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Title: Learning-Based Demand Response in Grid Interactive Buildings via Gaussian Processes

Journal Article · · Electric Power Systems Research

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 Lab. (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
DOE Contract Number:
Report Number(s):
NREL/JA-5D00-83774; MainId:84547; UUID:7ce5d4d4-3e0f-43e8-abe0-9a3a73a4348d; MainAdminID:65136
Journal Information:
Electric Power Systems Research, Vol. 211
Country of Publication:
United States

References (7)

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Deep Reinforcement Learning for Joint Datacenter and HVAC Load Control in Distributed Mixed-Use Buildings journal July 2021
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