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Improving energy management practices through accurate building energy consumption prediction: analyzing the performance of LightGBM, RF, and XGBoost models with advanced optimization strategies

Crossref DOI link: https://doi.org/10.1007/s00202-025-03167-8

Published Online: 2025-06-04

Published Print: 2025-09

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Dai, Zhenhua

Huang, Weiguo
Funding

Funding for this research was provided by:

the Open Fund General Project of Hunan Provincial Key Laboratory of Intelligent Protection and Utilization Technology in Masonry Artifacts (HUSE-2024-13)

the General Research Projects of Hunan Provincial Department of Education (23C0358)

the Key Scientific Research Foundation of Hunan Provincial Department of Education (23A0575)

the Hunan Provincial Natural Science Foundation (2024JJ7184)

the Project of Hunan Provincial Social Science Achievement Review Committee in 2023 (XSP2023JYC283)

the School level teaching reform project (XKYJ2024012)
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Text and Data Mining valid from 2025-06-04

Version of Record valid from 2025-06-04
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Article History

Received: 15 October 2024

Accepted: 11 May 2025

First Online: 4 June 2025

Declarations

:

: The authors declare no competing interests.

: The research paper has received ethical approval from the institutional review board, ensuring the protection of participants' rights and compliance with the relevant ethical guidelines.

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