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The electricity consumption forecasting is a critical component of theintelligent power system. And accurate monthly electricity consumptionforecasting, as one of the the medium and long term electricity consumptionforecasting problems, plays an important role in dispatching and management forelectric power systems. Although there are many studies for this problem, largesample data set is generally required to obtain higher prediction accuracy, andthe prediction performance become worse when only a little data is available.However, in practical, mostly we experience the problem of insufficient sampledata and how to accurately forecast the monthly electricity consumption withlimited sample data is a challenge task. The Holt-Winters exponential smoothingmethod often used to forecast periodic series due to low demand for trainingdata and high accuracy for forecasting. In this paper, based on Holt-Wintersexponential smoothing method, we propose a hybrid forecasting model namedFOA-MHW. The main idea is that, we use fruit fly optimization algorithm toselect smoothing parameters for Holt-Winters exponential smoothing method.Besides, electricity consumption data of a city in China is used tocomprehensively evaluate the forecasting performance of the proposed model. Theresults indicate that our model can significantly improve the accuracy ofmonthly electricity consumption forecasting even in the case that only a smallnumber of training data is available.

Monthly electricity consumption forecasting by the fruit fly optimization algorithm enhanced Holt-Winters smoothing method