Login | Register

Simulation-Based Optimization of Energy Consumption and Occupants Comfort in Open-Plan Office Buildings Using Probabilistic Occupancy Prediction Model

Title:

Simulation-Based Optimization of Energy Consumption and Occupants Comfort in Open-Plan Office Buildings Using Probabilistic Occupancy Prediction Model

Salimi, Shide (2019) Simulation-Based Optimization of Energy Consumption and Occupants Comfort in Open-Plan Office Buildings Using Probabilistic Occupancy Prediction Model. PhD thesis, Concordia University.

[thumbnail of Salimi_PhD_S2020.pdf]
Preview
Text (application/pdf)
Salimi_PhD_S2020.pdf - Accepted Version
7MB

Abstract

Considering the ever-growing increase in the world energy consumption and the fact that buildings contribute a large portion of the global energy consumption arises a need for detailed investigation towards more effective energy performance of buildings. Thus, monitoring, estimating, and reducing buildings’ energy consumption have always been important concerns for researchers and practitioners in the field of energy management. Since more than 80% of energy consumption happens during the operation phase of a building’s life cycle, efficient management of building operation is a promising way to reduce energy usage in buildings. Among the parameters influencing the total building energy consumption, building occupants’ presence and preferences could have high impacts on the energy usage of a building. To consider the effect of occupancy on building energy performance, different occupancy models, which aim to estimate the space utilization patterns, have been developed by researches. However, providing a comprehensive occupancy model, which could capture all important occupancy features, is still under development. Moreover, researchers investigated the effect of the application of occupancy-centered control strategies on the efficiency of the energy-consuming systems. However, there are still many challenges in this area of research mainly related to collecting, processing, and analyzing the occupancy data and the application of intelligent control strategies. In addition, generally, there is an inverse relationship between the energy consumption of operational systems and the comfort level of occupants using these systems. As a result, finding a balance between these two important concepts is crucial to improve the building operation. The optimal operation of building energy-consuming systems is a complex procedure for decision-makers, especially in terms of minimizing the energy cost and the occupants’ discomfort.
On this premise, this research aims to develop a new simulation-based multi-objective optimization model of the energy consumption in open-plan offices based on occupancy dynamic profiles and occupants’ preferences and has the following objectives: (1) developing a method for extracting detailed occupancy information with varying time-steps from collected Real-Time Locating System (RTLS) occupancy data. This method captures different resolution levels required for the application of intelligent, occupancy-centered local control strategies of different building systems; (2) developing a new time-dependent inhomogeneous Markov chain occupancy prediction model based on the derived occupancy information, which distinguishes the temporal behavior of different occupants within an open-plan office; (3) improving the performance of the developed occupancy prediction model by determining the near-optimum length of the data collection period, selecting the near-optimum training dataset, and finding the most satisfying temporal resolution level for analyzing the occupancy data; (4) developing local control algorithms for building energy-consuming systems; and (5) integrating the energy simulation model of an open-plan office with an optimization algorithm to optimally control the building energy-consuming systems and to analyze the trade-off between building energy consumption and occupants’ comfort. It is found that the occupancy perdition model is able to estimate occupancy patterns of the open-plan office with 92% and 86% accuracy at occupant and zone levels, respectively. Also, the proposed integrated model improves the thermal condition by 50% along with 2% savings in energy consumption by developing intelligent, optimal, and occupancy-centered local control strategies.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Salimi, Shide
Institution:Concordia University
Degree Name:Ph. D.
Program:Civil Engineering
Date:27 October 2019
Thesis Supervisor(s):Hammad, Amin
Keywords:Energy management system; Adaptive local control strategies; Dynamic occupancy profiles; comfort; Multi-objective optimization; Simulation; Adaptive occupancy prediction modelling; Real-time occupancy monitoring; Inhomogeneous Markov chain; Open-plan offices; Intelligent energy management
ID Code:986197
Deposited By: SHIDE SALIMI
Deposited On:25 Jun 2020 18:18
Last Modified:25 Jun 2020 18:18

References:

Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., & Weng, T. (2010). Occupancy-driven energy management for smart building automation. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, (pp. 1-6).
