GenTL: A General Transfer Learning Model for Building Thermal Dynamics

TL;DR

GenTL introduces a universal transfer learning model pretrained on diverse buildings, enabling efficient and accurate thermal dynamics modeling for new buildings without source selection, significantly reducing prediction errors.

Contribution

The paper presents GenTL, a general transfer learning model that eliminates source-building selection by serving as a universal pretrained model for diverse buildings.

Findings

42.1% average RMSE reduction in prediction error

Effective fine-tuning across 144 target buildings

Outperforms conventional single-source transfer learning

Abstract

Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging knowledge from a source building. Consequently, it enables the creation of data-efficient models that can be used for advanced control and fault detection & diagnosis. A major limitation of the TL approach is its inconsistent performance across different sources. Although accurate source-building selection for a target is crucial, it remains a persistent challenge. We present GenTL, a general transfer learning model for single-family houses in Central Europe. GenTL can be efficiently fine-tuned to a large variety of target buildings. It is pretrained on a Long Short-Term Memory (LSTM) network with data from 450 different buildings. The general transfer learning model eliminates the need for source-building selection by serving as a universal source for fine-tuning. Comparative analysis with conventional single-source to single-target TL demonstrates the efficacy and reliability of the general pretraining approach. Testing GenTL on 144 target buildings for fine-tuning reveals an average prediction error (RMSE) reduction of 42.1 % compared to fine-tuning single-source models.