Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162465
Title: Stochastic optimal control of HVAC system for energy-efficient buildings
Authors: Yang, Yu
Hu, Guoqiang
Spanos, Costas, J.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Yang, Y., Hu, G. & Spanos, C. J. (2021). Stochastic optimal control of HVAC system for energy-efficient buildings. IEEE Transactions On Control System Technology, 30(1), 376-383. https://dx.doi.org/10.1109/TCST.2021.3057630
Journal: IEEE Transactions on Control System Technology
Abstract: The heating, ventilation and air-conditioning (HVAC) system accounts for substantial energy use in buildings, whereas a large group of occupants are still not actually feeling comfortable staying inside. This poses the issue of developing energy-efficient HVAC control, i.e., reduce energy use (cost) while simultaneously enhancing human comfort. This paper pursues the objective and studies the stochastic optimal HVAC control subject to uncertain thermal demand (i.e., the weather and occupancy etc). Particularly, we involve the elaborate predicted mean vote (PMV) thermal comfort model in the optimization. The problem is computationally challenging due to the non-linear and non-analytical constraints imposed by the system dynamics and PMV model. We make the following contributions to address it. First, we formulate the problem as a Markov decision process (MDP) which is a desirable modeling technique capable of handling the complexities. Second, we propose a gradient-based learning (GB-L) method for progressively learning a stochastic control policy off-line and store it for on-line execution. Third, we prove the learning method’s converge to the optimal policies theoretically, and its performance (i.e., energy cost, thermal comfort and on-line computation) for HVAC control via simulations. The comparisons with the existing model predictive control based relaxation (MPC-R) method which is assumed with accurate future information and supposed to provide the near-optimal bounds, show that though there exists some discount in energy cost reduction (i.e., 6.5%), the proposed method can enable efficient on-line implementation (less than 1 second) and provide high probability of thermal comfort under uncertainties.
URI: https://hdl.handle.net/10356/162465
ISSN: 1063-6536
DOI: 10.1109/TCST.2021.3057630
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCST.2021.3057630.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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