Transfer learning for cross-building forecasting of building energy and indoor air temperature in model predictive control applications
Published:
When applying Model Predictive Control (MPC) for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings, accurate forecasting of short-term energy demand and indoor air condition profiles is essential. However, new or retrofitted buildings lack sufficient operation data to develop precise data-driven models. This study investigates transfer learning techniques to enhance the forecasting performance of black-box models under limited data conditions. Specifically, we leverage synthetic data from an open-source EnergyPlus building model to pre-train three neural network models, which are then transferred to a real building and fine-tuned with limited measurements. The results indicate that incorporating synthetic data into the pre-training phase significantly enhances the forecasting accuracy for building and HVAC energy, as well as indoor air temperature profiles, over a 12-h horizon with 15-min intervals. The study underscores the potential of combining transfer learning with synthetic data to address data limitations, extending the applicability of learning-based MPC in real-world buildings.
Recommended citation: Dou, H., Zhang, K., 2025. Transfer learning for cross-building forecasting of building energy and indoor air temperature in model predictive control applications. Journal of Building Engineering 111, 113341. https://doi.org/10.1016/j.jobe.2025.113341