How to cite this paper
Elshaboury, N. (2022). Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms.Decision Science Letters , 11(1), 91-104.
Refrences
Ahmad, J., Larijani, H., Emmanuel, R., Mannion, M., & Javed, A. (2020). Occupancy detection in non-residential buildings–A survey and novel privacy preserved occupancy monitoring solution. Applied Computing and Informatics.
Amayri, M., Ploix, S., Kazmi, H., Ngo, Q. D., & Safadi, E. L. (2019). Estimating occupancy from measurements and knowledge using the bayesian network for energy management. Journal of Sensors, 2019.
Andersen, R. (2012). The influence of occupant's behaviour on energy consumption investigated in 290 identical dwelling and 35 apartments. In: 10th International Conference on Healthy Buildings, Brisbane, Australia.
Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and information science, 3(1), 180.
Berger, C., & Mahdavi, A. (2020). Review of current trends in agent-based modeling of building occupants for energy and indoor-environmental performance analysis. Building and Environment, 173, 106726.
Brager, G., Paliaga, G., & De Dear, R. (2004). Operable windows, personal control and occupant comfort. ASHRAE Transactions, 110, 17-35.
Branco, G., Lachal, B., Gallinelli, P., & Weber, W. (2004). Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings, 36(6), 543-555.
Calì, D., Osterhage, T., Streblow, R., & Müller, D. (2016). Energy performance gap in refurbished German dwellings: Lesson learned from a field test. Energy and buildings, 127, 1146-1158.
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.
Chen, S., Yang, W., Yoshino, H., Levine, M. D., Newhouse, K., & Hinge, A. (2015). Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies. Energy and Buildings, 104, 1-13.
Chen, Z., Jiang, C., & Xie, L. (2018). Building occupancy estimation and detection: A review. Energy and Buildings, 169, 260-270.
De Simone, M., & Fajilla, G. (2018). Occupant behavior: A “new” factor in energy performance of buildings. Methods for its detection in houses and in offices. Journal of World Architecture, 2(2), 1-9.
Degelman, L.O. (1999). A model for simulation of daylighting and occupancy sensors as an energy control strategy for office buildings. In: Building simulation conference, Kyoto, Japan, pp. 571-578.
Delzendeh, E., Wu, S., Lee, A., & Zhou, Y. (2017). The impact of occupants’ behaviours on building energy analysis: A research review. Renewable and Sustainable Energy Reviews, 80, 1061-1071.
Dziedzic, J. W., Da, Y., & Novakovic, V. (2019). Indoor occupant behaviour monitoring with the use of a depth registration camera. Building and Environment, 148, 44-54.
Ekwevugbe, T., Brown, N., Pakka, V., & Fan, D. (2016). Advanced occupancy sensing for energy efficiency in office buildings. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 230(5), 410-423.
Elshaboury, N., & Marzouk, M. (2020a). Comparing machine learning models for predicting water pipelines condition. In: 2nd novel intelligent and leading emerging sciences conference (NILES), Giza, Egypt, pp. 134-139.
Elshaboury, N., & Marzouk, M. (2020b). Optimizing construction and demolition waste transportation for sustainable construction projects. Engineering, Construction and Architectural Management.
Elshaboury, N., Attia, T., & Marzouk, M. (2020). Application of evolutionary optimization algorithms for rehabilitation of water distribution networks. Journal of Construction Engineering and Management, 146(7), 04020069.
Erickson, V.L., Carreira-Perpinan, M.A., & Cerpa, A.E. (2011). Observe: Occupancy-based system for efficient reduction of HVAC energy. In: 10th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Chicago, United States, pp. 258-269.
Fabi, V., Barthelmes, V. M., Schweiker, M., & Corgnati, S. P. (2017). Insights into the effects of occupant behaviour lifestyles and building automation on building energy use. Energy Procedia, 140, 48-56.
Fabi, V., Corgnati, S., Andersen, R., Filippi, M., & Olesen, B. (2011). Effect of occupant behaviour related influencing factors on final energy and uses in building. In: Climamed, Madrid, Spain, pp. 243-258.
Fabi, V., D’Oca, S., Buso, T., & Corgnati, S. (2013). The influence of occupant's behaviour in a high performing building. In: Climamed -7th Mediterranean Congress of Climatization, Istanbul, Turkey.
