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

Ana Ospina, Yue Chen, Andrey Bernstein, Emiliano Dall'Anese

Research output: Contribution to journalArticlepeer-review

1 Scopus Citations

Abstract

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.

Original languageAmerican English
Article number108406
Number of pages7
JournalElectric Power Systems Research
Volume211
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022

NREL Publication Number

  • NREL/JA-5D00-83774

Keywords

  • Demand response
  • Gaussian processes regression
  • Multi-zone building
  • Predictive control

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