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
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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.
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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.
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