邱敏, 周颖, 赵伟博, 王阳, 陈宋宋, 郭耀扬, 赵波. 基于特征构建的区域电力负荷增长归因及量化分析方法[J]. 中国电力, 2024, 57(8): 190-205. DOI: 10.11930/j.issn.1004-9649.202310056
引用本文: 邱敏, 周颖, 赵伟博, 王阳, 陈宋宋, 郭耀扬, 赵波. 基于特征构建的区域电力负荷增长归因及量化分析方法[J]. 中国电力, 2024, 57(8): 190-205. DOI: 10.11930/j.issn.1004-9649.202310056
QIU Min, ZHOU Ying, ZHAO Weibo, WANG Yang, CHEN Songsong, GUO Yaoyang, ZHAO Bo. Attribution and Quantitative Analysis Method for Regional Power Load Growth Based on Feature Construction[J]. Electric Power, 2024, 57(8): 190-205. DOI: 10.11930/j.issn.1004-9649.202310056
Citation: QIU Min, ZHOU Ying, ZHAO Weibo, WANG Yang, CHEN Songsong, GUO Yaoyang, ZHAO Bo. Attribution and Quantitative Analysis Method for Regional Power Load Growth Based on Feature Construction[J]. Electric Power, 2024, 57(8): 190-205. DOI: 10.11930/j.issn.1004-9649.202310056

基于特征构建的区域电力负荷增长归因及量化分析方法

Attribution and Quantitative Analysis Method for Regional Power Load Growth Based on Feature Construction

  • 摘要: 电力负荷由于受到气温、经济、特殊事件等多种因素及多因素耦合影响,增长成因量化分析困难。同时,目前对于电力负荷研究多集中于预测方面,对负荷增长原因分析较少。通过研究电力负荷数据特征构建方法,提出一种电力负荷增长归因分析方法。首先,构建气象相关性指标、基于经济发展的自然负荷增长指标、基于电力电量修正的产业结构变化指标以及事件趋势一致性评价指标;在此基础上,分别提取气象负荷、自然经济负荷、业扩负荷、随机负荷,利用贡献率量化各因素对负荷增长的影响程度。最后,利用西北某2省的电力电量数据进行验证,结果显示所提方法能够很好地量化负荷增长的原因。

     

    Abstract: The power load is affected by various factors such as temperature, economy, special events, and multi-factor coupling, which makes the quantitative analysis of the causes of the power load growth difficult. At the same time, current load analysis is mostly focused on prediction, and it is uncommon to analyze the causes of load growth.Therefore, based on a study on the construction method of power load data characteristics, a power load growth attribution analysis method is proposed. Firstly, the meteorological correlation indicators, the economic development-based natural load growth indicators, the electrical quantity correction-based industrial structure change indicators, and the event trend consistency evaluation indicators are constructed. And then, the meteorological load, natural economic load, industrial expansion load and random load are respectively extracted, and the contribution rate is used to quantify the influence degree of each factor on the load growth. Finally, the power consumption data of two northwestern provinces are used to verify the proposed method, which indicates that the proposed method is effectively quantify the causes of load growth.

     

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