Global-Local Policy Search and its Application in Grid-Interactive Building Control: Paper No. 26

Research output: Contribution to conferencePaper

Abstract

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.
Original languageAmerican English
Number of pages8
StatePublished - 2023
EventEleventh International Conference on Learning Representations (ICLR2023) - Kigali Rwanda
Duration: 1 May 20235 May 2023

Conference

ConferenceEleventh International Conference on Learning Representations (ICLR2023)
CityKigali Rwanda
Period1/05/235/05/23

NREL Publication Number

  • NREL/CP-2C00-85975

Keywords

  • demand response
  • grid-interactive building control
  • reinforcement learning

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