Exploring the tabular foundation model TabPFN for performance map prediction of variable-speed heat pumps and compressors

Le résumé fourni par la source

Conventional approaches to accurate modelling of heat pumps and compressors are predicated on the availability of high-fidelity, data-intensive models for each component. Statistical and machine learning (ML)-based approaches are often-used, well-performing simplification pathways for purely physics-based modelling. TabPFN, a prior-data-fitted tabular foundation model leveraging the transformer architecture, consistently outperforms other ML approaches. This study investigates the performance of TabPFN in the domain of building energy systems (BES) by benchmarking its performance when predicting performance maps of heat pumps and compressors against other state-of-the-art approaches (XGBoost, Gradient Boost, Random Forest, Polynomial Regression). We develop an advanced training strategy incorporating group indices that buttress the ability of TabPFN to learn the basic, inherent characteristics from one dataset and transfer them when predicting the target variables of a nearly unseen dataset across variants and manufacturers. TabPFN outperforms all other approaches in nearly all instances. In the extreme case where only a single point from the target variant is included in the training dataset, average deviation in predictions remained under 10%. Group indices help in generalising effectively across manufacturers: when trained primarily on one manufacturer’s data, TabPFN yields high prediction accuracy (RMSE COP ≤ 0.2 and RMSE Q ˙ h ≤ 0.75) with the additional inclusion of only two points from the target variant from a different manufacturer. TabPFN can accurately predict equipment performance maps with minimal data, enabling rapid modelling of new component variants and reducing measurement requirements in BES.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.