Fisk, W. J. (2000). Health and productivity gains from better indoor environments and their relationship with building energy efficiency. Annual review of energy and the environment, 25(1), 537-566.
García-Ródenas, R., Linares, L. J., & López-Gómez, J. A. (2021). Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm. Neural Computing and Applications, 33(7), 2561-2588.
Gong, D., Lu, L., & Li, M. (2009). Robot path planning in uncertain environments based on particle swarm optimization. In: IEEE Congress on Evolutionary Computation, Trondheim, Norway, pp. 2127-2134.
Hong, T., & Lin, H.W. (2013). Occupant behavior: Impact on energy use of private offices, https://www.osti.gov/servlets/purl/1172115#:~:text=The%20simulation%20results%20demonstrate%20that,compared%20to%20the%20Standard%20workstyle (accessed 15 January 2020).
Hong, T., D'Oca, S., Turner, W. J., & Taylor-Lange, S. C. (2015). An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 92, 764-777.
Hong, T., Taylor-Lange, S. C., D’Oca, S., Yan, D., & Corgnati, S. P. (2016). Advances in research and applications of energy-related occupant behavior in buildings. Energy and buildings, 116, 694-702.
International Energy Agency (2020). Energy efficiency, https://www.iea.org/reports/energy-efficiency-2020 (accessed 17 January 2020).
Janda, K. B. (2011). Buildings don't use energy: people do. Architectural science review, 54(1), 15-22.
Jiang, S., Ji, Z., & Shen, Y. (2014). A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. International Journal of Electrical Power & Energy Systems, 55, 628-644.
Kavousian, A., Rajagopal, R., & Fischer, M. (2013). Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior. Energy, 55, 184-194.
Kennedy, J., & Eberhart, R.C. (1995). Particle swarm optimization. In: IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948.
Kumar, Y., & Sahoo, G. (2014). A review on gravitational search algorithm and its applications to data clustering & classification. International Journal of Intelligent Systems and Applications, 6(6), 79-93.
Labeodan, T., Zeiler, W., Boxem, G., & Zhao, Y. (2015). Occupancy measurement in commercial office buildings for demand-driven control applications—A survey and detection system evaluation. Energy and Buildings, 93, 303-314.
Lazzús, J. A. (2013). Neural network-particle swarm modeling to predict thermal properties. Mathematical and Computer Modelling, 57(9-10), 2408-2418.
Lindén, A. L., Carlsson-Kanyama, A., & Eriksson, B. (2006). Efficient and inefficient aspects of residential energy behaviour: What are the policy instruments for change?. Energy policy, 34(14), 1918-1927.
Lynn, N., & Suganthan, P. N. (2015). Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm and Evolutionary Computation, 24, 11-24.
Martinaitis, V., Zavadskas, E. K., Motuzienė, V., & Vilutienė, T. (2015). Importance of occupancy information when simulating energy demand of energy efficient house: A case study. Energy and Buildings, 101, 64-75.
Mirjalili, S., Hashim, S. Z. M., & Sardroudi, H. M. (2012). Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Applied Mathematics and Computation, 218(22), 11125-11137.
Mohammadi, M., & Rezaei, J. (2020). Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods. Omega, 96, 102254.
Nisiforou, O. A., Poullis, S., & Charalambides, A. G. (2012). Behaviour, attitudes and opinion of large enterprise employees with regard to their energy usage habits and adoption of energy saving measures. Energy and Buildings, 55, 299-311.
O'Brien, W., Gunay, B., Tahmasebi, F., & Mahdavi, A. (2017). Special issue on the fundamentals of occupant behaviour research. Journal of Building Performance Simulation, 10(5-6): 439-443.
Oldewurtel, F., Sturzenegger, D., & Morari, M. (2013). Importance of occupancy information for building climate control. Applied energy, 101, 521-532.
Page, J., Robinson, D., & Scartezzini, J.L. (2007). Stochastic simulation of occupant presence and behaviour in buildings. In: Building simulation conference, Beijing, China.
Pereira, P. F., Ramos, N. M., Almeida, R. M., & Simões, M. L. (2018). Methodology for detection of occupant actions in residential buildings using indoor environment monitoring systems. Building and Environment, 146, 107-118.
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm intelligence, 1(1), 33-57.
Polinder, H., Schweiker, M., Van der Aa, A., (2013). Occupant behavior and modeling. Total energy use in buildings, analysis and evaluation methods. Final Report Annex 53.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352-1372.
