Federated-learning Based Industry Load Forecasting Framework Under Privacy Protection of Meter Data

WANG Beibei; ZHU Jing; WANG Jiale; MA Jinjie

doi:10.7500/AEPS20220321016

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Federated-learning Based Industry Load Forecasting Framework Under Privacy Protection of Meter Data

Key Technologies of Load Characteristic Modeling and Forecasting for Diverse Users | 更新时间：2025-06-16

- Federated-learning Based Industry Load Forecasting Framework Under Privacy Protection of Meter Data
- Automation of Electric Power Systems Vol. 47, Issue 13, Pages: 86-93(2023)
- 作者机构：
  
  1.东南大学电气工程学院,江苏省南京市 210096
  2.东南大学网络空间安全学院,江苏省南京市 211189
  3.国网江苏省电力有限公司营销部,江苏省南京市 210000
- 作者简介：
- 基金信息：
  
  General Humanities and Social Sciences Research Project of the Ministry of Education(21YJAZH083)
- DOI：10.7500/AEPS20220321016
  CLC：
- Received：21 March 2022，
  
  Revised：2022-09-20，
  
  Published Online：15 December 2022，
  
  Published：10 July 2023
- 稿件说明：
移动端阅览
王蓓蓓,朱竞,王嘉乐等.电表数据隐私保护下的联邦学习行业电力负荷预测框架[J].电力系统自动化,2023,47(13):86-93.

WANG Beibei,ZHU Jing,WANG Jiale,et al.Federated-learning Based Industry Load Forecasting Framework Under Privacy Protection of Meter Data[J].Automation of Electric Power Systems,2023,47(13):86-93.
王蓓蓓,朱竞,王嘉乐等.电表数据隐私保护下的联邦学习行业电力负荷预测框架[J].电力系统自动化,2023,47(13):86-93. DOI： 10.7500/AEPS20220321016.

WANG Beibei,ZHU Jing,WANG Jiale,et al.Federated-learning Based Industry Load Forecasting Framework Under Privacy Protection of Meter Data[J].Automation of Electric Power Systems,2023,47(13):86-93. DOI： 10.7500/AEPS20220321016.

摘要

电力市场化改革背景下，供电公司的用电采集系统不对主动配电网运营商开放，同时，未来终端用户更倾向于将用户信息保存在本地以保护自己的隐私，主动配电网运营商需要在无读表权的条件下开展负荷预测等电力业务。为此，选择天气和时间因素作为负荷的关联因素，提出一种面向行业用户读表数据保护的联邦学习负荷预测框架。在此基础上，构建了行业用户数据集，基于长短期时间序列网络（LSTNet）建立负荷预测模型，同时利用FedML框架建立基于联邦学习的分行业负荷预测框架。算例分析表明，所述方法能使同行业的用户在不共享负荷数据的前提下进行联邦训练，在保护用户用电隐私的前提下支撑主动配电网运营商相关业务开展，具有较优的预测性能、较少的模型数量和较短的耗时。

Abstract

On the background of electricity market reform

the electricity consumption acquisition system of power supply companies is not open to the active distribution network operators. Meanwhile

end users prefer to keep user information locally to protect their privacy in the future

and active distribution network operators need to carry out power business such as load forecasting without meter reading right. Therefore

by selecting the weather and time factors as the correlation factors of load

a federated-learning based load forecasting framework for the protection of meter reading data of industrial users is proposed. On this basis

the industrial user data set is constructed; the load forecasting model is established based on the long- and short-term time-series network (LSTNet); and the sub-industry load forecasting framework based on the federated learning is established by using FedML framework. The case analysis shows that the proposed method can enable users in the same industry to conduct federated training without sharing load data

and support active distribution network operators to carry out relevant business on the premise of protecting users’ electricity privacy. It has better prediction performance

fewer models and shorter time consumption.

关键词

Keywords

references

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Related Institution

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