A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement

Yanhua Guo, Ningbo Wang, Shuangquan Shao, Congqi Huang, Zhentao Zhang, Xiaoqiong Li and Youdong Wang

Renewable and Sustainable Energy Reviews, 2024, vol. 204, issue C

Abstract: Purely data-driven modeling methods exhibit inherent “black box” characteristics when applied to the air source heat pump (ASHP) systems for energy efficiency improvement (EEI). Therefore, hybrid physics and data-driven modeling (HPDM) methods have been developed to enhance model interpretability, transparency, and generalizability. The comprehensive and systematic application of HPDM methods in ASHP systems for EEI are reviewed in this work. Firstly, recognizing the combined methods of data-driven and physical information, they are categorized as three essential types: (1) knowledge-infused data, (2) physics-guided modeling, and (3) physics-embedded loss. Secondly, three types of HPDM applied in the important sides (knowledge discovery, building load prediction, faults detection and diagnosis) of the ASHP systems for EEI are reviewed, and the superiority and bottleneck of HPDM in addressing issues in different application scenarios are summarized. Finally, the prospective directions and challenges of HPDM applied in the ASHP systems for EEI are discussed from the perspective of data acquirement and privacy protection, HPDM construction, real-model integration and implementation, method scalability and transferability for the future research reference.

Keywords: Air source heat pump; Efficiency improvement; Hybrid; Data-driven; Interpretability (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114804

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