Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152739
Title: Demystifying thermal comfort in smart buildings : an interpretable machine learning approach
Authors: Zhang, Wei
Wen, Yonggang
Tseng, King Jet
Jin, Guangyu
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Zhang, W., Wen, Y., Tseng, K. J. & Jin, G. (2020). Demystifying thermal comfort in smart buildings : an interpretable machine learning approach. IEEE Internet of Things Journal, 8(10), 8021-8031. https://dx.doi.org/10.1109/JIOT.2020.3042783
Project: NRF2015ENC_GBICRD001-012
BSEWWT2017_2_06
DSAIR@NTU
Journal: IEEE Internet of Things Journal
Abstract: Thermal comfort is a key consideration in smart buildings and a number of comfort models are available nowadays to evaluate the comfort level of occupants. However, the models are often complex and hardly interpretable for the developers and operators. Indeed, the model interpretations are beneficial in multifold such as for system inspection and optimization. In this paper, we propose an interpretable thermal comfort system to introduce interpretability to any black-box comfort models. First, we focus on the relationship between a model’s input features and output comfort level. The feature impact on comfort is investigated and the impact patterns are shown to be diverse for different features. Second, we unveil the model mechanisms about the data processing inside the model by building the model surrogates based on the interpretable machine learning algorithms. The surrogates offer outstanding fidelity for simulating the actual model mechanisms and the interpretations based on the surrogates are intuitive and informative. Our interpretable comfort system can be integrated with the existing building management systems. Accordingly, we can ease building owner’s concerns about adopting new black-box technologies and enable various smart building applications like smart energy management.
URI: https://hdl.handle.net/10356/152739
ISSN: 2327-4662
DOI: 10.1109/JIOT.2020.3042783
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: https://doi.org/10.1109/JIOT.2020.3042783.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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