Combining Transfer Learning and Synthetic Time-Series Data to Predict Building Energy Consumption

This study explores the usability of pretrained & fine-tuned data-driven building energy models to enhance model transferability across buildings. Using this transfer learning approach, four models were pretrained on synthetic data and tested on real building data. Two models were applied directly to the test data, and two models were previously fine-tuned. Fine-tuning involved adjusting the pretrained models by using the initial parameters for further optimization. Utilised algorithms were Multiple Linear Regression (MLR) and Random Forest Regression (RFR). The synthetic data were generated by simulating 804 variants of physical building energy models, using EnergyPlus. The MLR model applied directly to the target data performed with a mean CV-RMSE of 25.3 % and the RFR model with a mean CV-RMSE of 31.2 %, which decreased to 26.1 % after fine-tuning. Although these results show lower accuracy compared to models developed in similar studies, the models offer improved generalization and lower computational cost. The enhanced generalization potential enables these models to be versatile in various building types and scenarios. This is especially relevant for applications in data-scarce environments, where historical building data are inaccessible. Additionally, their lower computational cost enables more frequent usage in resource-limited settings, such as optimization