A physics-guided RNN-KAN for multi-step prediction of heat pump operation states

Siyi Guo, Ziqing Wei, Yaling Yin and Xiaoqiang Zhai

Energy, 2025, vol. 320, issue C

Abstract: Dynamic modeling and operating state prediction for heat pump components are crucial for achieving energy-efficient heating and form the foundation for advanced optimization and control strategies. Heat pump modeling faces two primary challenges: accounting for inherent thermal hysteresis and capturing the nonlinear relationships between model parameters. This paper presents a physics-guided, component-level dynamic modeling framework for heat pumps. Each component is modeled using an innovative RNN-KAN, with input and output variables grounded in physical principles. By sequentially coupling component models based on the thermodynamic cycle, the system achieves multi-step state predictions through a recursive process. To balance prediction accuracy and computational efficiency, a prediction-error-based training method is introduced. This study pioneers the application of the KAN combined with RNN in heat pumps, providing a novel dynamic modeling method and broadening the scope of application. The results demonstrate that the RNN-KAN model effectively captures nonlinear relationships and accounts for thermal hysteresis, improving heating capacity prediction accuracy by 7.75 % over conventional RNN models. Furthermore, the error-based training method enhances prediction accuracy by 4.38 % compared to batch training while reducing computational time by 53.7 % relative to online training methods.

Keywords: RNN-KAN; Heat pump; Heating capacity; Multi-step prediction; Error-based training (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225007509
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:320:y:2025:i:c:s0360544225007509

DOI: 10.1016/j.energy.2025.135108

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().