Thermal-GEMs: Generalized Models for Building Thermal Dynamics

TL;DR

This paper evaluates various general modeling approaches, including multi-source transfer learning and time series foundation models, for building thermal dynamics, providing insights into their accuracy and practical application considerations.

Contribution

It offers the first comprehensive assessment of multi-source transfer learning architectures and time series foundation models for building thermal modeling using real-world data.

Findings

Multi-source transfer learning models reduce forecasting errors by up to 63% compared to single-source models.

Pretraining with data from 16-32 buildings over a year is needed for multi-source TL to outperform TSFMs.

Results guide choosing modeling strategies based on available source building data.

Abstract

Data-driven models for building thermal dynamics are a scalable approach for enabling energy-efficient operation through fault detection & diagnosis or advanced control. To obtain accurate models, measurement data from a target building spanning months to years are required. Transfer Learning (TL) mitigates this challenge by employing pretrained models based on single or multiple source buildings. General multi-source TL models promise to outperform single-source TL, but alternative multi-source modeling architectures remain to be explored, and evaluation on real-world data is missing. Moreover, time series foundation models (TSFM) have emerged as candidates for the best-performing general models. Hence, we conduct a first, comprehensive assessment of general modeling approaches for building thermal dynamics, including multi-source TL and TSFMs. Our assessment includes ablations using four state-of-the-art multi-source TL architectures and evaluations on synthetic as well as real-world data. We demonstrate that multi-source TL models are highly effective in accurately modeling buildings in real-world applications, yielding up to 63% lower forecasting errors compared to single-source TL. Moreover, our results suggest a trade-off between multi-source TL models exclusively pretrained with building data and TSFMs pretrained with a multitude of different time series, revealing that data from 16-32 source buildings must be available over 1 year for pretraining multi-source TL models to consistently outperform TSFMs as evaluated using the mean absolute error. These findings provide practical guidance for selecting modeling strategies based on the number of source buildings available for pretraining multi-source TL models.