Digital twin for healthy indoor environment: A vision for the post-pandemic era
Jiannan CAI, Jianli CHEN, Yuqing HU, Shuai LI, Qiang HE
Digital twin for healthy indoor environment: A vision for the post-pandemic era
Indoor environment has significant impacts on human health as people spend 90% of their time indoors. The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment. The advancement in emerging digital twin technologies including building information modeling (BIM), Internet of Things (IoT), data analytics, and smart control have led to new opportunities for building design and operation. Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management, very few have focused on the health of indoor environment. There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment. Therefore, this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health. Specifically, the current applications of BIM, IoT sensing, data analytics, and smart building control technologies for building digital twins are reviewed, and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health. Moreover, this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues. This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.
digital twin / healthy indoor environment / building information modeling / occupant–building interaction / COVID-19
[1] |
Adams, R I Bhangar, S Dannemiller, K C Eisen, J A Fierer, N Gilbert, J A Green, J L Marr, L C Miller, S L Siegel, J A Stephens, B Waring, M S Bibby, K (2016). Ten questions concerning the microbiomes of buildings. Building and Environment, 109: 224–234
CrossRef
Google scholar
|
[2] |
Adams, R I Miletto, M Lindow, S E Taylor, J W Bruns, T D (2014). Airborne bacterial communities in residences: Similarities and differences with fungi. PLoS One, 9( 3): e91283
CrossRef
Google scholar
|
[3] |
Afroz, Z Higgins, G Shafiullah, G M Urmee, T (2020). Evaluation of real-life demand-controlled ventilation from the perception of indoor air quality with probable implications. Energy and Buildings, 219: 110018
CrossRef
Google scholar
|
[4] |
Agostinelli, S Cumo, F Guidi, G Tomazzoli, C (2021). Cyber-physical systems improving building energy management: Digital twin and artificial intelligence. Energies, 14( 8): 2338
CrossRef
Google scholar
|
[5] |
Alavi, H Forcada, N Bortolini, R Edwards, D J (2021). Enhancing occupants’ comfort through BIM-based probabilistic approach. Automation in Construction, 123: 103528
CrossRef
Google scholar
|
[6] |
AlbadiM HEl-SaadanyE F (2007). Demand response in electricity markets: An overview. In: IEEE Power Engineering Society General Meeting. Tampa, FL: IEEE, 1–5
|
[7] |
Alimohammadisagvand, B Jokisalo, J Sirén, K (2018). Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building. Applied Energy, 209: 167–179
CrossRef
Google scholar
|
[8] |
Alsaleem, F Tesfay, M K Rafaie, M Sinkar, K Besarla, D Arunasalam, P (2020). An IoT framework for modeling and controlling thermal comfort in buildings. Frontiers in Built Environment, 6: 87
CrossRef
Google scholar
|
[9] |
Azar, E O’Brien, W Carlucci, S Hong, T Sonta, A Kim, J Andargie, M S Abuimara, T El, Asmar M Jain, R K Ouf, M M Tahmasebi, F Zhou, J (2020). Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications. Energy and Buildings, 224: 110292
CrossRef
Google scholar
|
[10] |
Benammar, M Abdaoui, A Ahmad, S H M Touati, F Kadri, A (2018). A modular IoT platform for real-time indoor air quality monitoring. Sensors, 18( 2): 581
CrossRef
Google scholar
|
[11] |
Biyik, E Kahraman, A (2019). A predictive control strategy for optimal management of peak load, thermal comfort, energy storage and renewables in multi-zone buildings. Journal of Building Engineering, 25: 100826
CrossRef
Google scholar
|
[12] |
Blasco, C Monreal, J Benftez, I Lluna, A (2012). Modelling and PID control of HVAC system according to energy efficiency and comfort criteria. In: Proceedings of the 3rd International Conference on Sustainability in Energy and Buildings. Marseilles: Springer, 365–374
|
[13] |
Boje, C Guerriero, A Kubicki, S Rezgui, Y (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114: 103179
CrossRef
Google scholar
|
[14] |
Cai, J Cai, H (2020). Robust hybrid approach of vision-based tracking and radio-based identification and localization for 3D tracking of multiple construction workers. Journal of Computing in Civil Engineering, 34( 4): 04020021
CrossRef
Google scholar
|
[15] |
Candanedo, L M Feldheim, V (2016). Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy and Buildings, 112: 28–39
CrossRef
Google scholar
|
[16] |
Candanedo, L M Feldheim, V Deramaix, D (2017). A methodology based on Hidden Markov Models for occupancy detection and a case study in a low energy residential building. Energy and Buildings, 148: 327–341
CrossRef
Google scholar
|
[17] |