Aghemo, C., Blaso, L., & Pellegrino, A. (2014). Building automation and control systems: A case study to evaluate the energy and environmental performances of a lighting control system in offices. Automation in Construction, 43, 10-22.
Ai, B., Fan, Z., & Gao, R. X. (2014). Occupancy estimation for smart buildings by an auto-regressive hidden Markov model. In American Control Conference (ACC) (pp. 2234-2239). Portland, Oregon, USA: IEEE.
Akanmu, A., Rasheed, S. H., & Qader, I. A. (2013). Spatial mapping approach to component tracking using RTLS system. Architectural Engineering Conference 2013 (pp. 363-37). ASCE.
Al Bustani, H. (2014). UAE sustainable skyscrapers: understanding Abu Dhabi’s Al Bahar Towers. (Abu Dhabi Investment Council) Retrieved from https://www.thenational.ae/business/uae-sustainable-skyscrapers-understanding-abu-dhabi-s-al-bahar-towers-1.248717
Al-Mumin, A., Khattab, O., & Sridhar, G. (2003). Occupants’ behavior and activity patterns influencing the energy consumption in the Kuwaiti residences. Energy and buildings, 35(6), 549-559.
Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., & Vairo, C. (2017). Deep learning for decentralized parking lot occupancy detection. Expert Systems with Applications, 72, 327-334.
Analytics, G. (2015). Seeing Quuppa's indoor location technology at MWC 2015. Retrieved 4 15, 2017, from http://grizzlyanalytics.blogspot.ca/2015/03/seeing-quuppas-indoor-location.html
Annex 53. (2016). Total Energy Use in Buildings: Analysis and Evaluation Methods. AECOM Ltd.
Arora, A., Amayri, M., Badarla, V., Ploix, S., & Bandyopadhyay, S. (2015). Occupancy estimation using non intrusive sensors in energy efficient buildings. Proceedings of BS2015: 14th Conference of International Building Performance Simulation Association (pp. 1441-1448). Hyderabad, India: Building simulation.
ASHRAE. (2002). ASHRAE Guideline 14-2002, measurement of energy and demand savings. ISSN1049-894X. American Society of Heating Refrigeration and Air Conditioning Engineers.
ASHRAE. (2007). Energy Standard for Buildings Except Low-Rise Residential Buildings. ASHRAE, 90.1-2007.
ASHRAE/ANSI Standard 55-2010. (2010). Thermal Environmental Conditions for Human Occupancy. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers.
Aswani, A., Master, N., Taneja, J., Culler, D., & Tomlin, C. (2012). Reducing transient and steady state electricity consumption in HVAC using learning-based model-predictive control. Proceedings of the IEEE, 100(1), 240-253. doi:10.1109/JPROC.2011.2161242
Attar, R., Hailemariam, E., Breslav, S., Khan, A., & Kurtenbach, G. (2011). Sensor-enabled Cubicles for Occupant-centric Capture of Building Performance Data. ASHRAE Transactions, 117(2), 441-449.
Augello, A., Ortolani, M., Re, G. L., & Gaglio, S. (2011). Sensor mining for user behavior profiling in intelligent environments. In V. Pallotta, A. Soro, & E. Vargiu, Advances in Distributed Agent-Based Retrieval Tools (pp. 143-158). Springer Berlin Heidelberg.
Azar, E., & Menassa, C. C. (2011). Agent-based modeling of occupants and their impact on energy use in commercial buildings. Journal of Computing in Civil Engineering, 26(4), 506-518.
Azar, E., & Menassa, C. C. (2012). A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings. Energy and Buildings, 55, 841-853.
Azzouzi, S., Cremer, M., Dettmar, U., Knie, T., & Kronberger, R. (2011). Improved AoA based localization of UHF RFID tags using spatial diversity. RFID-Technologies and Applications (RFID-TA), (pp. 174-180).
Balaji, B., Xu, J., Nwokafor, A., Gupta, R., & Agarwal, Y. (2013). Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. Roma, Italy: ACM.
Benezeth, Y., Laurent, H., Emile, B., & Rosenberger, C. (2011). Towards a sensor for detecting human presence and characterizing activity. Energy and Buildings, 43(2), 305-314.
