中国电机工程学报2025，Vol.45Issue(17)：6612-6624,中插2,14.DOI:10.13334/j.0258-8013.pcsee.240407
基于改进FCI算法的电力负荷-气象数据因果关系图辨识
Casual Relationship Identification Between Power System Load and Meteorological Data Based on Improved FCI Algorithm
摘要
Abstract
In power systems,many variables appear as time series under different scales,such as voltage/current waveforms in transient analysis or power/load data.Among these variables,the relationship between load and other time series has been widely investigated in applications such as load forecasting or system dispatch.As an expression of relationship between variables,causality not only has a specific practical meaning,but also can reveal the underlying physical laws of power system operation to some extent,due to its low redundancy when expressing relationships.To this end,an improved fast casual inference algorithm based on multiple causality inference method is proposed in this paper and applied to infer the causality between load and multiple meteorological data series.Case studies based on real data from 2019 to 2022 are conducted to compare with a method based on correlation analysis,which validates the effectiveness of the proposed algorithm.关键词
因果分析/因果发现/电力负荷/气象因素Key words
casual inference/casual discovery/power system load/meteorological factor分类
信息技术与安全科学引用本文复制引用
张广伦,钟海旺..基于改进FCI算法的电力负荷-气象数据因果关系图辨识[J].中国电机工程学报,2025,45(17):6612-6624,中插2,14.基金项目
国家电网有限公司科技项目(5108-202218280A-2-424-XG).Science and Technology Project of State Grid Corporation of China(5108-202218280A-2-424-XG). （5108-202218280A-2-424-XG）
