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Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

Zhineng Hu (), Jing Ma (), Liangwei Yang (), Xiaoping Li () and Meng Pang ()
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Zhineng Hu: Business School, Sichuan University, Chengdu 610064, China
Jing Ma: Business School, Sichuan University, Chengdu 610064, China
Liangwei Yang: Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
Xiaoping Li: Business School, Sichuan University, Chengdu 610064, China
Meng Pang: Sichuan Regulatory Commission, State Administration of Energy of China, Chengdu 641000, China

Sustainability, 2019, vol. 11, issue 5, 1-25

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.

Keywords: dynamic adaptive forecast; entropy-based weighting; electricity demand forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2019
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