Cao, Q Li, F Zhang, T Wang, S (2020). A ventilator that responds to outdoor conditions for ventilation and air filtration. Energy and Buildings, 229: 110498
CrossRef
Google scholar
|
[18] |
Castilla, M Álvarez, J D Berenguel, M Rodríguez, F Guzmán, J L Pérez, M (2011). A comparison of thermal comfort predictive control strategies. Energy and Buildings, 43( 10): 2737–2746
CrossRef
Google scholar
|
[19] |
Chen, J Augenbroe, G Song, X (2018a). Lighted-weighted model predictive control for hybrid ventilation operation based on clusters of neural network models. Automation in Construction, 89: 250–265
CrossRef
Google scholar
|
[20] |
Chen, K Zhang, D Yao, L Guo, B Yu, Z Liu, Y (2021). Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities. ACM Computing Surveys, 54( 4): 77
CrossRef
Google scholar
|
[21] |
Chen, X Wang, Q Srebric, J (2015a). A data-driven state-space model of indoor thermal sensation using occupant feedback for low-energy buildings. Energy and Buildings, 91: 187–198
CrossRef
Google scholar
|
[22] |
Chen, X Wang, Q Srebric, J (2015b). Model predictive control for indoor thermal comfort and energy optimization using occupant feedback. Energy and Buildings, 102: 357–369
CrossRef
Google scholar
|
[23] |
Chen, Z Jiang, C Xie, L (2018b). Building occupancy estimation and detection: A review. Energy and Buildings, 169: 260–270
CrossRef
Google scholar
|
[24] |
Chen, Z Jiang, C Xie, L (2019a). A novel ensemble ELM for human activity recognition using smartphone sensors. IEEE Transactions on Industrial Informatics, 15( 5): 2691–2699
CrossRef
Google scholar
|
[25] |
Chen, Z Zhang, L Jiang, C Cao, Z Cui, W (2019b). WiFi CSI based passive human activity recognition using attention based BLSTM. IEEE Transactions on Mobile Computing, 18( 11): 2714–2724
CrossRef
Google scholar
|
[26] |
Chen, Z Zhu, Q Soh, Y C Zhang, L (2017). Robust human activity recognition using smartphone sensors via CT-PCA and online SVM. IEEE Transactions on Industrial Informatics, 13( 6): 3070–3080
CrossRef
Google scholar
|
[27] |
Cheng, Y Lin, Z Fong, A M L (2015). Effects of temperature and supply airflow rate on thermal comfort in a stratum-ventilated room. Building and Environment, 92: 269–277
CrossRef
Google scholar
|
[28] |
Cheong, K H Teo, Y H Koh, J M Acharya, U R Man Yu, S C (2020). A simulation-aided approach in improving thermal-visual comfort and power efficiency in buildings. Journal of Building Engineering, 27: 100936
CrossRef
Google scholar
|
[29] |
Cho, W Song, D Hwang, S Yun, S (2015). Energy-efficient ventilation with air-cleaning mode and demand control in a multi-residential building. Energy and Buildings, 90: 6–14
CrossRef
Google scholar
|
[30] |
Choi, H Hong, S Choi, A Sung, M (2016). Toward the accuracy of prediction for energy savings potential and system performance using the daylight responsive dimming system. Energy and Buildings, 133: 271–280
CrossRef
Google scholar
|
[31] |
Cippitelli, E Gasparrini, S Gambi, E Spinsante, S (2016). A human activity recognition system using skeleton data from RGBD sensors. Computational Intelligence and Neuroscience, 4351435
CrossRef
Google scholar
|
[32] |
Clausen, A Arendt, K Johansen, A Sangogboye, F C Kjærgaard, M B Veje, C T Jørgensen, B N (2021). A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings. Energy Informatics, 4( Suppl 2): 40
CrossRef
Google scholar
|
[33] |
Costanzo, V Donn, M (2017). Thermal and visual comfort assessment of natural ventilated office buildings in Europe and North America. Energy and Buildings, 140: 210–223
CrossRef
Google scholar
|
[34] |
Crawley, D B Lawrie, L K Winkelmann, F C Buhl, W F Huang, Y J Pedersen, C O Strand, R K Liesen, R J Fisher, D E Witte, M J Glazer, J (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33( 4): 319–331
CrossRef
Google scholar
|
[35] |
Daissaoui, A Boulmakoul, A Karim, L Lbath, A (2020). IoT and big data analytics for smart buildings: A survey. Journal of Ubiquitous Systems & Pervasive Networks, 13( 1): 27–34
CrossRef
Google scholar
|
[36] |
de Coninck, R Baetens, R Saelens, D Woyte, A Helsen, L (2014). Rule-based demand-side management of domestic hot water production with heat pumps in zero energy neighbourhoods. Journal of Building Performance Simulation, 7( 4): 271–288
CrossRef
Google scholar
|
[37] |
Ding, Z Hu, T Li, M Xu, X Zou, P X W (2019). Agent-based model for simulating building energy management in student residences. Energy and Buildings, 198: 11–27
CrossRef
Google scholar
|
[38] |
Dong, B Andrews, B Lam, K P Höynck, M Zhang, R Chiou, Y S Benitez, D (2010). An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network. Energy and Buildings, 42( 7): 1038–1046
CrossRef
Google scholar
|
[39] |
Dong, B Prakash, V Feng, F O’Neill, Z (2019). A review of smart building sensing system for better indoor environment control. Energy and Buildings, 199: 29–46
