References:

Amayri, M., Arora, A., Ploix, S., Bandhyopadyay, S., Ngo, Q.-D., & Badarla, V. R. (2016). Estimating occupancy in heterogeneous sensor environment. Energy and Buildings, 129, 46–58. https://doi.org/10.1016/j.enbuild.2016.07.026
Ansanay-Alex, G. (2013). Estimating Occupancy Using Indoor Carbon Dioxide Concen- trations Only in an Office Building: A Method and Qualitative Assessment,. 1–8.
Apostolo, G. H., Bernardini, F., Magalhaes, L. C. S., & Muchaluat-Saade, D. C. (2021). A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings. IEEE Access, 9, 11455–11469. https://doi.org/10.1109/ACCESS.2020.3048891
Arief-Ang, I. B., Hamilton, M., & Salim, F. D. (2018). A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO 2 Sensor Data. ACM Transactions on Sensor Networks, 14(3–4), 1–28. https://doi.org/10.1145/3217214
Ashouri, A., Newsham, G. R., Shi, Z., & Gunay, H. B. (2019). Day-ahead Prediction of Building Occupancy using WiFi Signals. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 1237–1242. https://doi.org/10.1109/COASE.2019.8843224
ASHRAE. (2019). ANSI/ASHRAE Standard 90.1-2016 Energy Standard for Buildings except Low-Rise Residential Buildings.
Azam, M., Blayo, M., Venne, J.-S., & Allegue-Martinez, M. (2019). Occupancy Estimation Using Wifi Motion Detection via Supervised Machine Learning Algorithms. 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 1–5. https://doi.org/10.1109/GlobalSIP45357.2019.8969297
Balaji, B., Xu, J., Nwokafor, A., Gupta, R., & Agarwal, Y. (2013). Sentinel: Occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings. Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems - SenSys ’13, 1–14. https://doi.org/10.1145/2517351.2517370
Capozzoli, A., Piscitelli, M. S., Gorrino, A., Ballarini, I., & Corrado, V. (2017). Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings. Sustainable Cities and Society, 35, 191–208. https://doi.org/10.1016/j.scs.2017.07.016
Chen, S., Zhang, G., Xia, X., Chen, Y., Setunge, S., & Shi, L. (2021). The impacts of occupant behavior on building energy consumption: A review. Sustainable Energy Technologies and Assessments, 45, 101212. https://doi.org/10.1016/j.seta.2021.101212
Chen, Z., Jiang, C., & Xie, L. (2018). Building occupancy estimation and detection: A review. Energy and Buildings, 169, 260–270. https://doi.org/10.1016/j.enbuild.2018.03.084
Chen, Z., Masood, M. K., & Soh, Y. C. (2016). A fusion framework for occupancy estimation in office buildings based on environmental sensor data. Energy and Buildings, 133, 790–798. https://doi.org/10.1016/j.enbuild.2016.10.030
Chen, Z., & Soh, Y. C. (2017). Comparing occupancy models and data mining approaches for regular occupancy prediction in commercial buildings. Journal of Building Performance Simulation, 10(5–6), 545–553. https://doi.org/10.1080/19401493.2016.1199735
Chen, Z., Xu, J., & Soh, Y. C. (2015). Modeling regular occupancy in commercial buildings using stochastic models. Energy and Buildings, 103, 216–223. https://doi.org/10.1016/j.enbuild.2015.06.009
