Dou, Hongwen et Zhang, Kun.
2025.
« Transfer learning for cross-building forecasting of building energy and indoor air temperature in model predictive control applications ».
Journal of Building Engineering, vol. 111.
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Résumé
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.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Zhang, Kun |
| Affiliation: | Génie mécanique |
| Date de dépôt: | 30 juill. 2025 13:36 |
| Dernière modification: | 25 août 2025 12:42 |
| URI: | https://espace2.etsmtl.ca/id/eprint/31197 |
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