Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes


Currently, building model calibration algorithms do not leverage data archived from previous related calibration tasks. In this paper, we propose the use of Attentive Neural Processes (ANPs) to meta-learn a distribution across previously seen calibration tasks, which is used to accelerate Bayesian Optimization-based calibration of the unseen target task. Our proposed MetaBOAN algorithm is demonstrated on a library of residential buildings generated by the United States Department of Energy. The experiment results show the significantly improved data efficiency in model calibration.