  Thermal dynamics modeling has been a critical issue in building heating,
ventilation, and air-conditioning (HVAC) systems, which can significantly
affect the control and maintenance strategies. Due to the uniqueness of each
specific building, traditional thermal dynamics modeling approaches heavily
depending on physics knowledge cannot generalize well. This study proposes a
deep supervised domain adaptation (DSDA) method for thermal dynamics modeling
of building indoor temperature evolution and energy consumption. A long short
term memory network based Sequence to Sequence scheme is pre-trained based on a
large amount of data collected from a building and then adapted to another
building which has a limited amount of data by applying the model fine-tuning.
We use four publicly available datasets: SML and AHU for temperature evolution,
long-term datasets from two different commercial buildings, termed as Building
1 and Building 2 for energy consumption. We show that the deep supervised
domain adaptation is effective to adapt the pre-trained model from one building
to another building and has better predictive performance than learning from
scratch with only a limited amount of data.
