Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150840
Title: Thermal comfort modeling for smart buildings : a fine-grained deep learning approach
Authors: Zhang, Wei
Hu, Weizheng
Wen, Yonggang
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Zhang, W., Hu, W. & Wen, Y. (2019). Thermal comfort modeling for smart buildings : a fine-grained deep learning approach. IEEE Internet of Things Journal, 6(2), 2540-2549. https://dx.doi.org/10.1109/JIOT.2018.2871461
Project: NRF2015ENC-GBICRD001-012
Journal: IEEE Internet of Things Journal
Abstract: The emerging Internet of Things (IoT) technology enables smart building management and operation to improve building energy efficiency and occupant thermal comfort. In this paper, we perform data analysis using the IoT generated building data to derive accurate thermal comfort model for smart building control. Deep neural network (DNN) is used to model the relationship between the controllable building operations and thermal comfort. As thermal comfort is determined by multiple comfort factors, a fine-grained architecture is proposed, where an exclusive model is trained for each factor and accordingly the corresponding thermal comfort can be evaluated. The experimental results show that the proposed fine-grained DNN outperforms its coarse-grained counterpart by 3.5× and is 1.7×, 2.5×, 2.4×, and 1.9× more accurate compared to four popular machine learning algorithms. Besides, DNN's performance promotes with deeper network topology and more neurons, and a simple topology with the same number of neurons per network hidden layer is sufficient to achieve high modeling accuracy. Finally, the derived thermal comfort model reveals a linear relationship between comfort and air conditioning setpoint. The linear property helps quickly and accurately search for the optimal controllable setpoint with the desired comfort.
URI: https://hdl.handle.net/10356/150840
ISSN: 2327-4662
DOI: 10.1109/JIOT.2018.2871461
Rights: © 2018 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/JIOT.2018.2871461.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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