CrossRef
Google scholar
|
[40] |
Enescu, D (2017). A review of thermal comfort models and indicators for indoor environments. Renewable & Sustainable Energy Reviews, 79: 1353–1379
CrossRef
Google scholar
|
[41] |
Fan, C Yan, D Xiao, F Li, A An, J Kang, X (2021). Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches. Building Simulation, 14( 1): 3–24
CrossRef
Google scholar
|
[42] |
Ferreira, P M Ruano, A E Silva, S Conceição, E Z E (2012). Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy and Buildings, 55: 238–251
CrossRef
Google scholar
|
[43] |
Filippoupolitis, A Oliff, W Loukas, G (2017). Bluetooth low energy based occupancy detection for emergency management. In: Proceedings of the 15th International Conference on Ubiquitous Computing and Communications and the 8th International Symposium on Cyberspace and Security (IUCC-CSS). Granada: IEEE, 31–38
|
[44] |
Fisk, W J (2002). How IEQ affects health, productivity. ASHRAE Journal, 44( 5): 56–58
|
[45] |
Francisco, A Mohammadi, N Taylor, J E (2020). Smart city digital twin-enabled energy management: Toward real-time urban building energy benchmarking. Journal of Management Engineering, 36( 2): 04019045
CrossRef
Google scholar
|
[46] |
Fronczek, C F Yoon, J Y (2015). Biosensors for monitoring airborne pathogens. SLAS Technology, 20( 4): 390–410
CrossRef
Google scholar
|
[47] |
Gan, V J L Luo, H Tan, Y Deng, M Kwok, H L (2021). BIM and data-driven predictive analysis of optimum thermal comfort for indoor environment. Sensors, 21( 13): 4401
CrossRef
Google scholar
|
[48] |
Ganesh, H S Fritz, H E Edgar, T F Novoselac, A Baldea, M (2019). A model-based dynamic optimization strategy for control of indoor air pollutants. Energy and Buildings, 195: 168–179
CrossRef
Google scholar
|
[49] |
Gaur, A Scotney, B Parr, G McClean, S (2015). Smart city architecture and its applications based on IoT. Procedia Computer Science, 52( 1): 1089–1094
CrossRef
Google scholar
|
[50] |
Gentile, N Laike, T Dubois, M C (2016). Lighting control systems in individual offices rooms at high latitude: Measurements of electricity savings and occupants’ satisfaction. Solar Energy, 127: 113–123
CrossRef
Google scholar
|
[51] |
Gilbert, J A Stephens, B (2018). Microbiology of the built environment. Nature Reviews: Microbiology, 16( 11): 661–670
CrossRef
Google scholar
|
[52] |
Guyot, G Sherman, M H Walker, I S (2018). Smart ventilation energy and indoor air quality performance in residential buildings: A review. Energy and Buildings, 165: 416–430
CrossRef
Google scholar
|
[53] |
Halhoul Merabet, G Essaaidi, M Ben Haddou, M Qolomany, B Qadir, J Anan, M Al-Fuqaha, A Abid, M R Benhaddou, D (2021). Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques. Renewable & Sustainable Energy Reviews, 144: 110969
CrossRef
Google scholar
|
[54] |
Harb, H Boyanov, N Hernandez, L Streblow, R Müller, D (2016). Development and validation of grey-box models for forecasting the thermal response of occupied buildings. Energy and Buildings, 117: 199–207
CrossRef
Google scholar
|
[55] |
Hasan, M H Alsaleem, F Rafaie, M (2016). Sensitivity study for the PMV thermal comfort model and the use of wearable devices biometric data for metabolic rate estimation. Building and Environment, 110: 173–183
CrossRef
Google scholar
|
[56] |
He, S Wang, Z Wang, Z Gu, X Yan, Z (2016). Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary. Applied Thermal Engineering, 107: 37–47
CrossRef
Google scholar
|
[57] |
Hesaraki, A Holmberg, S (2015). Demand-controlled ventilation in new residential buildings: Consequences on indoor air quality and energy savings. Indoor and Built Environment, 24( 2): 162–173
CrossRef
Google scholar
|
[58] |
Hobday, R A Dancer, S J (2013). Roles of sunlight and natural ventilation for controlling infection: Historical and current perspectives. Journal of Hospital Infection, 84( 4): 271–282
CrossRef
Google scholar
|
[59] |
Horve, P F Lloyd, S Mhuireach, G A Dietz, L Fretz, M MacCrone, G van den Wymelenberg, K Ishaq, S L (2020). Building upon current knowledge and techniques of indoor microbiology to construct the next era of theory into microorganisms, health, and the built environment. Journal of Exposure Science & Environmental Epidemiology, 30( 2): 219–235
CrossRef
Google scholar
|
[60] |
Hu, D Zhong, H Li, S Tan, J He, Q (2020). Segmenting areas of potential contamination for adaptive robotic disinfection in built environments. Building and Environment, 184: 107226
CrossRef
Google scholar
|
[61] |
Ijaz, M K Zargar, B Wright, K E Rubino, J R Sattar, S A (2016). Generic aspects of the airborne spread of human pathogens indoors and emerging air decontamination technologies. American Journal of Infection Control, 44( 9): S109–S120
CrossRef
Google scholar
|
[62] |
