Learning-Based Demand Response in Grid Interactive Buildings via Gaussian Processes
This paper presents a predictive controller for a grid-interactive multi-zone building where the temperature dynamics are learned via Gaussian Process (GP) regression. We investigate the development of a learning-based predictive control with two main objectives: (i) continuously learn the temperature dynamics of the building based on data; and, (ii) use the learned dynamics to solve a multi-objective predictive control problem to guarantee occupants' comfort and energy efficiency during normal conditions and demand response events. We leverage the probabilistic non-parametric properties of GPs to estimate the (unknown) non-linear temperature dynamics of the building and to incorporate the uncertainty of those predictions in a multi-objective optimization problem. The GP-based predictive control is solved via a zero-order primal-dual projected-gradient algorithm. We evaluate numerically the performance of the proposed controller using a five-zone commercial building.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1882679
- Report Number(s):
- NREL/JA-5D00-83774; MainId:84547; UUID:7ce5d4d4-3e0f-43e8-abe0-9a3a73a4348d; MainAdminID:65136
- Journal Information:
- Electric Power Systems Research, Journal Name: Electric Power Systems Research Vol. 211
- Country of Publication:
- United States
- Language:
- English
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