AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

Extraction method of typical IEQ spatial distributions based on low-rank sparse representation and multi-step clustering

Yuren Yang1,2, Yang Geng1,2( ), Hao Tang1,2, Mufeng Yuan1,2, Juan Yu1,2, Borong Lin1,2
School of Architecture, Tsinghua University, Beijing, China
Key Laboratory of Eco Planning & Green Building, Ministry of Education, Tsinghua University, China
Show Author Information

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.

Keywords

clusteringtemperature fieldthermal environmentspatial distributionindoor environment quality (IEQ)low-rank sparse representation (LRSR)

Electronic Supplementary Material

Download File(s)
bs-17-6-983_ESM.pdf (6.5 MB)

References

 

Al horr Y, Arif M, Katafygiotou M, et al. (2016). Impact of indoor environmental quality on occupant well-being and comfort: A review of the literature. International Journal of Sustainable Built Environment, 5: 1–11.
Crossref Google Scholar
 
Arthur D, Vassilvitskii S (2007). K-means++: The advantages of careful seeding. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA.
 

Chen T, Geman S (2014). Image warping using radial basis functions. Journal of Applied Statistics, 41: 242–258.
Crossref Google Scholar
 

Choi H, Kim H, Yeom S, et al. (2022). An indoor environmental quality distribution map based on spatial interpolation methods. Building and Environment, 213: 108880.
Crossref Google Scholar
 

De Caro F, Pepiciello A, Milano F, et al. (2023). Measurement-based coherency detection through Monte Carlo Consensus Clustering. Electric Power Systems Research, 216: 109075.
Crossref Google Scholar
 

Dong B, Prakash V, Feng F, et al. (2019). A review of smart building sensing system for better indoor environment control. Energy and Buildings, 199: 29–46.
Crossref Google Scholar
 

Doove LL, Van Buuren S, Dusseldorp E (2014). Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics & Data Analysis, 72: 92–104.
Crossref Google Scholar
 

Duan M, Sun H, Wu S, et al. (2023). A simplified model for the evaluation and comparison of the dynamic performance of different heating terminal types. Energy, 263: 125941.
Crossref Google Scholar
 

Fan C, Lin Y, Piscitelli MS, et al. (2023). Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts. Building Simulation, 16: 1499–1517.
Crossref Google Scholar
 

Fiori A, Mignone A, Rospo G (2016). DeCoClu: Density consensus clustering approach for public transport data. Information Sciences, 328: 378–388.
Crossref Google Scholar
 

Geng Y, Ji W, Lin B, et al. (2017). The impact of thermal environment on occupant IEQ perception and productivity. Building and Environment, 121: 158–167.
Crossref Google Scholar
 

Geng Y, Ji W, Xie Y, et al. (2022a). A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data. Automation in Construction, 139: 104303.
Crossref Google Scholar
 

Geng Y, Zhang Z, Yu J, et al. (2022b). An intelligent IEQ monitoring and feedback system: Development and applications. Engineering, 18: 218–231.
Crossref Google Scholar
 
Han J, Kamber M, Pei J (2012). Data Mining: Concepts and Techniques, 3rd edn. San Francisco: Morgan Kaufmann.
 

Hidalgo-Leon R, Urquizo J, Silva CE, et al. (2022). Powering nodes of wireless sensor networks with energy harvesters for intelligent buildings: A review. Energy Reports, 8: 3809–3826.
Crossref Google Scholar
 

Huang Z (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2: 283–304.
Crossref Google Scholar
 

Jang WS, Healy WM, Skibniewski MJ (2008). Wireless sensor networks as part of a web-based building environmental monitoring system. Automation in Construction, 17: 729–736.
Crossref Google Scholar
 
Jia K, Chan TH, Ma Y (2012). Robust and practical face recognition via structured sparsity. In: Proceedings of the 12th European Conference on Computer Vision, Florence, Italy.
Crossref
 

Jiang Y, Sun A, Zhao Y, et al. (2019). Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma. Nature, 567: 257–261.
Crossref Google Scholar
 

John CR, Watson D, Russ D, et al. (2020). M3C: Monte Carlo reference-based consensus clustering. Scientific Reports, 10: 1816.
Crossref Google Scholar
 

Kriegel HP, Kröger P, Zimek A (2009). Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery from Data, 3: 1.
Crossref Google Scholar
 

Lancichinetti A, Fortunato S (2012). Consensus clustering in complex networks. Scientific Reports, 2: 336.
Crossref Google Scholar
 
Liu G, Yan S (2011). Latent low-rank representation for subspace segmentation and feature extraction. In: Proceedings of 2011 International Conference on Computer Vision, Barcelona, Spain.
Crossref
 

Liu G, Lin Z, Yan S, et al. (2013). Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35: 171–184.
Crossref Google Scholar
 

Liu F, Ding Y, Xu F, et al. (2019). Learning low-rank regularized generic representation with block-sparse structure for single sample face recognition. IEEE Access, 7: 30573–30587.
Crossref Google Scholar
 
Lou S, Ma Y (2019). Dimensionality reduction via locality constrained competitive sparse representation by L2-norm regularization. In: Proceedings of 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China.
Crossref
 

Luo Y, Cui D, Song Y, et al. (2023). Fast and accurate prediction of air temperature and velocity field in non-uniform indoor environment under complex boundaries. Building and Environment, 230: 109987.
Crossref Google Scholar
 