Chen, Z., Zhao, R., Zhu, Q., Masood, M. K., Soh, Y. C., & Mao, K. (2017). Building Occupancy Estimation with Environmental Sensors via CDBLSTM. IEEE Transactions on Industrial Electronics, 64(12), 9549–9559. https://doi.org/10.1109/TIE.2017.2711530
Chen, Z., Zhu, Q., Masood, M. K., & Soh, Y. C. (2017). Environmental Sensors-Based Occupancy Estimation in Buildings via IHMM-MLR. IEEE Transactions on Industrial Informatics, 13(5), 2184–2193. https://doi.org/10.1109/TII.2017.2668444
Chidurala, V., & Li, X. (2021). Occupancy Estimation Using Thermal Imaging Sensors and Machine Learning Algorithms. IEEE Sensors Journal, 21(6), 8627–8638. https://doi.org/10.1109/JSEN.2021.3049311
Chong, A., Augenbroe, G., & Yan, D. (2021). Occupancy data at different spatial resolutions: Building energy performance and model calibration. Applied Energy, 286, 116492. https://doi.org/10.1016/j.apenergy.2021.116492
Cisco. (2017). Cisco Prime Infrastructure 3.4 User Guide. http://www.cisco.com
Cisco, E. (2014). Cisco Aironet Series 1700/2700/3700 Access Point Deployment Guide. http://www.cisco.com
Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. https://doi.org/10.1016/j.enbuild.2020.110159
Di Domenico, S., De Sanctis, M., Cianca, E., & Bianchi, G. (2016). A Trained-once Crowd Counting Method Using Differential WiFi Channel State Information. Proceedings of the 3rd International on Workshop on Physical Analytics - WPA ’16, 37–42. https://doi.org/10.1145/2935651.2935657
Ding, Y., Chen, W., Wei, S., & Yang, F. (2021). An occupancy prediction model for campus buildings based on the diversity of occupancy patterns. Sustainable Cities and Society, 64, 102533. https://doi.org/10.1016/j.scs.2020.102533
D’Oca, S., & Hong, T. (2015). Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88, 395–408. https://doi.org/10.1016/j.enbuild.2014.11.065
Ekwevugbe, T., Brown, N., Pakka, V., & Fan, D. (2017). Improved occupancy monitoring in non-domestic buildings. Sustainable Cities and Society, 30, 97–107. https://doi.org/10.1016/j.scs.2017.01.003
Erickson, V., Carreira-Perpinan, M. A., & Cerpa, A. (2011). OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. 258–269.
Esrafilian-Najafabadi, M., & Haghighat, F. (2021). Occupancy-based HVAC control systems in buildings: A state-of-the-art review. Building and Environment, 197, 107810. https://doi.org/10.1016/j.buildenv.2021.107810
Franco, A., & Leccese, F. (2020). Measurement of CO2 concentration for occupancy estimation in educational buildings with energy efficiency purposes. Journal of Building Engineering, 32, 101714. https://doi.org/10.1016/j.jobe.2020.101714
Gruber, M., Trüschel, A., & Dalenbäck, J.-O. (2014). CO 2 sensors for occupancy estimations: Potential in building automation applications. Energy and Buildings, 84, 548–556. https://doi.org/10.1016/j.enbuild.2014.09.002
Gul, M. S., & Patidar, S. (2015). Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy and Buildings, 87, 155–165. https://doi.org/10.1016/j.enbuild.2014.11.027
Gunay, B., Nagy, Z., Miller, C., Ouf, M. M., & Dong, B. (2021). Using Occupant-Centric Control for Commercial HVAC Systems. ASHRAE Journal, 63(5), 30-32,34-36,38-40.
