摘要
基于邢台市2013-2018年夏季制冷期(6-8月)逐日电力负荷数据和气象数据,引进闷热指数、舒适度指数,采用负荷趋势分离、相关分析、逐步回归法,研究夏季制冷期电力负荷变化特征及其预报模型。结果表明:邢台市夏季制冷期逐日最大电力负荷总体呈现逐年上升趋势,周平均值变化不明显,旬日最大电力负荷主要集中在8月上旬和7月下旬。气象负荷与逐日气温(平均、最高、最低)、闷热指数和舒适度指数均呈显著的正相关,相关系数均超过了0.600。利用逐步回归法,建立了基于气象因子、闷热指数和舒适度指数的日最大电力负荷预测方程,其中基于气象因子的日最大电力负荷预报方程拟合性最好。采用基于气象因子的日最大电力负荷方程对2019年夏季日电力负荷进行试预报,预报结果平均绝对误差为262.225MW,平均相对误差为6.037%,具有较高的精度。
This paper studies the variation characteristics of power load and its prediction model in summer cooling period by using the daily power load data and meteorological data of Xingtai City in the summer cooling period (June to August) during 2013-2018,the muggy index and comfort index,and the load trend separation,correlation analysis and stepwise regression methods.The results show that the daily maximum power load of Xingtai City in summer cooling period shows an increasing trend year by year,and the weekly average change is not obvious.The maximum power load of each dekad is mainly concentrated in the first dekad of August and the last dekad of July.Meteorological load has significant positive correlations with daily temperature(average,maximum and minimum),muggy index and comfort index,and their correlation coefficients have exceeded 0.600.The daily maximum power load forecasting equation based on meteorological factors,muggy index and comfort index is established by the use of stepwise regression method,of which the daily maximum power load forecasting equation based on meteorological factors has the best fitting.Then,the daily maximum power load equation based on meteorological factors is used to predict the daily power load in summer 2019,and the prediction result presents a higher accuracy with an average absolute error of 262.225MW and an average relative error of 6.037%.
作者
张杰
崔秀云
曲晓黎
Zhang Jie;Cui Xiuyun;Qu Xiaoli(Xingtai Meteorological Office,Xingtai 054000,China;Gansu Meteorological Bureau,Lanzhou 730030,China;Hebei Meteorological Service Center,Shijiazhuang 050021,China)
出处
《气象与环境科学》
2022年第4期106-111,共6页
Meteorological and Environmental Sciences
基金
邢台市科技局项目(2021ZC037)。
关键词
夏季制冷期
电力负荷
预报
气象因子
邢台
summer cooling period
power load
forecast
meteorological factor
Xingtai City