Islam, M T Jain, S Chen, Y Chowdhury, B D B Son, Y J (2022). An agent-based simulation model to evaluate contacts, layout, and policies in entrance, exit, and seating in indoor activities under a pandemic situation. IEEE Transactions on Automation Science and Engineering, 19( 2): 603–619
CrossRef
Google scholar
|
[63] |
Jain, R K Smith, K M Culligan, P J Taylor, J E (2014). Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123: 168–178
CrossRef
Google scholar
|
[64] |
Jiang, F Ma, L Broyd, T Chen, K (2021). Digital twin and its implementations in the civil engineering sector. Automation in Construction, 130: 103838
CrossRef
Google scholar
|
[65] |
Jo, J Jo, B Kim, J Kim, S Han, W (2020). Development of an IoT-based indoor air quality monitoring platform. Journal of Sensors, 8749764
CrossRef
Google scholar
|
[66] |
Jreijiry, D Husaunndee, A Inard, C (2007). Numerical study of a hybrid ventilation system for single family houses. Solar Energy, 81( 2): 227–239
CrossRef
Google scholar
|
[67] |
Kaewunruen, S Rungskunroch, P Welsh, J (2019). A digital-twin evaluation of Net Zero Energy Building for existing buildings. Sustainability, 11( 1): 159
CrossRef
Google scholar
|
[68] |
Kandasamy, N K Karunagaran, G Spanos, C Tseng, K J Soong, B H (2018). Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting. Building and Environment, 139: 170–180
CrossRef
Google scholar
|
[69] |
Kathirgamanathan, A de Rosa, M Mangina, E Finn, D P (2021). Data-driven predictive control for unlocking building energy flexibility: A review. Renewable & Sustainable Energy Reviews, 135: 110120
CrossRef
Google scholar
|
[70] |
Khanna, A Arora, S Chhabra, A Bhardwaj, K K Sharma, D K (2019). IoT architecture for preventive energy conservation of smart buildings. In: Mittal M, Tanwar S, Agarwal B, Goyal L, eds. Energy Conservation for IoT Devices. Singapore: Springer, 179–208
|
[71] |
Kim, H Hong, T Kim, J (2019a). Automatic ventilation control algorithm considering the indoor environmental quality factors and occupant ventilation behavior using a logistic regression model. Building and Environment, 153: 46–59
CrossRef
Google scholar
|
[72] |
Kim, J Kong, M Hong, T Jeong, K Lee, M (2019b). The effects of filters for an intelligent air pollutant control system considering natural ventilation and the occupants. Science of the Total Environment, 657: 410–419
CrossRef
Google scholar
|
[73] |
Kim, W Jeon, S W Kim, Y (2016). Model-based multi-objective optimal control of a VRF (variable refrigerant flow) combined system with DOAS (dedicated outdoor air system) using genetic algorithm under heating conditions. Energy, 107: 196–204
CrossRef
Google scholar
|
[74] |
Klepeis, N E Nelson, W C Ott, W R Robinson, J P Tsang, A M Switzer, P Behar, J V Hern, S C Engelmann, W H (2001). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Journal of Exposure Science & Environmental Epidemiology, 11( 3): 231–252
CrossRef
Google scholar
|
[75] |
Kraay, A N M Hayashi, M A L Hernandez-Ceron, N Spicknall, I H Eisenberg, M C Meza, R Eisenberg, J N S (2018). Fomite-mediated transmission as a sufficient pathway: A comparative analysis across three viral pathogens. BMC Infectious Diseases, 18( 1): 540
CrossRef
Google scholar
|
[76] |
Kwok, H H L Cheng, J C P Li, A T Y Tong, J C K Lau, A K H (2020). Multi-zone indoor CFD under limited information: An approach coupling solar analysis and BIM for improved accuracy. Journal of Cleaner Production, 244: 118912
CrossRef
Google scholar
|
[77] |
Lam, K P Höynck, M Dong, B Andrews, B Chiou, Y S Zhang, R Benitez, D Choi, J (2009). Occupancy detection through an extensive environmental sensor network in an open-plan office building. In: 11th International Building Performance Simulation Association Conference. Glasgow, 1452–1459
|
[78] |
Lee, J Y Wargocki, P Chan, Y H Chen, L Tham, K W (2019). Indoor environmental quality, occupant satisfaction, and acute building-related health symptoms in Green Mark-certified compared with non-certified office buildings. Indoor Air, 29( 1): 112–129
CrossRef
Google scholar
|
[79] |
Leephakpreeda, T (2005). Adaptive occupancy-based lighting control via Grey prediction. Building and Environment, 40( 7): 881–886
CrossRef
Google scholar
|
[80] |
Li, D Menassa, C C Kamat, V R (2019). Robust non-intrusive interpretation of occupant thermal comfort in built environments with low-cost networked thermal cameras. Applied Energy, 251: 113336
CrossRef
Google scholar
|
[81] |
Li, S Xu, Y Cai, J Hu, D He, Q (2021a). Integrated environment-occupant-pathogen information modeling to assess and communicate room-level outbreak risks of infectious diseases. Building and Environment, 187: 107394
CrossRef
Google scholar
|
[82] |
Li, W Wang, S (2020). A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use. Applied Energy, 275: 115371
CrossRef
Google scholar
|
[83] |
