TR2021-149
Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes
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- , "Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes", Advances in Neural Information Processing Systems (NeurIPS), December 2021.BibTeX TR2021-149 PDF
- @inproceedings{Zhan2021dec,
- author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2021,
- month = dec,
- url = {https://www.merl.com/publications/TR2021-149}
- }
- , "Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes", Advances in Neural Information Processing Systems (NeurIPS), December 2021.
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Abstract:
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.