Electricity Demand Forecasting with a Novel Hybrid Model

This paper proposed a novel model, MFES, that combines the multi-output FFNN (feedforward neural network) with EMD (empirical mode decomposition) and seasonal adjustment. In electric market, accurate electricity demand forecasting is often needed. But in electricity demand forecasting, noise signals, caused by various unstable factors, often corrupt demand series. So, in order to make accurate electricity demand forecasting, MFES first uses Empirical mode decomposition to remove the noise interference. Secondly, MFES removes the seasonal component from the denoised demand series and models the resultant series using FFNN model with a multi-output strategy. At last, MFES obtains the final prediction by restoring the season indexes back to the FFNN forecasts. Using the half-hour electricity demand series of Australia, this paper demonstrates that the proposed MFES model improves the forecasting accuracy noticeably comparing with other models.