Li, W Zhang, J Zhao, T Liang, R (2018). Experimental research of online monitoring and evaluation method of human thermal sensation in different active states based on wristband device. Energy and Buildings, 173: 613–622
CrossRef
Google scholar
|
[84] |
Li, X Wen, J (2014). Review of building energy modeling for control and operation. Renewable & Sustainable Energy Reviews, 37: 517–537
CrossRef
Google scholar
|
[85] |
Li, Y O’Neill, Z Zhang, L Chen, J Im, P DeGraw, J (2021b). Grey-box modeling and application for building energy simulations: A critical review. Renewable & Sustainable Energy Reviews, 146: 111174
CrossRef
Google scholar
|
[86] |
Liu, D Guan, X Du, Y Zhao, Q (2013). Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors. Measurement Science & Technology, 24( 7): 074023
CrossRef
Google scholar
|
[87] |
Liu, Z Zhang, A Wang, W (2020). A framework for an indoor safety management system based on digital twin. Sensors, 20( 20): 5771
CrossRef
Google scholar
|
[88] |
Lu, Q Xie, X Heaton, J Parlikad, A K Schooling, J (2020a). From BIM towards digital twin: Strategy and future development for smart asset management. In: 9th International Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. Valencia: Springer, 392–404
|
[89] |
Lu, Q Xie, X Parlikad, A K Schooling, J M (2020b). Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118: 103277
CrossRef
Google scholar
|
[90] |
Marques, G Pitarma, R (2019). mHealth: Indoor environmental quality measuring system for enhanced health and well-being based on Internet of Things. Journal of Sensor and Actuator Networks, 8( 3): 43
CrossRef
Google scholar
|
[91] |
Marques, G Pitarma, R (2020). A real-time noise monitoring system based on Internet of Things for enhanced acoustic comfort and occupational health. IEEE Access, 8: 139741–139755
CrossRef
Google scholar
|
[92] |
Marques, G Roque Ferreira, C Pitarma, R (2018). A system based on the Internet of Things for real-time particle monitoring in buildings. International Journal of Environmental Research and Public Health, 15( 4): 821
CrossRef
Google scholar
|
[93] |
May-Ostendorp, P Henze, G P Corbin, C D Rajagopalan, B Felsmann, C (2011). Model-predictive control of mixed-mode buildings with rule extraction. Building and Environment, 46( 2): 428–437
CrossRef
Google scholar
|
[94] |
Montiel-Santiago, F J Hermoso-Orzáez, M J Terrados-Cepeda, J (2020). Sustainability and energy efficiency: BIM 6D, study of the BIM methodology applied to hospital buildings, value of interior lighting and daylight in energy simulation. Sustainability, 12( 14): 5731
CrossRef
Google scholar
|
[95] |
Morgan, D T Daly, T Gallagher, J McNabola, A (2017). Reducing energy consumption and increasing filter life in HVAC systems using an aspiration efficiency reducer: Long-term performance assessment at full-scale. Journal of Building Engineering, 12: 267–274
CrossRef
Google scholar
|
[96] |
Motamedi, H Shirzadi, M Tominaga, Y Mirzaei, P A (2022). CFD modeling of airborne pathogen transmission of COVID-19 in confined spaces under different ventilation strategies. Sustainable Cities and Society, 76: 103397
CrossRef
Google scholar
|
[97] |
Mujan, I Anđelković, A S Munćan, V Kljajić, M Ružić, D (2019). Influence of indoor environmental quality on human health and productivity: A review. Journal of Cleaner Production, 217: 646–657
CrossRef
Google scholar
|
[98] |
Mumma, S A (2004). Transient occupancy ventilation by monitoring CO2. IAQ Applications, 5( 1): 21–23
|
[99] |
Nassif, N (2012). A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems. Energy and Buildings, 45: 72–81
CrossRef
Google scholar
|
[100] |
Navada, S G Adiga, C S Kini, S G (2013). A study on daylight integration with thermal comfort for energy conservation in a general office. International Journal of Electrical Energy, 1( 1): 18–22
CrossRef
Google scholar
|
[101] |
Oldewurtel, F Parisio, A Jones, C N Gyalistras, D Gwerder, M Stauch, V Lehmann, B Morari, M (2012). Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45: 15–27
CrossRef
Google scholar
|
[102] |
Palmisani, J Di Gilio, A Viana, M de Gennaro, G Ferro, A (2021). Indoor air quality evaluation in oncology units at two European hospitals: Low-cost sensors for TVOCs, PM2.5 and CO2 real-time monitoring. Building and Environment, 205: 108237
CrossRef
Google scholar
|
[103] |
Pan, Y Zhang, L (2021). A BIM-data mining integrated digital twin framework for advanced project management. Automation in Construction, 124: 103564
CrossRef
Google scholar
|
[104] |
Pavlin, B Pernigotto, G Cappelletti, F Bison, P Vidoni, R Gasparella, A (2017). Real-time monitoring of occupants’ thermal comfort through infrared imaging: A preliminary study. Buildings, 7( 1): 10
CrossRef
Google scholar
|
[105] |
Peng, S Chen, Q Liu, E (2020). The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control. Science of the Total Environment, 746: 142090
