首页|夏季日峰降温电力负荷预测灰色模型及其应用

夏季日峰降温电力负荷预测灰色模型及其应用

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基于2017-2020年石家庄市逐15分钟电力负荷及同期气象资料,计算人体舒适度指标有效温度和温湿指数,考虑基准负荷存在周期性和增长性,提出采用灰色模型GM(1,1)并结合滤波法、相关分析等方法,建立日峰降温电力负荷与人体舒适度指标分段回归模型。结果表明:石家庄电力负荷具有明显的逐年增长趋势;剥离出的日降温负荷曲线呈"W"型分布;分别对模型进行一次、二次和分段函数拟合,对3种预测模型进行检验发现分段函数预测精度较高,平均相对误差在4。8%~5。2%,有效温度和温湿指数的分段函数误差在-10%~10%所占比例分别为88。1%和90。5%;考虑了温度、湿度和风速的有效温度较温湿指数的夏季日峰降温电力负荷预测模型预测准确率更高,回归模型分段点为26。2℃,对电网"迎峰度夏"时期电力调度具有参考价值。
A Grey Model for Power Load Prediction of Summer Peak Cooling and Its Application
Based on the 15-min power load of Shijiazhuang City from 2017 to 2020 and the meteorological data of the same period,we calculate the effective temperature and the temperature-humidity index of hu-man comfort index.Considering the periodicity and growth of the benchmark load,we adopt the grey mod-el GM(1,1),and at the sametime,combining filtering method and correlation analysis method,we estab-lish a piecewise regression model of daily peak cooling load and human body comfort index.The results show that the power load in Shijiazhuang has an obvious year-on-year growth trend.The stripped-off daily cooling load curve shows a"W"-shaped distribution.The models are fitted with primary,secondary and piecewise functions respectively.The three prediction models are tested,and the fixdings suggest that the prediction accuracy of the piecewise function is relatively high,the average relative error is between 4.8%and 5.2%,and the piecewise function error between the effective temperature and the temperature-humidi-ty index is between-10%and 10%,and the proportions are 88.1%and 90.5%,respectively.The effec-tive temperature ratio considering temperature,humidity and wind speed has a higher forecast accuracy than the summer daily peak cooling load forecasting model which is based on the temperature-humidity in-dex.The regression model piecewise point is 26.2℃,which has better reference value for power dispatch during the"peak summer"period.

cooling-loadgrey modelfiltering methodcorrelation analysispiecewise regression

王洁、曲晓黎、尤琦、杨琳晗、时珉、张金满

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中国气象局雄安大气边界层重点开放实验室,河北雄安 071800

河北省气象与生态环境重点实验室,石家庄 050021

河北省气象服务中心,石家庄 050021

国网河北省电力有限公司,石家庄 050011

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降温负荷 灰色模型 滤波法 相关性分析 分段回归

河北省政府三三三人才资助项目河北省省级科技计划项目

A20210100122375405D

2024

气象
国家气象中心

气象

CSTPCD北大核心
影响因子:2.337
ISSN:1000-0526
年,卷(期):2024.50(1)
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