Abstract
The information from electricity demand forecasting helps energy generation enterprises develop an electricity supply system. This study aims to develop a monthly electricity forecasting model to predict the electricity demand for energy management. Given that the influence of weather factors, such as temperature and humidity, is diluted in the overall monthly electricity demand, the forecasting model uses historical electricity consumption data as an integrated factor to obtain future prediction. The proposed approach is applied to a monthly electricity demand time series forecasting model that includes trend and fluctuation series, of which the former describes the trend of the electricity demand series and the latter describes the periodic fluctuation imbedded in the trend. An integrated genetic algorithm and neural network model (GANN) is then trained to forecast the trend series. Given that the fluctuation series demonstrates an oscillatory behavior, we apply Fourier series to fit the fluctuation series. The complete demand model is named GANN–Fourier series. U.S. electricity demand data are used to evaluate the proposed model and to compare the results of applying this model with those of using conventional neural networks.