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