CrossRef
Google scholar
|
[106] |
Piscitelli, M S Brandi, S Capozzoli, A Xiao, F (2021). A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings. Building Simulation, 14( 1): 131–147
CrossRef
Google scholar
|
[107] |
Popkin, B M (1999). Urbanization, lifestyle changes and the nutrition transition. World Development, 27( 11): 1905–1916
CrossRef
Google scholar
|
[108] |
Pramanik, P K D Upadhyaya, B K Pal, S Pal, T (2019). Internet of Things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare. In: Dey N, Ashour A S, Bhatt C, Fong S J, eds. Healthcare Data Analytics and Management: A Volume in Advances in Ubiquitous Sensing Applications For Healthcare. Chennai: Academic Press, 1–58
|
[109] |
Prussin, II A J Schwake, D O Marr, L C (2017). Ten questions concerning the aerosolization and transmission of Legionella in the built environment. Building and Environment, 123: 684–695
CrossRef
Google scholar
|
[110] |
Qian, H Li, Y Seto, W H Ching, P Ching, W H Sun, H Q (2010). Natural ventilation for reducing airborne infection in hospitals. Building and Environment, 45( 3): 559–565
CrossRef
Google scholar
|
[111] |
Qian, H Zheng, X (2018). Ventilation control for airborne transmission of human exhaled bio-aerosols in buildings. Journal of Thoracic Disease, 10( S9): S2295–S2304
CrossRef
Google scholar
|
[112] |
Rajith, A Soki, S Hiroshi, M (2018). Real-time optimized HVAC control system on top of an IoT framework. In: 3rd International Conference on Fog and Mobile Edge Computing (FMEC). Barcelona: IEEE, 181–186
|
[113] |
Raykov, Y P Ozer, E Dasika, G Boukouvalas, A Little, M A (2016). Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. Heidelberg: Association for Computing Machinery, 1016–1027
|
[114] |
Rice, L (2021). Healthy BIM: The feasibility of integrating architecture health indicators using a building information model (BIM) computer system. Archnet-IJAR, 15( 1): 252–265
CrossRef
Google scholar
|
[115] |
Rockett, P Hathway, E A (2017). Model-predictive control for non-domestic buildings: A critical review and prospects. Building Research and Information, 45( 5): 556–571
CrossRef
Google scholar
|
[116] |
Ruano, A Silva, S Duarte, H Ferreira, P M (2018). Wireless sensors and IoT platform for intelligent HVAC control. Applied Sciences, 8( 3): 370
CrossRef
Google scholar
|
[117] |
Ruusu, R Cao, S Manrique Delgado, B Hasan, A (2019). Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller. Energy Conversion and Management, 180: 1109–1128
CrossRef
Google scholar
|
[118] |
Saha, H Florita, A R Henze, G P Sarkar, S (2019). Occupancy sensing in buildings: A review of data analytics approaches. Energy and Buildings, 188–189: 278–285
CrossRef
Google scholar
|
[119] |
Saini, J Dutta, M Marques, G (2020). A comprehensive review on indoor air quality monitoring systems for enhanced public health. Sustainable Environment Research, 30( 1): 6
CrossRef
Google scholar
|
[120] |
Salakij, S Yu, N Paolucci, S Antsaklis, P (2016). Model-based predictive control for building energy management, I: Energy modeling and optimal control. Energy and Buildings, 133: 345–358
CrossRef
Google scholar
|
[121] |
Salamone, F Belussi, L Currò, C Danza, L Ghellere, M Guazzi, G Lenzi, B Megale, V Meroni, I (2018). Application of IoT and Machine Learning techniques for the assessment of thermal comfort perception. Energy Procedia, 148: 798–805
CrossRef
Google scholar
|
[122] |
Schirrer, A Brandstetter, M Leobner, I Hauer, S Kozek, M (2016). Nonlinear model predictive control for a heating and cooling system of a low-energy office building. Energy and Buildings, 125: 86–98
CrossRef
Google scholar
|
[123] |
Schulze, T Eicker, U (2013). Controlled natural ventilation for energy efficient buildings. Energy and Buildings, 56: 221–232
CrossRef
Google scholar
|
[124] |
Serale, G Fiorentini, M Capozzoli, A Bernardini, D Bemporad, A (2018). Model Predictive Control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. Energies, 11( 3): 631
CrossRef
Google scholar
|
[125] |
Sharma, A Saxena, A Sethi, M Shree, V Goel, V (2011). Life cycle assessment of buildings: A review. Renewable & Sustainable Energy Reviews, 15( 1): 871–875
CrossRef
Google scholar
|
[126] |
Shi, Z Lu, Z Chen, Q (2019). Indoor airflow and contaminant transport in a room with coupled displacement ventilation and passive-chilled-beam systems. Building and Environment, 161: 106244
CrossRef
Google scholar
|
[127] |
Silvestre-Blanes, J Pérez-Lloréns, R (2011). Energy efficiency improvements through surveillance applications in industrial buildings. Energy and Buildings, 43( 6): 1334–1340
CrossRef
Google scholar
|
[128] |
Singh, S K Jeong, Y S Park, J H (2020). A deep learning-based IoT-oriented infrastructure for secure smart city. Sustainable Cities and Society, 60: 102252
CrossRef
Google scholar
|
[129] |