Guo, X., Tiller, D., Henze, G., & Waters, C. (2010). The performance of occupancy-based lighting control systems: A review. Lighting Research & Technology, 42(4), 415–431. https://doi.org/10.1177/1477153510376225
Heinzl, H., & Mittlböck, M. (2003). Pseudo R-squared measures for Poisson regression models with over- or underdispersion. Computational Statistics & Data Analysis, 44(1–2), 253–271. https://doi.org/10.1016/S0167-9473(03)00062-8
Hobson, B. W., Gunay, H. B., Ashouri, A., & Newsham, G. R. (2020). Clustering and motif identification for occupancy-centric control of an air handling unit. Energy and Buildings, 223, 110179. https://doi.org/10.1016/j.enbuild.2020.110179
Hobson, B. W., Lowcay, D., Gunay, H. B., Ashouri, A., & Newsham, G. R. (2019). Opportunistic occupancy-count estimation using sensor fusion: A case study. Building and Environment, 159, 106154. https://doi.org/10.1016/j.buildenv.2019.05.032
Hong, T., Yan, D., D’Oca, S., & Chen, C. (2017). Ten questions concerning occupant behavior in buildings: The big picture. Building and Environment, 114, 518–530. https://doi.org/10.1016/j.buildenv.2016.12.006
Hou, H., Pawlak, J., Sivakumar, A., Howard, B., & Polak, J. (2020). An approach for building occupancy modelling considering the urban context. Building and Environment, 183, 107126. https://doi.org/10.1016/j.buildenv.2020.107126
Jagadeesh Simma, K. C., Mammoli, A., & Bogus, S. M. (2019). Real-Time Occupancy Estimation Using WiFi Network to Optimize HVAC Operation. Procedia Computer Science, 155, 495–502. https://doi.org/10.1016/j.procs.2019.08.069
Jiang, C., Chen, Z., Su, R., Masood, M. K., & Soh, Y. C. (2020). Bayesian filtering for building occupancy estimation from carbon dioxide concentration. Energy and Buildings, 206, 109566. https://doi.org/10.1016/j.enbuild.2019.109566
Jiang, C., Masood, M. K., Soh, Y. C., & Li, H. (2016). Indoor occupancy estimation from carbon dioxide concentration. Energy and Buildings, 131, 132–141. https://doi.org/10.1016/j.enbuild.2016.09.002
Jin, M., Bekiaris-Liberis, N., Weekly, K., Spanos, C. J., & Bayen, A. M. (2018). Occupancy Detection via Environmental Sensing. IEEE Transactions on Automation Science and Engineering, 15(2), 443–455. https://doi.org/10.1109/TASE.2016.2619720
Jung, W., & Jazizadeh, F. (2019). Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions. Applied Energy, 239, 1471–1508. https://doi.org/10.1016/j.apenergy.2019.01.070
Kim, S., Sung, Y., Sung, Y., & Seo, D. (2019). Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools. Energies, 12(3), 433. https://doi.org/10.3390/en12030433
Li, N., Calis, G., & Becerik-Gerber, B. (2012). Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations. Automation in Construction, 24, 89–99. https://doi.org/10.1016/j.autcon.2012.02.013
Li, Z., & Dong, B. (2018). Short term predictions of occupancy in commercial buildings—Performance analysis for stochastic models and machine learning approaches. Energy and Buildings, 158, 268–281. https://doi.org/10.1016/j.enbuild.2017.09.052
Liang, X., Hong, T., & Shen, G. Q. (2016). Occupancy data analytics and prediction: A case study. Building and Environment, 102, 179–192. https://doi.org/10.1016/j.buildenv.2016.03.027
Liu, D., Guan, X., Du, Y., & Zhao, Q. (2013). Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors. Measurement Science and Technology, 24(7), 074023. https://doi.org/10.1088/0957-0233/24/7/074023
Longo, E., Redondi, A. E. C., & Cesana, M. (2019). Accurate occupancy estimation with WiFi and bluetooth/BLE packet capture. Computer Networks, 163, 106876. https://doi.org/10.1016/j.comnet.2019.106876
Mahdavi, A., & Tahmasebi, F. (2015). Predicting people’s presence in buildings: An empirically based model performance analysis. Energy and Buildings, 86, 349–355. https://doi.org/10.1016/j.enbuild.2014.10.027
Masood, M. K., Chai Soh, Y., & Chang, V. W.-C. (2015). Real-time occupancy estimation using environmental parameters. 2015 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN.2015.7280781
Masood, M. K., Soh, Y. C., & Jiang, C. (2017). Occupancy estimation from environmental parameters using wrapper and hybrid feature selection. Applied Soft Computing, 60, 482–494. https://doi.org/10.1016/j.asoc.2017.07.003
Melfi, R., Rosenblum, B., Nordman, B., & Christensen, K. (2011). Measuring building occupancy using existing network infrastructure. 2011 International Green Computing Conference and Workshops, 1–8. https://doi.org/10.1109/IGCC.2011.6008560
Meng, Y., Li, T., Liu, G., Xu, S., & Ji, T. (2020). Real-time dynamic estimation of occupancy load and an air-conditioning predictive control method based on image information fusion. Building and Environment, 173, 106741. https://doi.org/10.1016/j.buildenv.2020.106741
Mohottige, I. P., Sutjarittham, T., Raju, N., Gharakheili, H. H., & Sivaraman, V. (2018). Role of Campus WiFi Infrastructure for Occupancy Monitoring in a Large University. 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), 1–5. https://doi.org/10.1109/ICIAFS.2018.8913341
Nassif, N. (2012). A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems. Energy and Buildings, 45, 72–81. https://doi.org/10.1016/j.enbuild.2011.10.018
Natural Resources Canada (NRCan). (2018). Energy Efficiency Trends Analysis Tables. https://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/data_e/databases.cfm?attr=0
Naylor, S., Gillott, M., & Lau, T. (2018). A review of occupant-centric building control strategies to reduce building energy use. Renewable and Sustainable Energy Reviews, 96, 1–10. https://doi.org/10.1016/j.rser.2018.07.019
NECB. (2015). National Energy Code of Canada for Buildings.
