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Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

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  • Da Liu

    (Economics and Management School, North China Electric Power University, Changping District, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping District, Beijing 102206, China
    Institute of Smart Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Kun Sun

    (Economics and Management School, North China Electric Power University, Changping District, Beijing 102206, China
    Institute of Smart Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Han Huang

    (Economics and Management School, North China Electric Power University, Changping District, Beijing 102206, China
    Institute of Smart Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Pingzhou Tang

    (Economics and Management School, North China Electric Power University, Changping District, Beijing 102206, China)

Abstract

Accurate load forecasting can help alleviate the impact of renewable-energy access to the network, facilitate the power plants to arrange unit maintenance and encourage the power broker companies to develop a reasonable quotation plan. However, the traditional prediction methods are insufficient for the analysis of load sequence fluctuations. The economic variables are not introduced into the input variable selection and the redundant information interferes with the final prediction results. In this paper, a set of the ensemble empirical mode is used to decompose the electricity consumption sequence. Appropriate economic variables are as selected as model input for each decomposition sequence to model separately according to its characteristics. Then the models are constructed by selecting the optimal parameters in the random forest. Finally, the result of the component prediction is reconstituted. Compared with random forest, support vector machine and seasonal naïve method, the example results show that the prediction accuracy of the model is better than that of the contrast models. The validity and feasibility of the method in the monthly load forecasting is verified.

Suggested Citation

  • Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3282-:d:169798
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    References listed on IDEAS

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    2. Tadeusz A. Grzeszczyk & Michal K. Grzeszczyk, 2022. "Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models," Energies, MDPI, vol. 15(5), pages 1-20, March.
    3. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
    4. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.

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