Bengea, S. C., Kelman, A. D., Borrelli, F., Taylor, R., & Narayanan, S. (2014). Implementation of model predictive control for an HVAC system in a mid-size commercial building. HVAC&R Research, 20(1), 121-135.
Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1), 1-127.
Bloomberg. (2015, September 23). The Smartest Building in the World. (PLP Architecture) Retrieved from https://www.bloomberg.com/features/2015-the-edge-the-worlds-greenest-building/
Bluetooth. (2016). Bluetooth Core Specification v5.0. Bluetooth SIG Working Groups.
Brackney, L. J., Florita, A. R., Swindler, A. C., Polese, L. G., & Brunemann, G. A. (2012). Design and performance of an image processing occupancy sensor. In Proceedings: The Second International Conference on Building Energy and Environment 2012987 Topic 10 (pp. 987-994). Intelligent buildings and advanced control techniques.
Brager, G., Paliaga, G., & De Dear, R. (2004). Operable Windows, Personal Control and Occupant Comfort. ASHRAE, pp. 17-35.
Brooks, J., & Barooah, P. (2014). Energy-efficient control of under-actuated HVAC zones in buildings. In American Control Conference (ACC) (pp. 424-429). Portland, Oregon, USA: IEEE.
Brooks, J., Goyal, S., Subramany, R., Lin, Y., Middelkoop, T., Arpan, L., . . . Barooah, P. (2014). An experimental investigation of occupancy-based energy-efficient control of commercial building indoor climate. In Decision and Control (CDC) (pp. 5680-5685). Los Angeles, California, USA: IEEE 53rd Annual Conference.
Brooks, J., Kumar, S., Goyal, S., Subramany, R., & Barooah, P. (2015). Energy-efficient control of under-actuated HVAC zones in commercial buildings. Energy and Buildings, 93, 160-168.
buildingSMART. (2018). Technical Vision. Retrieved from buildingSMART International home of openBIM: https://www.buildingsmart.org/standards/technical-vision/
Caicedo, D., & Pandharipande, A. (2016). Daylight and occupancy adaptive lighting control system: An iterative optimization approach. Lighting Research & Technology, 48(6), 661-675.
Caicedo, D., Li, S., & Pandharipande, A. (2017). Smart lighting control with workspace and ceiling sensors. Lighting Research and Technology, 49, 446–460. doi:1477153516629531
Caicedo, D., Pandharipande, A., & Vissenberg, M. C. (2015). Smart modular lighting control system with dual-beam luminaires. Lighting Research & Technology, 47(4), 389-404.
CapotaLand. (2017). The future of urban living. (CapitaLand) Retrieved from https://www.capitaland.com/sg/en.html?id=11
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.
Chang, W. K., & Hong, T. (2013). Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data. In Building Simulation, 6(1), 23-32.
Chen, J., & Ahn, C. (2014). Assessing occupants’ energy load variation through existing wireless network infrastructure in commercial and educational buildings. Energy and Buildings, 82, 540-549.
Chen, Z., Xu, J., & Soh, Y. C. (2015). Modeling regular occupancy in commercial buildings using stochastic models. Energy and Buildings, 103, 216-223.
Cho, Y., Lim, S. O., & Yang, H. S. (2010). Collaborative occupancy reasoning in visual sensor network for scalable smart video surveillance. IEEE transactions on Consumer Electronics, 56(3), 1997-2003.
Christ, M., Kempa-Liehr, A. W., & Feindt, M. (2016). Distributed and parallel time series feature extraction for industrial big data applications. arXiv preprint arXiv:1610.07717.
Chung, T. M., & Burnett, J. (2001). On the prediction of lighting energy savings achieved by occupancy sensors. Energy engineering, 98(4), 6-23.
Çiftler, B. S., Dikmese, S., Güvenç, I., Akkaya, K., & Kadri, A. (2017). Occupancy counting with burst and intermittent signals in smart buildings. IEEE Internet of Things Journal, 5(2), 724-735.
Clevenger, C. M., & Haymaker, J. (2006). The impact of the building occupant on energy modeling simulations. Joint International Conference on Computing and Decision Making in Civil and Building Engineering, (pp. 1-10). Montreal, Canada.