Monti S, Tamayo P, Mesirov J, et al. (2003). Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning, 52: 91–118.
Crossref Google Scholar
 

Nie F, Chang W, Hu Z, et al. (2022). Robust subspace clustering with low-rank structure constraint. IEEE Transactions on Knowledge and Data Engineering, 34: 1404–1415.
Crossref Google Scholar
 

Olshausen BA, Field DJ (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381: 607–609.
Crossref Google Scholar
 

Pollard B, Held F, Engelen L, et al. (2021). Data fusion in buildings: Synthesis of high-resolution IEQ and occupant tracking data. Science of the Total Environment, 776: 146047.
Crossref Google Scholar
 

Ramos NMM, Almeida RMSF, Simões ML, et al. (2017). Knowledge discovery of indoor environment patterns in mild climate countries based on data mining applied to in situ measurements. Sustainable Cities and Society, 30: 37–48.
Crossref Google Scholar
 

Șenbabaoğlu Y, Michailidis G, Li JZ (2014). Critical limitations of consensus clustering in class discovery. Scientific Reports, 4: 6207.
Crossref Google Scholar
 

Sha X, Ma Z, Sethuvenkatraman S, et al. (2023). A new clustering method with an ensemble of weighted distance metrics to discover daily patterns of indoor air quality. Journal of Building Engineering, 76: 107289.
Crossref Google Scholar
 

Tekler ZD, Low R, Gunay B, et al. (2020). A scalable Bluetooth Low Energy approach to identify occupancy patterns and profiles in office spaces. Building and Environment, 171: 106681.
Crossref Google Scholar
 

Ünlü R, Xanthopoulos P (2019). Estimating the number of clusters in a dataset via consensus clustering. Expert Systems with Applications, 125: 33–39.
Crossref Google Scholar
 

Wang D, Li W, Liu Y, et al. (2023). Non-uniform operative temperature distribution characteristics and heat-source-controlled core-area range of local heating radiators. Building Simulation, 16: 87–103.
Crossref Google Scholar
 

Wu Z, Pan S, Chen F, et al. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32: 4–24.
Crossref Google Scholar
 

Xia Y, Zhu M, Jiang A, et al. (2023). Model predictive control of indoor thermal environment conditioned by a direct expansion air conditioning system. Building Simulation, 16: 357–378.
Crossref Google Scholar
 
Xie J, Girshick RB, Farhadi A (2016). Unsupervised deep embedding for clustering analysis. In: Proceedings of the 33rd International Conference on Machine Learning (ICML).
 

Yan W, Yang M, Li Y (2023). Robust low rank and sparse representation for multiple kernel dimensionality reduction. IEEE Transactions on Circuits and Systems for Video Technology, 33: 1–15.
Crossref Google Scholar
 
Ye H, Lv H, Sun Q (2016). An improved semi-supervised K-means clustering algorithm. In: Proceedings of 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, China.
 

Yi J, Wu L, Zhou W, et al. (2021). A sparse dimensionality reduction approach based on false nearest neighbors for nonlinear fault detection. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51: 4980–4992.
Crossref Google Scholar
 

Yu Z, Song Y, Song D, et al. (2021). Spatial interpolation-based analysis method targeting visualization of the indoor thermal environment. Building and Environment, 188: 107484.
Crossref Google Scholar
 
Zhang S, Zhang H (2012). A review of wireless sensor networks and its applications. In: Proceedings of 2012 IEEE International Conference on Automation and Logistics, Zhengzhou, China.
Crossref
 

Zhang W, Hiyama K, Kato S, et al. (2013). Building energy simulation considering spatial temperature distribution for nonuniform indoor environment. Building and Environment, 63: 89–96.
Crossref Google Scholar
 

Zhang Z, Xu Y, Yang J, et al. (2015). A survey of sparse representation: algorithms and applications. IEEE Access, 3: 490–530.
Crossref Google Scholar
 

Zhang L, Chen Z, Zhang X, et al. (2023). Challenges and opportunities of machine learning control in building operations. Building Simulation, 16: 831–852.
Crossref Google Scholar
 

Zhao Y, Chen L, Philip Chen CL (2021). Laplacian regularized nonnegative representation for clustering and dimensionality reduction. IEEE Transactions on Circuits and Systems for Video Technology, 31: 1–14.
Crossref Google Scholar
 

Zheng J, Yu T, Lei B, et al. (2023). Evaluation of the thermal performance of radiant floor heating system with the influence of unevenly distributed solar radiation based on the theory of discretization. Building Simulation, 16: 105–120.
Crossref Google Scholar
 

Zhou P, Wang S, Zhou J, et al. (2023). A modelling method for large-scale open spaces orientated toward coordinated control of multiple air-terminal units. Building Simulation, 16: 225–241.
Crossref Google Scholar
Building Simulation
Volume 17 Issue 6,
June 2024
Pages 983-1006
DOI: 10.1007/s12273-024-1117-6
Cite this article:
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. Building Simulation, 2024, 17(6): 983-1006. https://doi.org/10.1007/s12273-024-1117-6

191

Views

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 24 November 2023
Revised: 04 February 2024
Accepted: 18 February 2024
Published: 18 March 2024
© Tsinghua University Press 2024
Return