A Corrected Hybrid Approach for Electricity Demand Forecasting

For proper and efficient evaluation of electricity demand forecasting, a hybrid Seasonal Auto-Regression Integrated Moving Average and Least Square Support Vector Machine (SARIMA-LSSVM) model is significantly developed to forecast the electricity demand in New South Wales of Australia. The design concept of combining the Seasonal Auto-Regression Integrated Moving Average (SARIMA) method with the Least Square Support Vector Machine (LSSVM) algorithm shows more powerful forecasting capacity for daily electricity demand forecasting at electricity parks, when compared with the single SARIMA and LSSVM models. To verify the developed approach, the daily data from New South Wales of Australia is used for model construction and model testing. The simulation and hypothesis test results show that the developed method is simple and quite efficient.