Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption

Electricity consumption forecasting is essential for intelligent power systems. In fact, accurate forecasting of monthly consumption to predict medium- and long-term demand substantially contributes to the appropriate dispatch and management of electric power systems. Most existing studies on monthly electricity consumption forecasting require large datasets for accurate prediction, which is severely undermined when scarce data are available. However, in practical scenarios, data is not always sufficient, thereby hindering the accurate forecasting of monthly electricity consumption. The Holt–Winters exponential smoothing allows to accurately forecast periodic series with relatively few training samples. Based on this method, we propose a hybrid forecasting model to predict electricity consumption. The fruit fly optimization algorithm is used to select the best smoothing parameters for the Holt–Winters exponential smoothing. We used electricity consumption data from a city in China to comprehensively evaluate the forecasting performance of the proposed model compared to similar methods. The results indicate that the proposed model can substantially improve the prediction accuracy of monthly electricity consumption even when few training samples are available. Moreover, the computation time of the proposed model is the shortest among the evaluated hybrid benchmark algorithms.