O’Brien, W., Wagner, A., Schweiker, M., Mahdavi, A., Day, J., Kjærgaard, M. B., Carlucci, S., Dong, B., Tahmasebi, F., Yan, D., Hong, T., Gunay, H. B., Nagy, Z., Miller, C., & Berger, C. (2020). Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation. Building and Environment, 178, 106738. https://doi.org/10.1016/j.buildenv.2020.106738
Oppermann, M., & Munzner, T. (2020). Ocupado: Visualizing Location‐Based Counts Over Time Across Buildings. Computer Graphics Forum, 39(3), 127–138. https://doi.org/10.1111/cgf.13968
Ouf, M. M., Issa, M. H., Azzouz, A., & Sadick, A.-M. (2017). Effectiveness of using WiFi technologies to detect and predict building occupancy. Sustainable Buildings, 2, 7. https://doi.org/10.1051/sbuild/2017005
Ouf, M. M., O’Brien, W., & Gunay, B. (2019). On quantifying building performance adaptability to variable occupancy. Building and Environment, 155, 257–267. https://doi.org/10.1016/j.buildenv.2019.03.048
Page, J., Robinson, D., Morel, N., & Scartezzini, J.-L. (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40(2), 83–98. https://doi.org/10.1016/j.enbuild.2007.01.018
Park, J. Y., Ouf, M. M., Gunay, B., Peng, Y., O’Brien, W., Kjærgaard, M. B., & Nagy, Z. (2019). A critical review of field implementations of occupant-centric building controls. Building and Environment, 165, 106351. https://doi.org/10.1016/j.buildenv.2019.106351
Pasquel Mohottige, I., Gharakheili, H. H., Vishwanath, A., Kanhere, S. S., & Sivaraman, V. (2020). Evaluating Emergency Evacuation Events Using Building WiFi Data. 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI), 116–127. https://doi.org/10.1109/IoTDI49375.2020.00018
Peng, Y., Rysanek, A., Nagy, Z., & Schlüter, A. (2017). Occupancy learning-based demand-driven cooling control for office spaces. Building and Environment, 122, 145–160. https://doi.org/10.1016/j.buildenv.2017.06.010
Peng, Y., Rysanek, A., Nagy, Z., & Schlüter, A. (2018). Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Applied Energy, 211, 1343–1358. https://doi.org/10.1016/j.apenergy.2017.12.002
Petersen, S., Pedersen, T. H., Nielsen, K. U., & Knudsen, M. D. (2016). Establishing an image-based ground truth for validation of sensor data-based room occupancy detection. Energy and Buildings, 130, 787–793. https://doi.org/10.1016/j.enbuild.2016.09.009
Rafsanjani, H. N., & Ghahramani, A. (2019). Extracting occupants’ energy-use patterns from Wi-Fi networks in office buildings. Journal of Building Engineering, 26, 100864. https://doi.org/10.1016/j.jobe.2019.100864
Ravi, A., & Misra, A. (2021). Practical server-side WiFi-based indoor localization: Addressing cardinality & outlier challenges for improved occupancy estimation. Ad Hoc Networks, 115, 102443. https://doi.org/10.1016/j.adhoc.2021.102443
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. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 1016–1027. https://doi.org/10.1145/2971648.2971746
Rueda, L., Agbossou, K., Cardenas, A., Henao, N., & Kelouwani, S. (2020). A comprehensive review of approaches to building occupancy detection. Building and Environment, 180, 106966. https://doi.org/10.1016/j.buildenv.2020.106966
Salimi, S., & Hammad, A. (2019). Critical review and research roadmap of office building energy management based on occupancy monitoring. Energy and Buildings, 182, 214–241. https://doi.org/10.1016/j.enbuild.2018.10.007
