Global-Local Policy Search and its Application in Grid-Interactive Building Control

Conference · Thu May 04 04:00:00 EDT 2023

OSTI ID:2246633

Zhang, Xiangyu; Chen, Yue; Bernstein, Andrey

As the buildings sector represents over 70% of the total U.S. electricity consumption, it offers a great amount of untapped demand-side resources to tackle many critical grid-side problems and improve the overall energy system's efficiency. To help make buildings grid-interactive, this paper proposes a global-local policy search method to train a reinforcement learning (RL) based controller which optimizes building operation during both normal hours and demand response (DR) events. Experiments on a simulated five-zone commercial building demonstrate that by adding a local fine-tuning stage to the evolution strategy policy training process, the control costs can be further reduced by 7.55% in unseen testing scenarios. Baseline comparison also indicates that the learned RL controller outperforms a pragmatic linear model predictive controller (MPC), while not requiring intensive online computation.

• Research Organization: National Renewable Energy Laboratory (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: AC36-08GO28308
• OSTI ID: 2246633
• Report Number(s): NREL/CP-2C00-85975; MainId:86748; UUID:298f7a1f-18f8-4125-ac4d-8e90210a834b; MainAdminID:71332
• Country of Publication: United States
• Language: English

Related Subjects

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
demand response
grid-interactive building control
reinforcement learning