主办单位：中国气象局沈阳大气环境研究所
国际刊号：ISSN 1673-503X
国内刊号：CN 21-1531/P
气象与环境学报

气象与环境学报 ›› 2020, Vol. 36 ›› Issue (4): 104-111.doi: 10.3969/j.issn.1673-503X.2020.04.014

• 快报 • 上一篇    

气象因素对淮北市电力负荷的影响及其预测研究

汪付华1(),周后福2,3,*(),张屏1,靳继斌4,孙金贺1,王苏瑶1   

  1. 1. 淮北市气象局, 安徽 淮北 235037
    2. 安徽省气象科学研究所, 安徽 合肥 230031
    3. 大气科学与卫星遥感安徽省重点实验室, 安徽 合肥 230031
    4. 国网淮北供电公司, 安徽 淮北 235000
  • 收稿日期:2019-09-02 出版日期:2020-08-30 发布日期:2020-06-16
  • 通讯作者: 周后福 E-mail:34899742@qq.com;zhf_ahqx@sohu.com
  • 作者简介:汪付华,男, 1962年生,高级工程师,主要从事天气预报服务工作, E-mail:34899742@qq.com
  • 基金资助:
    安徽省公益性科研项目(1604f0804002);安徽省气象局科技基金(KM201709)

Effects of meteorological factors on electrical load and the forecasting of electrical load

Fu-hua WANG1(),Hou-fu ZHOU2,3,*(),Ping ZHANG1,Ji-bin JIN4,Jin-he SUN1,Su-yao WANG1   

  1. 1. Huaibei Meteorological Service, Huaibei 235037, China
    2. Anhui Institute of Meteorological Sciences, Hefei 230031, China
    3. Anhui Province Key Lab of Atmospheric Sciences and Satellite Remote Sensing, Hefei 230031, China
    4. Huaibei Power Supply Company, Huaibei 235000, China
  • Received:2019-09-02 Online:2020-08-30 Published:2020-06-16
  • Contact: Hou-fu ZHOU E-mail:34899742@qq.com;zhf_ahqx@sohu.com

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摘要：

利用2012—2016年安徽省淮北市逐日用电负荷和气象要素数据，采用相关分析、回归分析、曲线拟合等统计方法，分析用电负荷的季节变化、周末/节假日效应变化规律，提炼主要气象影响因子并分析温度对负荷的1℃效应和负荷对最高温度的敏感性，构建趋势负荷和趋势方程，介绍将周末/节假日效应应用于气象负荷提取和不同预测模型的建立，采用趋势法建立逐日负荷预测的多元回归方程和曲线拟合方程，并针对趋势法的弱点提出2日增量法，建立相应的预测模型，其中2日增量法预测模型的历史拟合率和2017年试报应用均达到96%—97%，比趋势法高2%—3%，比目前的考核要求高4%—5%，提高了负荷预测精度。

关键词: 趋势负荷, 气象负荷, 周末效应, 趋势法, 2日增量法

Abstract:

Using the daily electrical load and meteorological data of Huaibei in Anhui province from 2012 to 2016, statistical methods such as correlation analysis, regression analysis, and curve fitting were used to analyze the seasonal variation and weekend/holiday effect of electrical load.Main meteorological influence factors were extracted.The effect of temperature (1 ℃) on electrical load and the sensitivity of electrical load to maximum temperature were also analyzed.The trend load and trend equation were determined in this study.The methods of applying weekend/holiday effects to different forecasting models and scientific methods of extracting meteorological load were introduced.The multivariate regression equation and curve-fitting equation of daily electrical load forecasting were established using the trend method.Considering the weakness of the trend method, a 2-day increment method was proposed.The corresponding forecasting model was established.Among them, the historical fitting rate of the 2-day increment forecasting model and the accuracy of the trial forecasting in 2017 both reach 96%-97% which is 2%-3% higher than those with the trend method, and 4%-5% higher than the current assessment requirements.In conclusion, a 2-day increment method improves the accuracy of electrical load forecasting.

Key words: Trend load, Meteorological load, Weekend effect, Trend method, 2-day increment method

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