A reinforcing transfer learning approach to predict buildings energy performance

IF 3.1 Q2 CONSTRUCTION & BUILDING TECHNOLOGY Construction Innovation-England Pub Date : 2023-08-01 DOI:10.1108/ci-12-2022-0333
Elham Mahamedi, M. Wonders, Nima Gerami Seresht, W. L. Woo, M. Kassem
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Abstract

Purpose The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems. Design/methodology/approach This paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy. Findings It was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error. Research limitations/implications The case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available. Originality/value This approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.
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一种强化迁移学习方法预测建筑能源性能
本文的目的是提出一种新的数据驱动方法来预测建筑物的能源性能,该方法可以解决质量数据的稀缺性,并考虑建筑系统的动态特性。设计/方法/方法本文提出了一种基于迁移学习(TL)的强化机器学习(ML)方法来解决这些挑战。该方法将建筑管理系统捕获的数据动态地整合到模型中,以提高模型的准确性。结果表明,与传统的TL(非强化)方法相比,该方法可以将能量性能预测的准确性提高19个百分点的平均绝对百分比误差。研究局限性/意义案例研究结果证实了所提出方法的实用性,并表明当可用数据很少时,它优于标准ML方法(没有转移知识)。原创性/价值这种方法通过使用TL解决建筑部门有限的数据可用性,从而为知识体系做出贡献;并通过加固结构对建筑物的能源性能进行动态计算。在英国伦敦的一个案例研究项目中实现了所提出的方法。
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来源期刊
Construction Innovation-England
Construction Innovation-England CONSTRUCTION & BUILDING TECHNOLOGY-
CiteScore
7.10
自引率
12.10%
发文量
71
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