摘要
传统的短期负荷预测中并未考虑实时气象因素的耦合作用,针对此提出了考虑实时气象耦合作用的时域卷积网络短期负荷预测方法。首先,分析了各项实时综合气象指数与负荷曲线的相关性,进而构建了混合日特征量与实时气象因素的相似日选取方法。然后,引入各项实时综合气象指数作为模型输入。最后,采用能够充分考虑并包容实时气象因素与负荷"时差性"特点的时域卷积网络进行日前负荷预测建模。实验仿真以某地区电网实际负荷为例,研究表明该预测模型能够有效提升地区电网日前负荷预测精度。
In the traditional short-term load forecasting,the coupling effect of real-time meteorological factors is not considered.To solve the above problem,a short-term load forecasting method of temporal convolutional network is proposed considering the real-time meteorological coupling effect.First,the correlation between the real-time comprehensive meteorological indices and load curves is analyzed,and then the similar day selection method of the mixed daily characteristics and the real-time meteorological factors is constructed.Moreover,the real-time comprehensive meteorological indices are introduced as the model inputs.Finally,the temporal convolutional network which can fully consider and contain the characteristics of"time difference"between the real-time meteorological factors and the loads is used to carry out the day-ahead load forecasting modeling.The experiment simulation takes the actual load of a regional power grid as an example,and the simulation results show that the forecasting model can effectively improve the accuracy of day-ahead load forecasting in regional power grid.
作者
李滨
陆明珍
LI Bin;LU Mingzhen(Guangxi Key Laboratory of Power System Optimization and Energy Technology(Guangxi University),Nanning 530004,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2020年第17期60-75,共16页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(51767004)
关键词
短期负荷预测
实时气象因素
相似日选取
时域卷积网络
short-term load forecasting
real-time meteorological factor
similar day selection
temporal convolutional network
