Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

Abstract

(1) Background: Electricity consumption data are often made up of complex, unstable series that have different fluctuation characteristics in different industries. However, electricity demand forecasting is a prerequisite for the control and scheduling of power systems. (2) Methods: As most previous research has focused on prediction accuracy rather than stability, this paper developed a decomposition-based combination forecasting model using dynamic adaptive entropy-based weighting for total electricity demand forecasting at the engineering level. (3) Results: To further illustrate the prediction accuracy and stationarity of the proposed method, a comparison analysis using an analysis of variance and an orthogonal approach to solve the least squares equations was conducted using classical individual models, a combination forecasting model, and a decomposition-based combination forecasting model. The proposed method had a very satisfactory overall performance with good verification and validation compared to autoregressive integrated moving average (ARIMA) and artificial neural-networks (ANN). (4) Conclusion: As the proposed method dynamically combines various forecast models and can decompose and adapt to various characteristic data sets, it was found to have an accurate, stable forecast performance. Therefore, it could be broadly applied to forecasting electricity demand and developing electricity generation plans and related energy policies.

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