Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model

Weigang Zhao (), Jianzhou Wang and Haiyan Lu

Omega, 2014, vol. 45, issue C, 80-91

Abstract: Electricity consumption forecasting has been always playing a vital role in power system management and planning. Inaccurate prediction may cause wastes of scarce energy resource or electricity shortages. However, forecasting electricity consumption has proven to be a challenging task due to various unstable factors. Especially, China is undergoing a period of economic transition, which highlights this difficulty. This paper proposes a time-varying-weight combining method, i.e. High-order Markov chain based Time-varying Weighted Average (HM-TWA) method to predict the monthly electricity consumption in China. HM-TWA first calculates the in-sample time-varying combining weights by quadratic programming for the individual forecasts. Then it predicts the out-of-sample time-varying adaptive weights through extrapolating these in-sample weights using a high-order Markov chain model. Finally, the combined forecasts can be obtained. In addition, to ensure that the sample data have the same properties as the required forecasts, a reasonable multi-step-ahead forecasting scheme is designed for HM-TWA. The out-of-sample forecasting performance evaluation shows that HM-TWA outperforms the component models and traditional combining methods, and its effectiveness is further verified by comparing it with some other existing models.

Keywords: Monthly electricity consumption in China; Time-varying-weight combining method; High-order Markov chain model; Multi-step-ahead combination forecasting; Out-of-sample forecasting accuracy (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (25)

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DOI: 10.1016/j.omega.2014.01.002

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