2025年4月2日 周三
首页 > 过刊浏览>2024年第39卷第2期 >112-123. DOI:10.19781/j.issn.1673-9140.2024.02.013
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夏季气象电力负荷相关性分析模型修正方法——以北京为例
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作者:
作者单位:

(1.华北电力大学资源环境系统优化教育部重点实验室，北京 102206;2.北京市科学技术研究院城市系统工程研究所，北京 100035)

通讯作者:

沈春明（1985—)，男，博士，研究员，主要从事电力系统韧性提升方面的研究；E?mail：Chunming_Shen@126.com

中图分类号:

TM715

基金项目:

北京市科技新星计划项目（Z191100001119069）；国家重点研发计划（2018YFE0208400）


Correction method of correlation analysis model between meteorology and electric power load in summer: a case study of Beijing
Author:
Affiliation:

(1. Key Laboratory of Resource and Environment System Optimization, Ministry of Education, North China Electric Power University College,Beijing 102206, China; 2. Institute of Urban System Engineering, Beijing Academy of Science and Technology,Beijing 100035, China)

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    摘要:

    气象电力负荷相关性分析对电力负荷预测影响关键，需根据实际数据对相关性经验模型修正。基于综合气象指数、积温效应以及电力负荷与气象因素相关性分析经验公式，提出一种夏季气象负荷相关性模型修正方法；采用负荷趋势分析和Python爬取提取气象负荷与气象数据，提高分析数据的精准性；通过对比分析负荷与单气象因子、综合气象指数以及两种积温效应修正的相关系数，结合负荷与气象指标随时间变化趋势吻合度，确定适用于相关性分析的最优指标参数，进而构建气象负荷与最优指标参数之间的拟合关系式，并以2019年北京夏季主城区为例应用验证。结果表明，与单气象因素相比，电力负荷、气象负荷和综合气象指数的相关性更强；各综合气象指数中，基于日平均气温的酷热指数与气象负荷的相关系数最高；2种积温修正方法中，考虑累积效应系数的方法对气温的修正效果更好，修正后气温与气象负荷的相关系数提高7.39%；基于修正气温的酷热指数与气象负荷的相关性较未修正时均有所提高，与负荷的变化趋势更接近；以酷热指数和修正后温度为自变量构建的气象负荷拟合关系式与实际值的吻合度高于参考的经验公式。

    Abstract:

    The correlation analysis between meteorological factors and power load is critical to power load forecasting, and it is necessary to correct the empirical model of correlation according to actual data. Based on the comprehensive meteorological index, accumulated temperature effect, and the empirical formula of correlation analysis between power load and meteorological factors, a method for correcting the correlation model of meteorological load in summer is proposed. Load trend analysis and Python crawling are used to extract meteorological load and meteorological data to improve the accuracy of analytical data. By comparing and analyzing the correlation coefficients between load and single meteorological factors, comprehensive meteorological indices, and two kinds of accumulated temperature effect corrections, combined with the coincidence degree of load and meteorological indicators over time, the optimal index parameters suitable for correlation analysis are determined. And then the fitting relationship between meteorological load and optimal index parameters is constructed. The proposed method is applied and verified by taking the main urban area of Beijing in summer 2019 as an example. The results show that compared with single meteorological factors, there is a stronger correlation between power load, meteorological load, and comprehensive meteorological index. Among the comprehensive meteorological indices, the heat index based on daily average temperature has the highest correlation coefficient with meteorological load. Among the two accumulated temperature correction methods, the method considering the cumulative effect coefficient has a better correction effect on temperature, and the correlation coefficient between the corrected temperature and meteorological load increases by 7.39%. The correlation between the heat index based on the corrected temperature and meteorological load is higher than that before correction, which is closer to the changing trend of load. The coincidence degree between the fitting relationship of meteorological load constructed with heat index and corrected temperature as independent variables and the actual value is higher than that of the referenced empirical formula.

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刘文娇,沈春明,郭军红,等.夏季气象电力负荷相关性分析模型修正方法——以北京为例[J].电力科学与技术学报,2024,39(2):112-123.
LIU Wenjiao, SHEN Chunming, GUO Junhong, et al. Correction method of correlation analysis model between meteorology and electric power load in summer: a case study of Beijing[J]. Journal of Electric Power Science and Technology,2024,39(2):112-123.

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