Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models

摘要

Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making.Multiwindowmoving average algorithmis proposed to decompose themonthly electric energy consumption time series into several periodic waves and a long-termapproximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extracted increasing trend. The forecasting results of themonthly electric energy consumption can be obtained by adding the forecasting values of eachmodel. To test the performance by comparison, the proposed and other threemodels are used to forecastChina’smonthly electric energy consumption fromJanuary 2011 to December 2012. Results show that the proposed model exhibited the best performance in terms ofmean absolute percentage error (MAPE) and maximal absolute percentage error (MaxAPE).