Salimi, S., Liu, Z., & Hammad, A. (2019). Occupancy prediction model for open-plan offices using real-time location system and inhomogeneous Markov chain. Building and Environment, 152, 1–16. https://doi.org/10.1016/j.buildenv.2019.01.052
Seghezzi, E., Locatelli, M., Pellegrini, L., Pattini, G., Di Giuda, G. M., Tagliabue, L. C., & Boella, G. (2021). Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment. Applied Sciences, 11(7), 3108. https://doi.org/10.3390/app11073108
Shen, W., Newsham, G., & Gunay, B. (2017). Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review. Advanced Engineering Informatics, 33, 230–242. https://doi.org/10.1016/j.aei.2016.12.008
Shetty, S. S., Chinh, H. D., Gupta, M., & Panda, S. K. (2017). User Presence Estimation in Multi-Occupancy Rooms Using Plug-Load Meters and PIR Sensors. GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 1–6. https://doi.org/10.1109/GLOCOM.2017.8255036
Sobron, I., Del Ser, J., Eizmendi, I., & Velez, M. (2018). Device-Free People Counting in IoT Environments: New Insights, Results, and Open Challenges. IEEE Internet of Things Journal, 5(6), 4396–4408. https://doi.org/10.1109/JIOT.2018.2806990
Sun, K., Zhao, Q., & Zou, J. (2020). A review of building occupancy measurement systems. Energy and Buildings, 216, 109965. https://doi.org/10.1016/j.enbuild.2020.109965
Szczurek, A., Maciejewska, M., & Pietrucha, T. (2017). Occupancy determination based on time series of CO2 concentration, temperature and relative humidity. Energy and Buildings, 147, 142–154. https://doi.org/10.1016/j.enbuild.2017.04.080
Teixeira, Thiago, Dublon, Gershon, & Savvides, Andreas. (2010). A Survey of Human-Sensing: Methods for Detecting Presence, Count, Location, Track, and Identity. 5.
Tekler, Z. D., Low, R., Gunay, B., Andersen, R. K., & Blessing, L. (2020). A scalable Bluetooth Low Energy approach to identify occupancy patterns and profiles in office spaces. Building and Environment, 171, 106681. https://doi.org/10.1016/j.buildenv.2020.106681
Wahl, F., Milenkovic, M., & Amft, O. (2012). A Distributed PIR-based Approach for Estimating People Count in Office Environments. 2012 IEEE 15th International Conference on Computational Science and Engineering, 640–647. https://doi.org/10.1109/ICCSE.2012.92
Wang, F., Feng, Q., Chen, Z., Zhao, Q., Cheng, Z., Zou, J., Zhang, Y., Mai, J., Li, Y., & Reeve, H. (2017). Predictive control of indoor environment using occupant number detected by video data and CO 2 concentration. Energy and Buildings, 145, 155–162. https://doi.org/10.1016/j.enbuild.2017.04.014
Wang, H.-T., Jia, Q.-S., Song, C., Yuan, R., & Guan, X. (2014). Building occupant level estimation based on heterogeneous information fusion. Information Sciences, 272, 145–157. https://doi.org/10.1016/j.ins.2014.02.080
Wang, W., Chen, J., & Hong, T. (2018a). Modeling occupancy distribution in large spaces with multi-feature classification algorithm. Building and Environment, 137, 108–117. https://doi.org/10.1016/j.buildenv.2018.04.002
Wang, W., Chen, J., & Hong, T. (2018b). Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings. Automation in Construction, 94, 233–243. https://doi.org/10.1016/j.autcon.2018.07.007
