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Title: Deepcomfort : energy-efficient thermal comfort control in buildings via reinforcement learning
Authors: Gao, Guanyu
Li, Jie
Wen, Yonggang
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Gao, G., Li, J. & Wen, Y. (2020). Deepcomfort : energy-efficient thermal comfort control in buildings via reinforcement learning. IEEE Internet of Things Journal, 7(9), 8472-8484.
Project: NRF2015ENC-GBICRD001-012
Journal: IEEE Internet of Things Journal
Abstract: Heating, Ventilation, and Air Conditioning (HVAC) are extremely energy-consuming, accounting for 40% of total building energy consumption. It is crucial to design some energy-efficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep reinforcement learning based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants’ thermal comfort. We first design a deep Feedforward Neural Network (FNN) based approach for predicting the occupants’ thermal comfort, and then propose a Deep Deterministic Policy Gradients (DDPG) based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simu- lation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants’ thermal comfort by 13.6%.
ISSN: 2327-4662
DOI: 10.1109/JIOT.2020.2992117
Rights: © 2020 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:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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