Conte, G., De Marchi, M., Nacci, A. A., Rana, V., & Sciuto, D. (2014). BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system. In BuildSys@ SenSys (pp. 11-19). Memphis, TN, USA: ACM.
Curran, K., Furey, E., Lunney, T., Santos, J., Woods, D., & McCaughey, A. (2011). An evaluation of indoor location determination technologies. Journal of Location Based Services, 5(2), 61-78.
D’Oca, S., & Hong, T. (2015). Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88, 395-408.
Daum, D., & Morel, N. (2010). Assessing the total energy impact of manual and optimized blind control in combination with different lighting schedules in a building simulation environment. Journal of Building Performance Simulation, 3(1), 1-16.
Davis, J. A., & Nutter, D. W. (2010). Occupancy diversity factors for common university building types. Energy and buildings, 42(9), 1543-1551.
Day, J. K., & Gunderson, D. E. (2015). Understanding high performance buildings: The link between occupant knowledge of passive design systems, corresponding behaviors, occupant comfort and environmental satisfaction. Building and Environment, 84, 114-124.
de Bakker, C., Aries, M., Kort, H., & Rosemann, A. (2017). Occupancy-based lighting control in open-plan office spaces: A state-of-the-art review. Building and Environment, 112, 308-321.
Deb, K. (2005). Muti-Objective Optimization. In E. Burke, & G. Kendall, Search Methodologies Introductory Tutorials in Optimization and Decision Support Techniques (pp. 97-125). United States of America: Springer Science-i-Business Media, LLC.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions, 6(2), 182-197.
Dedesko, S., Stephens, B., Gilbert, J. A., & Siegel, J. A. (2015). Methods to assess human occupancy and occupant activity in hospital patient rooms. Building and Environment, 90, 136-145.
Delaney, D. T., O'Hare, G. M., & Ruzzelli, A. G. (2009). Evaluation of energy-efficiency in lighting systems using sensor networks. In Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (pp. 61-66). ACM.
Delgarm, N., Sajadi, B., Azarbad, K., & Delgarm, S. (2018). Sensitivity analysis of building energy performance: A simulation-based approach using OFAT and variance-based sensitivity analysis methods. Journal of Building Engineering , 15, 181-193.
Delgoshaei, P., Heidarinejad, M., Xu, K., Wentz, J. R., Delgoshaei, P., & Srebric, J. (2017). Impacts of building operational schedules and occupants on the lighting energy consumption patterns of an office space. In Building Simulation, 10(4), 447-458.
Deng, Z., & Chen, Q. (2019). Simulating the Impact of Occupant Behavior on Energy Use of HVAC Systems by Implementing a Behavioral Artificial Neural Network Model. Energy and Buildings, 198, 216-227.
Díaz, P., Olivares, T., Galindo, R., Ortiz, A., Royo, F., & Clemente, T. (2011). The ecosense project: an intelligent energy management system with a wireless sensor and actor network. In Sustainability in Energy and Buildings (pp. 237-245). Berlin Heidelberg: Springer.
Dikel, E. E., Newsham, G. R., Xue, H., & Valdés, J. J. (2017). Potential energy savings from high-resolution sensor controls for LED lighting. Energy and Buildings, 156, 43-53.
DiLaura, D. L., Houser, K. W., Mistrick, R. G., & Steffy, G. R. (2011). The lighting handbook: Reference and application (10th ed.). New York: Illuminating Engineering Society of North America (IESNA).
Dobbs, J. R., & Hencey, B. M. (2014a). Predictive HVAC control using a Markov occupancy model. In American Control Conference (ACC) (pp. 1057-1062). Portland, Oregon, USA: IEEE.
Dobbs, J. R., & Hencey, B. M. (2014b). Model predictive HVAC control with online occupancy model. Energy and Buildings, 82, 675-684.
Dodier, R. H., Henze, G. P., Tiller, D. K., & Guo, X. (2006). Building occupancy detection through sensor belief networks. Energy and buildings,38(9),, 38(9), 1033-1043.
DOE. (2015). Application Guide for EMS Energy Management System–User Guide. U.S. Department of Energy.
