A novel model ensemble method based on self-adaptive weight for building energy transfer learning

Yan, Ruofei, Chen, Zhen, Zhang, Xingxing, Zhao, Tianyi, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 and Li, Yu 2025. A novel model ensemble method based on self-adaptive weight for building energy transfer learning. Journal of Building Engineering 109 , 113024. 10.1016/j.jobe.2025.113024 Item availability restricted.

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Abstract

Accurate building energy consumption prediction is crucial for building energy management. However, a substantial number of buildings lack sufficient data that hinders the application of data-driven models for energy prediction. Transfer learning emerges as a powerful strategy to address the challenge posed by limited data availability. This research proposes a novel model ensemble method based on Multi-Layer Perception (MLP) structure, which can realise self-adaptive weight, to exploit the advantage of existing transfer learning strategies for building energy sequence-to-sequence (Seq2seq) prediction. The overall and stepwise model performance comparisons between traditional transfer models and traditional ensemble methods in 4 different transfer scenarios are conducted to prove the superior performance of proposed method under all the investigated transfer conditions. The impact of prediction step length on the model performance is also investigated. The results show that the proposed method outperforms traditional transfer models and ensemble methods at different prediction steps in all the investigated transfer conditions. Compared to the best performing transfer model, the proposed method can reduce prediction error by 6.83 %–25.08 %. Compared to the best performing ensemble method, the proposed method can reduce prediction error by 6.32 %–36.54 %. The analysis of the self-adaptive weight reveals that the proposed method is capable of dynamically allocating weights to the two transfer models to enhance the prediction accuracy.

Item Type:	Article
Date Type:	Publication
Status:	In Press
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	2352-7102
Date of First Compliant Deposit:	10 July 2025
Date of Acceptance:	27 May 2025
Last Modified:	16 Jul 2025 12:00
URI:	https://orca.cardiff.ac.uk/id/eprint/179685

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