摘要
为分析电力负荷变化特征与气象要素的关系,定量解析气象因子对电力负荷预测的主要贡献,该文以华中电网某地区为研究对象,预报因子选用电力负荷和精细化气象数据,依据逐步回归和BP神经网络模型建立滚动预报模型.通过研究发现:当日负荷除与历史负荷有较好的相关关系外,当日温度与前一日温度对负荷也有较大的影响.气象因子在逐步回归和神经网络预测方法中对负荷预测准确率的提升均有正的贡献,贡献率分别为0.28%~17.87%和0.97%~17.78%.尤其是转折天气条件下,精细化气象因子对短期负荷预测的准确率的提升尤为重要.
A certain area of Central China Power Grid is taken as the research object to analyze the relationship between the electric load and meteorological elements.A rolling prediction model is established by stepwise regression and BP neural network by electric load and refined meteorological data.We quantitatively analyze the main contribution of meteorological factors to electric power load forecasting.It is found that the daily load has a good correlation with the historical load,and the daily temperature and the previous day temperature also have a greater impact on the load.Meteorological factors have positive contributions of load prediction accuracy by stepwise regression and neural network,with contribution rates of 0.28%to 17.87%and 0.97%to 17.78%,respectively.Especially in the case of turning weather conditions,the improvement of the accuracy of short-term load forecasting by the refined meteorological factors is particularly important.
作者
程定芳
任永建
陈正洪
CHEN Dingfang;REN Yongjian;CHEN Zhenghong(Huanggang Meteorology Service Center, Huanggang, Hubei 438000, China;Hubei Meteorology Service Center, Wuhan 430074, China)
出处
《华中师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第5期792-797,共6页
Journal of Central China Normal University：Natural Sciences
基金
湖北省气象局重点项目(2019Z08)
中国气象局气候变化专项(CCSF202033)。
关键词
精细化
短期负荷预测
气象贡献率
滚动预测
refined
short-term load forecasting
meteorological contribution rate
rolling prediction
