电力建设2025，Vol.46Issue(1)：37-47,11.DOI:10.12204/j.issn.1000-7229.2025.01.004
基于联邦学习的能源聚合服务商负荷预测
Federated Learning-Based Load Forecasting for Energy Aggregation Service Providers
摘要
Abstract
Energy aggregation service providers in the electricity market are highly dependent on the load-forecasting accuracy to maximize their market interests.However,the required forecasting data are typically scattered among different energy suppliers,and owing to data security and privacy concerns,these suppliers are often unwilling to share their data.Inaccurate load forecasting reduces the operational efficiency of energy aggregation service providers and limits the in-depth understanding of supplier operating models.To address this issue,this study proposes a load-forecasting model based on federated learning,which achieves an effective improvement in load-forecasting accuracy while ensuring data security and privacy.First,an artificial neural network with multidimensional environmental feature selection is established according to the task requirements.Subsequently,a weighted-average strategy was used to jointly train the artificial neural network,bridging the gap between individual energy entities and environmental features.Finally,the model features trained separately are sent to the server of the energy-aggregation service provider for the fitting process.The results of a case analysis using actual data from a large energy aggregation service provider in a southern city in China demonstrate that the proposed model significantly improves operational efficiency and forecasting accuracy.关键词
能源聚合服务商/负荷预测/联邦学习/数据隐私/神经网络Key words
energy aggregation service provider/load forecasting/federated learning/data privacy/neural network分类
动力与电气工程引用本文复制引用
黄一川,宋瑜辉,荆朝霞..基于联邦学习的能源聚合服务商负荷预测[J].电力建设,2025,46(1):37-47,11.基金项目
This work is supported by National Natural Science Foundation of China(No.52107106). 国家自然科学基金项目(52107106) （No.52107106）
