Big data mining of industry power consumption based on component index about seasonal-adjusted load
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    Abstract:

    Under the background of energy digital economy, in order to display and analyze power consuming behavior, and to explore regional economic trends, this paper proposes a periodic adjusted load component index by referring to the stock market index. Firstly, some representative enterprise users are selected as samples based on given rules. Then, the periodic components of the selected user's historical daily electricity quantity are extracted by STL. Hence, the adjustment of the daily electricity quantity can be calculated, and the working strength coefficient is proposed. Then, according to the industry and individual differences, multiple indices are proposed, and the cycle adjusted daily load is weighted and integrated by fuzzy expert evaluation method. After that, based on a selected day’s value, the load trend can be displayed. Finally, the analysis shows that the working strength coefficient is helpful to link with the actual production activities, and the load index can reflect regional daily electricity consumption behavior. Furthermore, after neglecting the influence of temperature, the index has a strong correlation with economic indicators, which can explain the relationship between investment, output and production, and represent the economy of social subject.

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严玉婷,薛冰,方力谦,黄国权,张勇军.基于周期调整负荷成分指数的行业用电大数据价值挖掘[J].电力科学与技术学报英文版,2022,37(6):181-189. YAN Yuting, XUE Bing, FANG Liqian, HUANG Guoquan, ZHANG Yongjun. Big data mining of industry power consumption based on component index about seasonal-adjusted load[J]. Journal of Electric Power Science and Technology,2022,37(6):181-189.

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  • Received:
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  • Online: January 16,2023
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