DOE. (2016). DOE-2 Building Energy Use and Cost Analysis Tool. Retrieved from http://doe2.com/DOE2/index.html
Dong, B., & Andrews, B. (2009). Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings. In Proceedings of building simulation, (pp. 1444-1451).
Dong, B., & Lam, K. P. (2011). Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network. Journal of Building Performance Simulation, 4(4), 359-369.
Dong, B., & Lam, K. P. (2014). A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Building Simulation, 7(1), 89-106.
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.
Dong, B., Lam, K. P., & Neuman, C. (2011). Integrated building control based on occupant behavior pattern detection and local weather forecasting. InTwelfth International IBPSA Conference (pp. 14-17). Sydney, Australia: IBPSA.
Douc, R., Moulines, E., & Rosenthal, J. S. (2004). Quantitative bounds on convergence of time-inhomogeneous Markov chains. Annals of Applied Probability, 14(4), 1643-1665.
Doukas, H., Patlitzianas, K. D., Iatropoulos, K., & Psarras, J. (2007). Intelligent building energy management system using rule sets. Building and environment, 42(10), 3562-3569.
Duarte, C., Budwig, R., & Van Den Wymelenberg, K. (2015). Energy and demand implication of using recommended practice occupancy diversity factors compared to real occupancy data in whole building energy simulation. Journal of Building Performance Simulation , 8(6), 408-423.
Duarte, C., Van Den Wymelenberg, K., & Rieger, C. (2013). Revealing occupancy patterns in an office building through the use of occupancy sensor data. Energy and buildings, 67, 587-595.
Ekwevugbe, T., Brown, N., & Fan, D. (2012). A design model for building occupancy detection using sensor fusion. In Digital Ecosystems Technologies (DEST), 6th IEEE International Conference (pp. 1-6). IEEE.
Ekwevugbe, T., Brown, N., Pakka, V., & Fan, D. (2017). Improved occupancy monitoring in non-domestic buildings. Sustainable Cities and Society, , 30, 97-107.
Energy Information Administration (EIA). (2010). Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables. Retrieved from U.S. Energy Information Administration (EIA): https://www.eia.gov/
EnergyPlus. (2015). EnergyPlus Engineering Reference. a National Laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (NREL), Berkeley, California: LBNL Press.
EnergyPlus. (2015). Weather Data. Retrieved from DesignBuilder Documents: https://energyplus.net/weather
EPA. (1989). Report to Congress on Indoor Air Quality. Washington, DC.: U.S. Environmental Protection Agency, EPA/400/1-89/001C.
Erickson, V. L., & Cerpa, A. E. (2010). Occupancy based demand response HVAC control strategy. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (pp. 7-12). ACM.
Erickson, V. L., Carreira-Perpiñán, M. Á., & Cerpa, A. E. (2011). OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. In Information Processing in Sensor Networks (IPSN), 10th International Conference (pp. 258-269). Chicago, Illinois: IEEE.
Erickson, V. L., Lin, Y., Kamthe, A., Brahme, R., Surana, A., Cerpa, A. E., . . . Narayanan, S. (2009). Energy efficient building environment control strategies using real-time occupancy measurements. In Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (pp. 19-24). ACM.
Ersue, M., Romascanu, D., Schoenwaelder, J., & Sehgal, A. (2015). Management of networks with constrained devices: use cases. (No. RFC 7548).
Escuyer, S., & Fontoynont, M. (2001). Lighting controls: a field study of office workers’ reactions. Transactions of the Illuminating Engineering Society, 33(2), 77-94.
ESRU. (2012). ESP-r. Retrieved from http://www.esru.strath.ac.uk/Programs/ESP-r.htm
EU. (2018). EU energy in figures, statistical pocketbook 2018. Luxembourg: Imprimerie Centrale SA.
European Commission. (2003). Indoor Air Pollution: New EU Research Reveals Higher Risks than Previously Thought. Brussels: Press Release Database.
Fabi, V., Camisassi, V., Causone, F., Corgnati, S. P., & Andersen, R. K. (2014). Light switch behaviour: occupant behaviour stochastic models in office buildings. In 8th windsor conference (pp. 1-9). Windsor, UK: London: Network for Comfort and Energy Use in Buildings.