Ridha, H. M., Gomes, C., Hizam, H., Ahmadipour, M., Heidari, A. A., & Chen, H. (2021). Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review. Renewable and Sustainable Energy Reviews, 135, 110202.
Rinaldi, A., Schweiker, M., & Iannone, F. (2018). On uses of energy in buildings: Extracting influencing factors of occupant behaviour by means of a questionnaire survey. Energy and Buildings, 168, 298-308.
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1985). Learning internal representations by error propagation. In: David ER, James LM and Group CPR (eds). Parallel distributed processing: explorations in the microstructure of cognition. Cambridge, MA: MIT Press, pp. 318-362.
Salamone, F., Bellazzi, A., Belussi, L., Damato, G., Danza, L., Dell’Aquila, F., ... & Vitaletti, W. (2020). Evaluation of the visual stimuli on personal thermal comfort perception in real and virtual environments using machine learning approaches. Sensors, 20(6), 1627.
Schweiker, M., & Shukuya, M. (2009). Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions. Building and Environment, 44(10), 2137-2149.
Sembroiz, D., Careglio, D., Ricciardi, S., & Fiore, U. (2019). Planning and operational energy optimization solutions for smart buildings. Information Sciences, 476, 439-452.
Shaheen, M. A., Hasanien, H. M., & Alkuhayli, A. (2021). A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution. Ain Shams Engineering Journal, 12(1), 621-630.
Tam, V. W., Almeida, L., & Le, K. (2018). Energy-related occupant behaviour and its implications in energy use: A chronological review. Sustainability, 10(8), 2635.
Tang, R., Wang, S., & Sun, S. (2021). Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use. Building Simulation, 14(1), 209-217.
Trivedi, D., & Badarla, V. (2020). Occupancy detection systems for indoor environments: A survey of approaches and methods. Indoor and Built Environment, 29(8), 1053-1069.
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.
Zhang, Y., Bai, X., & Mills, F. P. (2020). Characterizing energy-related occupant behavior in residential buildings: Evidence from a survey in Beijing, China. Energy and Buildings, 214, 109823.
Zhang, Y., Bai, X., Mills, F. P., & Pezzey, J. C. (2018). Rethinking the role of occupant behavior in building energy performance: A review. Energy and Buildings, 172, 279-294.
Amayri, M., Ploix, S., Kazmi, H., Ngo, Q. D., & Safadi, E. L. (2019). Estimating occupancy from measurements and knowledge using the bayesian network for energy management. Journal of Sensors, 2019.
Andersen, R. (2012). The influence of occupant's behaviour on energy consumption investigated in 290 identical dwelling and 35 apartments. In: 10th International Conference on Healthy Buildings, Brisbane, Australia.
Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and information science, 3(1), 180.
Berger, C., & Mahdavi, A. (2020). Review of current trends in agent-based modeling of building occupants for energy and indoor-environmental performance analysis. Building and Environment, 173, 106726.
Brager, G., Paliaga, G., & De Dear, R. (2004). Operable windows, personal control and occupant comfort. ASHRAE Transactions, 110, 17-35.
Branco, G., Lachal, B., Gallinelli, P., & Weber, W. (2004). Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings, 36(6), 543-555.
Calì, D., Osterhage, T., Streblow, R., & Müller, D. (2016). Energy performance gap in refurbished German dwellings: Lesson learned from a field test. Energy and buildings, 127, 1146-1158.
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.
Chen, S., Yang, W., Yoshino, H., Levine, M. D., Newhouse, K., & Hinge, A. (2015). Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies. Energy and Buildings, 104, 1-13.
Chen, Z., Jiang, C., & Xie, L. (2018). Building occupancy estimation and detection: A review. Energy and Buildings, 169, 260-270.
De Simone, M., & Fajilla, G. (2018). Occupant behavior: A “new” factor in energy performance of buildings. Methods for its detection in houses and in offices. Journal of World Architecture, 2(2), 1-9.
Degelman, L.O. (1999). A model for simulation of daylighting and occupancy sensors as an energy control strategy for office buildings. In: Building simulation conference, Kyoto, Japan, pp. 571-578.
Delzendeh, E., Wu, S., Lee, A., & Zhou, Y. (2017). The impact of occupants’ behaviours on building energy analysis: A research review. Renewable and Sustainable Energy Reviews, 80, 1061-1071.
