基于负荷分解与辨识的短期电力负荷预测  

Short-term power load forecasting based on load decomposition and identification

作　　者：朱俊澎 李子钰[1] 李虎军 邓振立 袁越[1] ZHU Junpeng;LI Ziyu;LI Hujun;DENG Zhenli;YUAN Yue(College of Energy and Electrical Engineering,Hohai University,Nanjing 210098,China;Economic Research Institute,State Grid Henan Eletric Power Company,Zhengzhou 450052,China)

机构地区：[1]河海大学电气与动力工程学院,南京210098 [2]国网河南省电力公司经济技术研究院,郑州450052

出　　处：《电力需求侧管理》2025年第2期55-61,共7页Power Demand Side Management

基　　金：江苏省自然科学基金资助项目(BK20221165)。

摘　　要：为进一步降低电力负荷数据预测误差,提出一种基于负荷分解与辨识的负荷短期预测方法。首先,针对各行业电力负荷数据,以温度敏感负荷与温度序列的多项式拟合误差为目标函数,将负荷分解转化为数学优化问题,将各行业总负荷分解为周度基荷分量和温度敏感负荷分量;其次,基于长短时记忆网络对温度敏感负荷分量进行短期负荷预测;最后,将温敏负荷预测结果与周度基荷分量叠加得到完整的负荷预测结果。采用某省2022年分行业电力负荷数据进行验证,结果表明提出的基于负荷分解与辨识的短期电力负荷预测方法可有效降低短期负荷预测误差。In order to further reduce the forecasting error of electric load data,a short-term power load forecasting method based on load decomposition and identification is proposed.First,for the electric power load data of each industry,the polynomial fitting error of temperature-sensitive load to the temperature series is taken as the objective function,and the load decomposition is transformed into a mathematical optimization problem,and the total load of each industry is decomposed into the weekly load based on load identification component and the temperature-sensitive load component.Second,the short-term load prediction is performed for the temperature-sensitive load component based on the long short-term memory network.Finally,the temperature-sensitive load prediction results are superimposed with the weekly load based on load identification component to obtain the complete load forecast results.The results show that the short-term load forecasting method based on load decomposition and identification proposed can effectively reduce the short-term load forecasting error.

关 键 词：负荷预测 负荷分解 负荷辨识 长短时记忆网络 

分 类 号：TM714[电气工程—电力系统及自动化] TK018[动力工程及工程热物理]