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

Pergamon-Elsevier Science Ltd
Publication Type:
Journal Article
Omega-International Journal Of Management Science, 2014, 45 pp. 80 - 91
Issue Date:
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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 under going 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 forHM-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.
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