Abstract
Indoor environment quality (IEQ) is one of the most concerned building performances during the operation stage. The non-uniform spatial distribution of various IEQ parameters in large-scale public buildings has been demonstrated to be an essential factor affecting occupant comfort and building energy consumption. Currently, IEQ sensors have been widely employed in buildings to monitor thermal, visual, acoustic and air quality. However, there is a lack of effective methods for exploring the typical spatial distribution of indoor environmental quality parameters, which is crucial for assessing and controlling non-uniform indoor environments. In this study, a novel clustering method for extracting IEQ spatial distribution patterns is proposed. Firstly, representation vectors reflecting IEQ distributions in the concerned space are generated based on the low-rank sparse representation. Secondly, a multi-step clustering method, which addressed the problems of the “curse of dimensionality”, is designed to obtain typical IEQ distribution patterns of the entire indoor space. The proposed method was applied to the analysis of indoor thermal environment in Beijing Daxing international airport terminal. As a result, four typical temperature spatial distribution patterns of the terminal were extracted from a four-month monitoring, which had been validated for their good representativeness. These typical patterns revealed typical environmental issues in the terminal, such as long-term localized overheating and temperature increases due to a sudden influx of people. The extracted typical IEQ spatial distribution patterns could assist building operators in effectively assessing the uneven distribution of IEQ space under current environmental conditions, facilitating targeted environmental improvements, optimization of thermal comfort levels, and application of energy-saving measures.
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Acknowledgements
This study is supported by the China National Key Research and Development Program (Grant No. 2022YFC3801300), the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 52208113), the Key Program of National Natural Science Foundation of China (Grant No. 52130803), and the Hang Lung Center for Real Estate, Tsinghua University. The authors also express special thanks to the Command Center of Beijing Daxing International Airport for their long-term and strong support to this research.
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All authors contributed to the study conception and design. Methodology was proposed by Yuren Yang, Yang Geng and Borong Lin. Modeling and algorithm writing was performed by Yuren Yang. Material preparation, data collection, data processing and analysis were performed by Yuren Yang, Yang Geng, Hao Tang, Mufeng Yuan, Juan Yu and Borong Lin. The original draft of the manuscript was written by Yuren Yang. Revision of the manuscript was written by Yuren Yang, Yang Geng, Hao Tang, Mufeng Yuan and all authors commented on this version of the manuscript.
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The authors have no competing interests to declare that are relevant to the content of this article. Borong Lin is an Editorial Board member of Building Simulation.
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Appendix to Extraction method of typical IEQ spatial distributions based on low-rank sparse representation and multi-step clustering
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Yang, Y., Geng, Y., Tang, H. et al. Extraction method of typical IEQ spatial distributions based on low-rank sparse representation and multi-step clustering. Build. Simul. (2024). https://doi.org/10.1007/s12273-024-1117-6
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DOI: https://doi.org/10.1007/s12273-024-1117-6