Dziedzic, J. W., Da, Y., & Novakovic, V. (2019). Indoor occupant behaviour monitoring with the use of a depth registration camera. Building and Environment, 148, 44-54.
Ekwevugbe, T., Brown, N., Pakka, V., & Fan, D. (2016). Advanced occupancy sensing for energy efficiency in office buildings. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 230(5), 410-423.
Elshaboury, N., & Marzouk, M. (2020a). Comparing machine learning models for predicting water pipelines condition. In: 2nd novel intelligent and leading emerging sciences conference (NILES), Giza, Egypt, pp. 134-139.
Elshaboury, N., & Marzouk, M. (2020b). Optimizing construction and demolition waste transportation for sustainable construction projects. Engineering, Construction and Architectural Management.
Elshaboury, N., Attia, T., & Marzouk, M. (2020). Application of evolutionary optimization algorithms for rehabilitation of water distribution networks. Journal of Construction Engineering and Management, 146(7), 04020069.
Erickson, V.L., Carreira-Perpinan, M.A., & Cerpa, A.E. (2011). Observe: Occupancy-based system for efficient reduction of HVAC energy. In: 10th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Chicago, United States, pp. 258-269.
Fabi, V., Barthelmes, V. M., Schweiker, M., & Corgnati, S. P. (2017). Insights into the effects of occupant behaviour lifestyles and building automation on building energy use. Energy Procedia, 140, 48-56.
Fabi, V., Corgnati, S., Andersen, R., Filippi, M., & Olesen, B. (2011). Effect of occupant behaviour related influencing factors on final energy and uses in building. In: Climamed, Madrid, Spain, pp. 243-258.
Fabi, V., D’Oca, S., Buso, T., & Corgnati, S. (2013). The influence of occupant's behaviour in a high performing building. In: Climamed -7th Mediterranean Congress of Climatization, Istanbul, Turkey.
Fisk, W. J. (2000). Health and productivity gains from better indoor environments and their relationship with building energy efficiency. Annual review of energy and the environment, 25(1), 537-566.
García-Ródenas, R., Linares, L. J., & López-Gómez, J. A. (2021). Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm. Neural Computing and Applications, 33(7), 2561-2588.
Gong, D., Lu, L., & Li, M. (2009). Robot path planning in uncertain environments based on particle swarm optimization. In: IEEE Congress on Evolutionary Computation, Trondheim, Norway, pp. 2127-2134.
Hong, T., & Lin, H.W. (2013). Occupant behavior: Impact on energy use of private offices, https://www.osti.gov/servlets/purl/1172115#:~:text=The%20simulation%20results%20demonstrate%20that,compared%20to%20the%20Standard%20workstyle (accessed 15 January 2020).
Hong, T., D'Oca, S., Turner, W. J., & Taylor-Lange, S. C. (2015). An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 92, 764-777.
Hong, T., Taylor-Lange, S. C., D’Oca, S., Yan, D., & Corgnati, S. P. (2016). Advances in research and applications of energy-related occupant behavior in buildings. Energy and buildings, 116, 694-702.
International Energy Agency (2020). Energy efficiency, https://www.iea.org/reports/energy-efficiency-2020 (accessed 17 January 2020).
Janda, K. B. (2011). Buildings don't use energy: people do. Architectural science review, 54(1), 15-22.
Jiang, S., Ji, Z., & Shen, Y. (2014). A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. International Journal of Electrical Power & Energy Systems, 55, 628-644.
Kavousian, A., Rajagopal, R., & Fischer, M. (2013). Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior. Energy, 55, 184-194.
Kennedy, J., & Eberhart, R.C. (1995). Particle swarm optimization. In: IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948.
Kumar, Y., & Sahoo, G. (2014). A review on gravitational search algorithm and its applications to data clustering & classification. International Journal of Intelligent Systems and Applications, 6(6), 79-93.
Labeodan, T., Zeiler, W., Boxem, G., & Zhao, Y. (2015). Occupancy measurement in commercial office buildings for demand-driven control applications—A survey and detection system evaluation. Energy and Buildings, 93, 303-314.
Lazzús, J. A. (2013). Neural network-particle swarm modeling to predict thermal properties. Mathematical and Computer Modelling, 57(9-10), 2408-2418.