Fanger, P. (1972). Thermal Comfort-Analysis and Applications in Environmental Engineering. Copenhagen: Danish Technical Press.
Feng, X., Yan, D., & Hong, T. (2015). Simulation of occupancy in buildings. Energy and Buildings, 87, 348-359.
Fernandes, L. L., Lee, E. S., DiBartolomeo, D. L., & McNeil, A. (2014). Monitored lighting energy savings from dimmable lighting controls in The New York Times Headquarters Building. Energy and Buildings, 68, 498-514.
Foster, T. W., Bhatt, D. V., Hancke, G. P., & Silva, B. (2016). A Web-Based Office Climate Control System Using Wireless Sensors. IEEE Sensors Journal, 16(15), 6104-6113.
Gagnon, R., Gosselin, L., & Decker, S. (2018). Sensitivity analysis of energy performance and thermal comfort throughout building design process. Energy and Buildings, 164, 278-294.
Galasiu, A. D., & Newsham, G. R. (2009). Energy savings due to occupancy sensors and personal controls: A pilot field study. Proceedings of Lux Europa. Ottawa, Ontario, Canada: NSERC.
Galasiu, A. D., Newsham, G. R., Suvagau, C., & Sander, D. M. (2007). Energy saving lighting control systems for open-plan offices: a field study. Leukos, 4(1), 7-29.
Garg, V., & Bansal, N. K. (2000). Smart occupancy sensors to reduce energy consumption. Energy and Buildings, 32(1), 81-87.
Gentile, N., & Dubois, M. C. (2017). Field data and simulations to estimate the role of standby energy use of lighting control systems in individual offices. Energy and Buildings, 155, 390-403.
Goldstein, R., Tessier, A., & Khan, A. (2010a). Customizing the behavior of interacting occupants using personas. In Proceedings of the National IBPSA-USA Conference, (pp. 252-259). New York, USA.
Goldstein, R., Tessier, A., & Khan, A. (2010b). Schedule-calibrated occupant behavior simulation. In Proceedings of the 2010 Spring Simulation Multiconference (p. p. 180). Society for Computer Simulation International.
Goldstein, R., Tessier, A., & Khan, A. (2011). Space layout in occupant behavior simulation. In Conference Proceedings: IBPSA-AIRAH Building Simulation Conference (pp. 1073-1080). Sydney: 12th Conference of International Building Performance Simulation Association.
Goyal, S., Barooah, P., & Middelkoop, T. (2015). Experimental study of occupancy-based control of HVAC zones. Applied Energy, 140, 75-84.
Goyal, S., Ingley, H. A., & Barooah, P. (2012). Effect of various uncertainties on the performance of occupancy-based optimal control of HVAC zones. In Decision and Control (CDC), 2012 IEEE 51st Annual Conference (pp. 7565-7570). IEEE.
Goyal, S., Ingley, H. A., & Barooah, P. (2013). Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance. Applied Energy, 106, 209-221.
Granderson, J., & Price, P. N. (2014). Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models. Energy, 66, 981-990.
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.
Guerra-Santin, O., & Itard, L. (2012). The effect of energy performance regulations on energy consumption. Energy Efficiency, 5(3), 269-282.
Gunay, H. B., O'Brien, W. B.-M., & Bursill, J. (2016). Implementation of an adaptive occupancy and building learning temperature setback algorithm. Ashrae Transactions, 122, 179-192.
Gunay, H. B., O'Brien, W., Beausoleil-Morrison, I., Goldstein, R., Breslav, S., & Khan, A. (2014). Coupling stochastic occupant models to building performance simulation using the discrete event system specification formalism. Journal of Building Performance Simulation, 7(6), 457-478.
Guo, X., Tiller, D. K., Henze, G. P., & Waters, C. E. (2010). The performance of occupancy-based lighting control systems: A review. Lighting Research & Technology, 42(4), 415-431.
Hailemariam, E., Goldstein, R., Attar, R., & Khan, A. (2011). Real-time occupancy detection using decision trees with multiple sensor types. In Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design (pp. 141-148). Society for Computer Simulation International.
Haldi, F., & Robinson, D. (2008). On the behaviour and adaptation of office occupants. Building and environment, 43(12), 2163-2177.