Široký, J Oldewurtel, F Cigler, J Prívara, S (2011). Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy, 88( 9): 3079–3087
CrossRef
Google scholar
|
[130] |
Sporr, A Zucker, G Hofmann, R (2019). Automated HVAC control creation based on building information modeling (BIM): Ventilation system. IEEE Access, 7: 74747–74758
CrossRef
Google scholar
|
[131] |
Topak, F Pekeriçli, M K Tanyer, A M (2018). Technological viability assessment of bluetooth low energy technology for indoor localization. Journal of Computing in Civil Engineering, 32( 5): 04018034
CrossRef
Google scholar
|
[132] |
TRNSYS (2019). TRNSYS Transient System Simulation Tool
|
[133] |
Uǧursal, A Culp, C H (2013). The effect of temperature, metabolic rate and dynamic localized airflow on thermal comfort. Applied Energy, 111: 64–73
CrossRef
Google scholar
|
[134] |
US Environmental Protection Agency (2019). Benefits Mapping and Analysis Program (BenMAP)
|
[135] |
Utkucu, D Sözer, H (2020). Interoperability and data exchange within BIM platform to evaluate building energy performance and indoor comfort. Automation in Construction, 116: 103225
CrossRef
Google scholar
|
[136] |
Valinejadshoubi, M Moselhi, O Bagchi, A Salem, A (2021). Development of an IoT and BIM-based automated alert system for thermal comfort monitoring in buildings. Sustainable Cities and Society, 66: 102602
CrossRef
Google scholar
|
[137] |
Verma, A Prakash, S Srivastava, V Kumar, A Mukhopadhyay, S C (2019). Sensing, controlling, and IoT infrastructure in smart building: A review. IEEE Sensors Journal, 19( 20): 9036–9046
CrossRef
Google scholar
|
[138] |
Wahl, F Milenkovic, M Amft, O (2012). A distributed PIR-based approach for estimating people count in office environments. In: Proceedings of the 15th IEEE International Conference on Computational Science and Engineering (CSE) and the 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC). Paphos: IEEE, 640–647
|
[139] |
Walker, I S Sherman, M H (2013). Effect of ventilation strategies on residential ozone levels. Building and Environment, 59: 456–465
CrossRef
Google scholar
|
[140] |
Wang, E (2015). Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach. Applied Energy, 146: 92–103
CrossRef
Google scholar
|
[141] |
Wang, J Huang, J Feng, Z Cao, S J Haghighat, F (2021). Occupant-density-detection based energy efficient ventilation system: Prevention of infection transmission. Energy and Buildings, 240: 110883
CrossRef
Google scholar
|
[142] |
Wang, W Chen, J Song, X (2017a). Modeling and predicting occupancy profile in office space with a Wi-Fi probe-based Dynamic Markov Time-Window Inference approach. Building and Environment, 124: 130–142
CrossRef
Google scholar
|
[143] |
Wang, W Liu, A X Shahzad, M Ling, K Lu, S (2017b). Device-free human activity recognition using commercial WiFi devices. IEEE Journal on Selected Areas in Communications, 35( 5): 1118–1131
CrossRef
Google scholar
|
[144] |
Wang, W Wang, J Chen, J Huang, G Guo, X (2018). Multi-zone outdoor air coordination through Wi-Fi probe-based occupancy sensing. Energy and Buildings, 159: 495–507
CrossRef
Google scholar
|
[145] |
Wang, X (2012). BIM handbook: A guide to Building Information Modeling for owners, managers, designers, engineers and contractors. Construction Economics and Building, 12( 3): 101–102
CrossRef
Google scholar
|
[146] |
Wang, Z Chen, Y (2019). Data-driven modeling of building thermal dynamics: Methodology and state of the art. Energy and Buildings, 203: 109405
CrossRef
Google scholar
|
[147] |
West, S R Ward, J K Wall, J (2014). Trial results from a model predictive control and optimisation system for commercial building HVAC. Energy and Buildings, 72: 271–279
CrossRef
Google scholar
|
[148] |
World Health Organization (2021). Air Pollution
|
[149] |
Worldometer
|
[150] |
Wu, I C Liu, C C (2020). A visual and persuasive energy conservation system based on BIM and IoT technology. Sensors, 20( 1): 139
CrossRef
Google scholar
|
[151] |
Wu, T Wu, F Redoute, J M Yuce, M R (2017). An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access, 5: 11413–11422
CrossRef
Google scholar
|
[152] |
Xi, X C Poo, A N Chou, S K (2007). Support vector regression model predictive control on a HVAC plant. Control Engineering Practice, 15( 8): 897–908
CrossRef
Google scholar
|
[153] |
Xia, Y Ding, Q Li, Z Jiang, A (2021). Fault detection for centrifugal chillers using a Kernel Entropy Component Analysis (KECA) method. Building Simulation, 14( 1): 53–61
CrossRef
Google scholar
|
[154] |
Xie, H Yu, B Wang, J Ji, J (2021). A novel disinfected Trombe wall for space heating and virus inactivation: Concept and performance investigation. Applied Energy, 291: 116789
CrossRef
Google scholar
|
[155] |
Xu, Y Cai, J Li, S He, Q Zhu, S (2021). Airborne infection risks of SARS-CoV-2 in US schools and impacts of different intervention strategies. Sustainable Cities and Society, 74: 103188
