Data-driven control of micro-climate in buildings: An event-triggered reinforcement learning approach
Name
2001.10505.pdf
Description
Accepted version
Size
1.94 MB
Format
Adobe PDF
Checksum (MD5)
fe16748c5982827bb587d8fc62313dcf
Author(s)
Haji Hosseinloo, Ashkan
Ryzhov, Alexander
Bischi, Aldo
Ouerdane, Henni
Turitsyn, Konstantin
Dahleh, Munther A
Date Issued
November 2020
Journal
Applied Energy
Publisher
Elsevier BV
Citation
Haji Hosseinloo, Ashkan et al. "Data-driven control of micro-climate in buildings: An event-triggered reinforcement learning approach." Applied Energy 277 (November 2020): 115451 © 2020 Elsevier Ltd
Version
Author's final manuscript
Abstract
Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. Future of the smart buildings lies in using sensory data for adaptive decision making and control that is currently gloomed by the key challenge of learning a good control policy in a short period of time in an online and continuing fashion. To tackle this challenge, an event-triggered – as opposed to classic time-triggered – paradigm, is proposed in which learning and control decisions are made when events occur and enough information is collected. Events are characterized by certain design conditions and they occur when the conditions are met, for instance, when a certain state threshold is reached. By systematically adjusting the time of learning and control decisions, the proposed framework can potentially reduce the variance in learning, and consequently, improve the control process. We formulate the micro-climate control problem based on semi-Markov decision processes that allow for variable-time state transitions and decision making. Using extended policy gradient theorems and temporal difference methods in a reinforcement learning set-up, we propose two learning algorithms for event-triggered control of micro-climate in buildings. We show the efficacy of our proposed approach via designing a smart learning thermostat that simultaneously optimizes energy consumption and occupants’ comfort in a test building.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.apenergy.2020.115451
