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Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case

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  • Li, Der-Chiang
  • Chang, Che-Jung
  • Chen, Chien-Chih
  • Chen, Wen-Chih
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    Abstract

    The overall electricity consumption, treated as a primary guideline for electricity system planning, is a major measurement to indicate the degree of a nation's development. The electricity consumption forecast is especially important with regard to policy making in developing countries (Asian countries in this work). However, since the economic growth rates in these countries are usually high and unstable, it is difficult to obtain accurate predictions using long-term data, and thus forecasting with limited (short-term) data is more effective and of considerable interest. Grey theory is one approach that can be used to construct a model with limited samples to provide better forecasting advantage for short-term problems. The forecasting performance of AGM(1,1), based on grey theory, has been confirmed using the Asia-Pacific economic cooperation energy database, and the results, compared with those obtained from back propagation neural networks (BPN) and support vector regression (SVR), show that the proposed approach can effectively deal with the problem of forecasting electricity consumption when the sample size is limited.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 40 (2012)
    Issue (Month): 6 ()
    Pages: 767-773

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    Handle: RePEc:eee:jomega:v:40:y:2012:i:6:p:767-773

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    Keywords: Forecasting; Grey theory; Electricity consumption; Small data set;

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    Cited by:
    1. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    2. Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
    3. Zheng-Xin Wang, 2013. "A genetic algorithm-based grey method for forecasting food demand after snow disasters: an empirical study," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 675-686, September.
    4. O.A. Carboni & P. Russu, 2014. "Measuring Environmental and Economic Efficiency in Italy: an Application of the Malmquist-DEA and Grey Forecasting Model," Working Paper CRENoS 201401, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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