Wang, W., Chen, J., Hong, T., & Zhu, N. (2018). Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology. Building and Environment, 138, 160–170. https://doi.org/10.1016/j.buildenv.2018.04.034
Wang, W., Chen, J., & Song, X. (2017). 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. https://doi.org/10.1016/j.buildenv.2017.08.003
Wang, W., Hong, T., Li, N., Wang, R. Q., & Chen, J. (2019). Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification. Applied Energy, 236, 55–69. https://doi.org/10.1016/j.apenergy.2018.11.079
Wang, W., Hong, T., Xu, N., Xu, X., Chen, J., & Shan, X. (2019). Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering. Building and Environment, 162, 106280. https://doi.org/10.1016/j.buildenv.2019.106280
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. https://doi.org/10.1016/j.enbuild.2017.11.041
Wang, Y., & Shao, L. (2018). Understanding occupancy and user behaviour through Wi-Fi-based indoor positioning. Building Research & Information, 46(7), 725–737. https://doi.org/10.1080/09613218.2018.1378498
Wang, Z., Hong, T., Piette, M. A., & Pritoni, M. (2019). Inferring occupant counts from Wi-Fi data in buildings through machine learning. Building and Environment, 158, 281–294. https://doi.org/10.1016/j.buildenv.2019.05.015
Weekly, K., Jin, M., Zou, H., Hsu, C., Soyza, C., Bayen, A., & Spanos, C. (2018). Building-in-Briefcase: A Rapidly-Deployable Environmental Sensor Suite for the Smart Building. Sensors, 18(5), 1381. https://doi.org/10.3390/s18051381
Wohlin, C., Höst, M., Per, R., & Wesslén, A. (2003). Software Reliability In R. Meyers (Eds.). In Encyclopedia of Physical Science and Technology (Third, pp. 25–39). Academic Press. https://doi.org/10.1016/B0-12-227410-5/00858-9
Wu, L., & Wang, Y. (2019). A Low-Power Electric-Mechanical Driving Approach for True Occupancy Detection Using a Shuttered Passive Infrared Sensor. IEEE Sensors Journal, 19(1), 47–57. https://doi.org/10.1109/JSEN.2018.2875659
Yang, J., Santamouris, M., & Lee, S. E. (2016). Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings. Energy and Buildings, 121, 344–349. https://doi.org/10.1016/j.enbuild.2015.12.019
Yang, Z., & Becerik-Gerber, B. (2014). The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use. Energy and Buildings, 78, 113–122. https://doi.org/10.1016/j.enbuild.2014.04.002
Yang, Z., Li, N., Becerik-Gerber, B., & Orosz, M. (2014). A systematic approach to occupancy modeling in ambient sensor-rich buildings. SIMULATION, 90(8), 960–977. https://doi.org/10.1177/0037549713489918
Yoo, W., Kim, H., & Shin, M. (2020). Stations-oriented indoor localization (SOIL): A BIM-Based occupancy schedule modeling system. Building and Environment, 168, 106520. https://doi.org/10.1016/j.buildenv.2019.106520
Zou, H., Jiang, H., Yang, J., Xie, L., & Spanos, C. (2017). Non-intrusive occupancy sensing in commercial buildings. Energy and Buildings, 154, 633–643. https://doi.org/10.1016/j.enbuild.2017.08.045
Zou, H., Zhou, Y., Yang, J., & Spanos, C. J. (2018). Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT. Energy and Buildings, 174, 309–322. https://doi.org/10.1016/j.enbuild.2018.06.040
Zuraimi, M. S., Pantazaras, A., Chaturvedi, K. A., Yang, J. J., Tham, K. W., & Lee, S. E. (2017). Predicting occupancy counts using physical and statistical Co2-based modeling methodologies. Building and Environment, 123, 517–528. https://doi.org/10.1016/j.buildenv.2017.07.027