Keywords
industry power users, component index, cycle decomposition, working strength, Granger causality test
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.
DOI
10.19781/j.issn.1673-9140.2022.06.021
First Page
181
Last Page
189
Recommended Citation
YAN, Yuting; XUE, Bing; FANG, Liqian; HUANG, Guoquan; and ZHANG, Yongjun
(2023)
"Big data mining of industry power consumption based on component index about seasonal-adjusted load,"
Journal of Electric Power Science and Technology: Vol. 37:
Iss.
6, Article 21.
DOI: 10.19781/j.issn.1673-9140.2022.06.021
Available at:
https://jepst.researchcommons.org/journal/vol37/iss6/21