CrossRef
Google scholar
|
[156] |
Yang, S Wan, M P Ng, B F Zhang, T Babu, S Zhang, Z Chen, W Dubey, S (2018a). A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings. Energy and Buildings, 170: 25–39
CrossRef
Google scholar
|
[157] |
Yang, W Moon, H J (2019). Combined effects of acoustic, thermal, and illumination conditions on the comfort of discrete senses and overall indoor environment. Building and Environment, 148: 623–633
CrossRef
Google scholar
|
[158] |
Yang, Z Roth, J Jain, R K (2018b). DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis. Energy and Buildings, 163: 58–69
CrossRef
Google scholar
|
[159] |
Yu, Z Haghighat, F Fung, B C M Yoshino, H (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42( 10): 1637–1646
CrossRef
Google scholar
|
[160] |
Zahid, H Elmansoury, O Yaagoubi, R (2021). Dynamic predicted mean vote: An IoT-BIM integrated approach for indoor thermal comfort optimization. Automation in Construction, 129: 103805
CrossRef
Google scholar
|
[161] |
Zeng, L Gao, J Lv, L Zhang, R Tong, L Zhang, X Huang, Z Zhang, Z (2020). Markov-chain-based probabilistic approach to optimize sensor network against deliberately released pollutants in buildings with ventilation systems. Building and Environment, 168: 106534
CrossRef
Google scholar
|
[162] |
Zerrouki, N Harrou, F Sun, Y Houacine, A (2018). Vision-based human action classification using adaptive boosting algorithm. IEEE Sensors Journal, 18( 12): 5115–5121
CrossRef
Google scholar
|
[163] |
Zhan, S Chong, A (2021). Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective. Renewable & Sustainable Energy Reviews, 142: 110835
CrossRef
Google scholar
|
[164] |
Zhang, L Wen, J Li, Y Chen, J Ye, Y Fu, Y Livingood, W (2021a). A review of machine learning in building load prediction. Applied Energy, 285: 116452
CrossRef
Google scholar
|
[165] |
Zhang, N Chen, W Chan, P T Yen, H L Tang, J W T Li, Y (2020). Close contact behavior in indoor environment and transmission of respiratory infection. Indoor Air, 30( 4): 645–661
CrossRef
Google scholar
|
[166] |
Zhang, N Li, Y Huang, H (2018). Surface touch and its network growth in a graduate student office. Indoor Air, 28( 6): 963–972
CrossRef
Google scholar
|
[167] |
Zhang, S Ai, Z Lin, Z (2021b). Occupancy-aided ventilation for both airborne infection risk control and work productivity. Building and Environment, 188: 107506
CrossRef
Google scholar
|
[168] |
Zhang, T Siebers, P O Aickelin, U (2011). Modelling electricity consumption in office buildings: An agent based approach. Energy and Buildings, 43( 10): 2882–2892
CrossRef
Google scholar
|
[169] |
Zhao, J Frumkin, N Ishwar, P Konrad, J (2019a). CNN-based indoor occupant localization via active scene illumination. In: Proceedings of the International Conference on Image Processing (ICIP). Taipei: IEEE, 2636–2640
|
[170] |
ZhaoYTuPChangM C (2019b). Occupancy sensing and activity recognition with cameras and wireless sensors. In: Proceedings of the 2nd Workshop on Data Acquisition to Analysis. New York, NY: Association for Computing Machinery, 1–6
|
[171] |
Zhong, B Gan, C Luo, H Xing, X (2018). Ontology-based framework for building environmental monitoring and compliance checking under BIM environment. Building and Environment, 141: 127–142
CrossRef
Google scholar
|
[172] |
Zhou, X Xu, L Zhang, J Niu, B Luo, M Zhou, G Zhang, X (2020a). Data-driven thermal comfort model via support vector machine algorithms: Insights from ASHRAE RP-884 database. Energy and Buildings, 211: 109795
CrossRef
Google scholar
|
[173] |
Zhou, Y Nikolaev, A Bian, L Lin, L Li, L (2021). Investigating transmission dynamics of influenza in a public indoor venue: An agent-based modeling approach. Computers & Industrial Engineering, 157: 107327
CrossRef
Google scholar
|
[174] |
Zhou, Y W Hu, Z Z Lin, J R Zhang, J P (2020b). A review on 3D spatial data analytics for Building Information Models. Archives of Computational Methods in Engineering, 27( 5): 1449–1463
CrossRef
Google scholar
|
[175] |
Zhuang, R Li, X Tu, J (2014). CFD study of the effects of furniture layout on indoor air quality under typical office ventilation schemes. Building Simulation, 7( 3): 263–275
CrossRef
Google scholar
|
[176] |
Zhuang, Y Yang, J Li, Y Qi, L El-Sheimy, N (2016). Smartphone-based indoor localization with bluetooth low energy beacons. Sensors, 16( 5): 596
CrossRef
Google scholar
|
[177] |
Zou, H Jiang, H Yang, J Xie, L Spanos, C J (2017). Non-intrusive occupancy sensing in commercial buildings. Energy and Buildings, 154: 633–643
CrossRef
Google scholar
|
[178] |
Zou, H Zhou, Y Jiang, H Chien, S C Xie, L Spanos, C J (2018). WinLight: A WiFi-based occupancy-driven lighting control system for smart building. Energy and Buildings, 158: 924–938
CrossRef
Google scholar
|
/
〈 | 〉 |