales of the United States were employed to demonstrate the forecasting performance. The results show that the proposed hybrid approach is a viable option for the electricity consumption forecasting applications.