GenTL: A General Transfer Learning Model for Building Thermal Dynamics

2025-01-23

AI Summary

To address the challenges of uncertain source-building selection and poor generalizability in transfer learning for thermal dynamic modeling of single-family dwellings in Central Europe, this paper proposes GenTL—a generic transfer learning framework. GenTL leverages multi-source time-series data from 450 buildings to perform large-scale supervised pretraining of an LSTM model, abandoning conventional single-source transfer paradigms and enabling plug-and-play fine-tuning without manual selection of “teacher” buildings. Experiments across 144 target buildings demonstrate that GenTL reduces average RMSE by 42.1% compared to single-source transfer methods, significantly improving cross-building prediction accuracy and robustness. Its core contribution lies in being the first to introduce generic pretraining into building thermal dynamic modeling, establishing a scalable, highly generalizable modeling paradigm for small-sample and heterogeneous building scenarios.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Generation
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Transfer Learning
• Building Temperature Dynamics
• Single-family Dwellings in Central Europe

Innovation

• GenTL
• LSTM network
• thermal performance prediction