A privacy preserving multi-center federated learning framework for district heating forecast

IF 6.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Kais Dai, Esteban Fabello González, Rebeca Isabel García-Betances
{"title":"A privacy preserving multi-center federated learning framework for district heating forecast","authors":"Kais Dai,&nbsp;Esteban Fabello González,&nbsp;Rebeca Isabel García-Betances","doi":"10.1016/j.enbuild.2024.115164","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a privacy-preserving Multi-Center Federated Learning (MCFL) framework for district heating demand forecasting with a 24-hour prediction horizon. To evaluate the effectiveness of this framework, we conducted a comparative analysis across three models: a monolithic model, a traditional federated learning (FL) model, and the proposed MCFL model. Our results demonstrate that the MCFL model improves the prediction accuracy of the standard FL model by 13.86%, suggesting it as a promising enhancement in federated settings. Furthermore, MCFL is particularly well-suited for district heating forecasting, as it handles data heterogeneity, reinforces privacy protections, and supports scalability, making it an ideal choice for complex, distributed environments.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"328 ","pages":"Article 115164"},"PeriodicalIF":6.6000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and Buildings","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378778824012805","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

This paper presents a privacy-preserving Multi-Center Federated Learning (MCFL) framework for district heating demand forecasting with a 24-hour prediction horizon. To evaluate the effectiveness of this framework, we conducted a comparative analysis across three models: a monolithic model, a traditional federated learning (FL) model, and the proposed MCFL model. Our results demonstrate that the MCFL model improves the prediction accuracy of the standard FL model by 13.86%, suggesting it as a promising enhancement in federated settings. Furthermore, MCFL is particularly well-suited for district heating forecasting, as it handles data heterogeneity, reinforces privacy protections, and supports scalability, making it an ideal choice for complex, distributed environments.

Abstract Image

区域供热预报的隐私保护多中心联合学习框架
提出了一个隐私保护的多中心联邦学习框架,用于24小时区域供热需求预测。为了评估该框架的有效性,我们对三种模型进行了比较分析:单一模型、传统联邦学习(FL)模型和提出的MCFL模型。我们的研究结果表明,MCFL模型比标准FL模型的预测精度提高了13.86%,这表明它在联邦环境中是一个有前途的增强。此外,MCFL特别适合区域供热预测,因为它处理数据异构性,加强隐私保护,并支持可扩展性,使其成为复杂的分布式环境的理想选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Energy and Buildings
Energy and Buildings 工程技术-工程:土木
CiteScore
12.70
自引率
11.90%
发文量
863
审稿时长
38 days
期刊介绍: An international journal devoted to investigations of energy use and efficiency in buildings Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信