电力信息与通信技术2024，Vol.22Issue(5)：36-42,7.DOI:10.16543/j.2095-641x.electric.power.ict.2024.05.06
基于压缩感知的纵向联邦学习园区负荷预测方法
Load Forecasting Method for Vertical Federated Learning Park Based on Compressed Sensing
摘要
Abstract
With the increasing importance of park load forecasting,in order to satisfy the data security and privacy protection needs between power enterprises and users,this paper proposes a vertical federated learning park load forecasting method based on compressed sensing.The method utilizes the compressed sensing technique to downscale the gradient,which effectively reduces the amount of transmitted data.Experimental results show that compared with the traditional federated learning method,the method in this paper significantly reduces the communication consumption while maintaining the model accuracy.In addition,the method effectively protects the privacy of the data and provides a safer cooperation environment between power enterprises and users.关键词
负荷预测/纵向联邦学习/压缩感知Key words
load forecasting/vertical federated learning/compressed sensing分类
信息技术与安全科学引用本文复制引用
杨珂,朱洪斌,李达,张闻彬,杨挺,覃小兵..基于压缩感知的纵向联邦学习园区负荷预测方法[J].电力信息与通信技术,2024,22(5):36-42,7.基金项目
国家电网有限公司总部科技项目资助"能源大数据多方安全融合应用隐私计算技术研究"(5108-202218280A-2-393-XG). （5108-202218280A-2-393-XG）
