Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141628
Title: Improved thermal comfort modeling for smart buildings : a data analytics study
Authors: Zhang, Wei
Liu, Fang
Fan, Rui
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Zhang, W., Liu, F., & Fan, R. (2018). Improved thermal comfort modeling for smart buildings : a data analytics study. International Journal of Electrical Power and Energy Systems, 103, 634-643. doi:10.1016/j.ijepes.2018.06.026
Journal: International Journal of Electrical Power and Energy Systems
Abstract: Thermal comfort is a key consideration in the design and modeling of buildings and is one of the main steps to achieving smart building control and operation. Existing solutions model thermal comfort based on factors such as indoor temperature. However, these factors are not directly controllable by building operations, and instead are a by-product of complex interactions between controllable parameters such as air conditioning setpoint and other environmental conditions. In this paper, we use machine learning (ML) to bridge the gap between controllable building parameters and thermal comfort, by conducting an extensive study on the efficacy of different ML techniques for modeling comfort levels. We show that neural networks are especially effective, and achieve 98.7% accuracy on average. We also show these networks can lead to linear models where thermal comfort score scales linearly with the HVAC setpoint, and that the linear models can be used to quickly and accurately find the optimal setpoint for the desired comfort level.
URI: https://hdl.handle.net/10356/141628
ISSN: 0142-0615
DOI: 10.1016/j.ijepes.2018.06.026
Rights: © 2018 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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