Journal of Arid Meteorology ›› 2021, Vol. 39 ›› Issue (4): 709-715.

Previous Articles    

Analysis on Forecast Effect of Daily Maximum Power Load Variation in Shijiazhuang Based on Three Models

WU Huiqin, YANG Linhan, ZHAGN Zhongjie   

  1. Public Meteorological Service Centre of Hebei Province, Shijiazhuang 050021, China
  • Online:2021-08-31 Published:2021-09-13

基于3种模型的石家庄日最大电力负荷变幅预报效果分析
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武辉芹,杨琳晗,张中杰   

  1. 河北省气象服务中心,河北石家庄050021

  • 通讯作者: 张中杰(1968— ),男,高级工程师,主要从事电力、交通、医疗气象服务研究. E-mail: 13315996720@189.cn。
  • 作者简介:武辉芹(1973— ),女,高级工程师,主要从事电力和交通气象服务研究. E-mail: 1532650350@qq.com。
  • 基金资助:
    “基于影响的精细化电力气象负荷变化率研究”(19ky07)资助

Abstract: Based on daily maximum power load in Shijiazhuang from 2017 to 2019 and meteorological data during the same period, the variation characteristic of daily maximum power load was analyzed. The correlation between variation of daily maximum power load and meteorological factors, air quality index (AQI) was also analyzed. The forecasting models of daily maximum power load variation in winter and summer were established by using stepwise regression, multiple linear regression and generalized additive model (GAM), the data of corresponding time in 2019 were taken as the independent test samples of forecasting effect. The results show that the daily maximum power load in Shijiazhuang had an obvious increasing trend from 2017 to 2019, and the correlation between variation of power load and factors had obvious seasonality. The factors which had a negative correlation with daily maximum power load variation in winter were positively correlated with daily maximum power load variation in summer, and vice versa. Among the three models, GAM model had the best prediction effect, and its forecast effect in summer was better than that in winter. In business application, GAM model with AQI could be selected in summer, and GAM model without AQI should be selected in winter.


Key words: power load variation, meteorological factor, GAM, stepwise regression, multiple linear regression

摘要: 基于石家庄2017—2019年逐日最大电力负荷和同期气象资料,分析日最大电力负荷的变化规律,在分析日最大电力负荷变幅与气象因子及空气质量指数(AQI)的相关性基础上,采用逐步回归、多元线性回归和广义相加模型(GAM)分冬季和夏季建立日最大电力负荷变幅预报模型,将2019年冬季和夏季月资料作为预报效果独立检验样本。结果表明:2017—2019年石家庄日最大电力负荷存在明显增长趋势,电力负荷变幅和气象因子及AQI的相关性具有明显的季节性,冬季与日最大电力负荷变幅呈负相关的因子在夏季与日最大电力负荷变幅呈正相关,反之亦然;3种模型中,GAM模型的预报效果最好,且夏季预报效果好于冬季,在业务应用中,夏季可选取含AQI的GAM模型,冬季应选取不含AQI的GAM模型。

关键词: 电力负荷变幅, 气象因子, GAM, 逐步回归, 多元线性回归

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