Lindén, A. L., Carlsson-Kanyama, A., & Eriksson, B. (2006). Efficient and inefficient aspects of residential energy behaviour: What are the policy instruments for change?. Energy policy, 34(14), 1918-1927.
Lynn, N., & Suganthan, P. N. (2015). Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm and Evolutionary Computation, 24, 11-24.
Martinaitis, V., Zavadskas, E. K., Motuzienė, V., & Vilutienė, T. (2015). Importance of occupancy information when simulating energy demand of energy efficient house: A case study. Energy and Buildings, 101, 64-75.
Mirjalili, S., Hashim, S. Z. M., & Sardroudi, H. M. (2012). Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Applied Mathematics and Computation, 218(22), 11125-11137.
Mohammadi, M., & Rezaei, J. (2020). Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods. Omega, 96, 102254.
Nisiforou, O. A., Poullis, S., & Charalambides, A. G. (2012). Behaviour, attitudes and opinion of large enterprise employees with regard to their energy usage habits and adoption of energy saving measures. Energy and Buildings, 55, 299-311.
O'Brien, W., Gunay, B., Tahmasebi, F., & Mahdavi, A. (2017). Special issue on the fundamentals of occupant behaviour research. Journal of Building Performance Simulation, 10(5-6): 439-443.
Oldewurtel, F., Sturzenegger, D., & Morari, M. (2013). Importance of occupancy information for building climate control. Applied energy, 101, 521-532.
Page, J., Robinson, D., & Scartezzini, J.L. (2007). Stochastic simulation of occupant presence and behaviour in buildings. In: Building simulation conference, Beijing, China.
Pereira, P. F., Ramos, N. M., Almeida, R. M., & Simões, M. L. (2018). Methodology for detection of occupant actions in residential buildings using indoor environment monitoring systems. Building and Environment, 146, 107-118.
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm intelligence, 1(1), 33-57.
Polinder, H., Schweiker, M., Van der Aa, A., (2013). Occupant behavior and modeling. Total energy use in buildings, analysis and evaluation methods. Final Report Annex 53.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352-1372.
Ridha, H. M., Gomes, C., Hizam, H., Ahmadipour, M., Heidari, A. A., & Chen, H. (2021). Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review. Renewable and Sustainable Energy Reviews, 135, 110202.
Rinaldi, A., Schweiker, M., & Iannone, F. (2018). On uses of energy in buildings: Extracting influencing factors of occupant behaviour by means of a questionnaire survey. Energy and Buildings, 168, 298-308.
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1985). Learning internal representations by error propagation. In: David ER, James LM and Group CPR (eds). Parallel distributed processing: explorations in the microstructure of cognition. Cambridge, MA: MIT Press, pp. 318-362.
Salamone, F., Bellazzi, A., Belussi, L., Damato, G., Danza, L., Dell’Aquila, F., ... & Vitaletti, W. (2020). Evaluation of the visual stimuli on personal thermal comfort perception in real and virtual environments using machine learning approaches. Sensors, 20(6), 1627.
Schweiker, M., & Shukuya, M. (2009). Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions. Building and Environment, 44(10), 2137-2149.
Sembroiz, D., Careglio, D., Ricciardi, S., & Fiore, U. (2019). Planning and operational energy optimization solutions for smart buildings. Information Sciences, 476, 439-452.
Shaheen, M. A., Hasanien, H. M., & Alkuhayli, A. (2021). A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution. Ain Shams Engineering Journal, 12(1), 621-630.
Tam, V. W., Almeida, L., & Le, K. (2018). Energy-related occupant behaviour and its implications in energy use: A chronological review. Sustainability, 10(8), 2635.
Tang, R., Wang, S., & Sun, S. (2021). Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use. Building Simulation, 14(1), 209-217.
Trivedi, D., & Badarla, V. (2020). Occupancy detection systems for indoor environments: A survey of approaches and methods. Indoor and Built Environment, 29(8), 1053-1069.
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.
Zhang, Y., Bai, X., & Mills, F. P. (2020). Characterizing energy-related occupant behavior in residential buildings: Evidence from a survey in Beijing, China. Energy and Buildings, 214, 109823.
Zhang, Y., Bai, X., Mills, F. P., & Pezzey, J. C. (2018). Rethinking the role of occupant behavior in building energy performance: A review. Energy and Buildings, 172, 279-294.