基于因果关系分析的短期负荷预测方法研究  

Research on Forecasting Methods of Short-Term Load Based on Causal Relationship Analysis

作　　者：张光儒 马振祺 杨军亭 张家午 苏娟[2] 高天 丁泽琦 方舒 ZHANG Guangru;MA Zhenqi;YANG Junting;ZHANG Jiawu;SU Juan;GAO Tian;DING Zeqi;FANG Shu(State Grid Gansu Electric Power Company Electric Power Research Institute,Lanzhou 730070,China;College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;Tai’an Power Supply Company,State Grid Shandong Electric Power Company,Tai’an 271000,China)

机构地区：[1]国网甘肃省电力公司电力科学研究院,甘肃兰州730070 [2]中国农业大学信息与电气工程学院,北京100083 [3]国网山东省电力公司泰安供电公司,山东泰安271000

出　　处：《电器与能效管理技术》2022年第8期23-32,共10页Electrical & Energy Management Technology

基　　金：大学城绿色低碳多能互补综合能源系统关键技术研究与示范(52090020002X)。

摘　　要：随着高比例可再生能源和电力市场的快速发展,电力系统不确定性增大。为提高市场环境下负荷预测精度,提出一种基于因果关系分析的短期负荷预测方法。首先,采用灰色关联度分析法量化气象因素和市场因素与负荷的相关性;然后,采用最优模态分解法对负荷模态分解,利用Granger因果分析法将影响因素与模态子序列进行匹配;最后,对子序列分别采用差分自回归移动平均(ARIMA)模型和双向长短时记忆(Bi-LSTM)神经网络模型进行预测,将预测结果叠加得到短期负荷预测结果。仿真结果表明,所提方法的预测精度可达到92%,验证了方法的准确性和有效性。With the high proportion of renewable energy and the rapid development of the electricity market, the uncertainty of the power system increases.In order to improve the accuracy of load forecasting in the market environment, a short-term load forecasting method based on causal relationship analysis is proposed.First, the grey correlation analysis method is used to quantify the correlation between meteorological factors and market factors and the load.Then, the optimal modal decomposition method is used to decompose the load mode, and the Granger causal analysis method is used to match the influencing factors with the modal subsequences.Finally, the subsequence is predicted using the differential autoregressive moving average(ARIMA) model and the bidirectional long-short-term memory(Bi-LSTM) neural network model.The prediction results are superimposed to obtain the short-term load prediction results.The simulation results show that the prediction accuracy of the proposed method can reach 92%,which can verifie the accuracy and effectiveness of the proposed method.

关 键 词：短期负荷预测 最优模态分解 GRANGER因果分析 ARIMA Bi-LSTM 

分 类 号：TM734[电气工程—电力系统及自动化]