2025/01/23 by Fabian Raisch, Thomas Krug, Raisch, Fabian +5 · 2 citations
#Building Energy and Comfort Optimization #Neural Networks and Applications
paper · pdf · doi:10.1145/3679240.3734589
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 singlefamily houses in Central Europe.GenTL can be efficiently finetuned 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 singlesource models.