Haldi, F., & Robinson, D. (2011). Modelling occupants’ personal characteristics for thermal comfort prediction. International journal of biometeorology, 55(5), 681-694.
Han, Z., Gao, R. X., & Fan, Z. (2012). Occupancy and indoor environment quality sensing for smart buildings. In Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International (pp. 882-887). IEEE.
Harle, R. K., & Hopper, A. (2008). The potential for location-aware power management. In Proceedings of the 10th international conference on Ubiquitous computing (pp. 302-311). Seoul, Korea: ACM.
Harris, C., & Cahill, V. (2005). Exploiting user behaviour for context-aware power management. In Wireless And Mobile Computing, Networking And Communications, IEEE International Conference. 4, pp. 122-130. IEEE.
Hassan, R., Cohanim, B., De Weck, O., & Venter, G. (2005). A comparison of particle swarm optimization and the genetic algorithm. In Proceedings of the 1st AIAA multidisciplinary design optimization specialist conference (pp. 1-13). American Institute of Aeronautics and Astronautics.
Hightower, J., & Borriello, G. (2001). Location systems for ubiquitous computing. Computer, 34(8), 57-66.
Holland, J. (1975). Adaptation in Natural and Artificial System. Ann Arbor, MI: University of Michigan Press.
Hong, T., & Lin, H.W. (2014). Occupant behavior: impact on energy use of private offices. In ASim 2012-1st Asia conference of International Building Performance Simulation Association, (pp. 1-12). Shanghai, China, 11/25/12-11/27/12.
Hong, T., D'Oca, S., Turner, W. J., & Taylor-Lange, S. C. (2015a). 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., D'Oca, S., Taylor-Lange, S. C., Turner, W. J., Chen, Y., & Corgnati, S. P. (2015b). An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema. Building and Environment, 94, 196-205.
Howard, B., Acha, S., Shah, N., & Polak, J. (2019). Implicit Sensing of Building Occupancy Count with Information and Communication Technology Data Sets. Building and Environment, 157, 297-308.
Humphreys, M. A., Rijal, H. B., & Nicol, J. F. (2013). Updating the adaptive relation between climate and comfort indoors; new insights and an extended database. Building and Environment, 63, 40-55.
IBM. (2016). Embracing the Internet of Things in the new era of cognitive buildings. United States of America: IBM Corporation.
IBM. (2017). Energy and environment. (IBM) Retrieved from https://www.ibm.com/ibm/green/smarter_buildings.html
IEA EBC Annex 66. (2013-2017). International Energy Agency. Energy in buildings and communities program. Retrieved from http://www.annex66.org/
IES. (2019). Integrated Environmental Solutions . Retrieved from https://www.iesve.com/
Imanishi, T., Tennekoon, R., Palensky, P., & Nishi, H. (2015). Enhanced building thermal model by using CO2 based occupancy data. IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society (pp. 003116-003121). IEEE.
IndustryARC. (2016). Bluetooth Smart/Bluetooth Low Energy market: applications (consumer electronics, healthcare, sports & fitness, retail, automotive, security); by technology [discrete modules, integrated modules (single & dual mode)]-forecast (2017-2022). Retrieved from http://industryarc.com
Ioannou, A., & Itard, L. C. (2015). Energy performance and comfort in residential buildings: Sensitivity for building parameters and occupancy. Energy and Buildings, 92, 216-233.
Jain, S., & Madamopoulos, N. (2016). Ahorrar: Indoor Occupancy Counting to Enable Smart Energy Efficient Office Buildings. In Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom) (pp. 469-476). IEEE.
Javed, A., Larijani, H., Ahmadinia, A., Emmanuel, R., Mannion, M., & Gibson, D. (2016). Design and implementation of a cloud enabled random neural network-based decentralized smart controller with intelligent sensor nodes for HVAC. IEEE Internet of Things, 4(2), 393-403.
Jazizadeh, F., & Becerik-Gerber, B. (2012). A novel method for non intrusive load monitoring of lighting systems in commercial buildings. Computing in Civil Engineering, 523-530.
Jennings, J. D., Rubinstein, F. M., DiBartolomeo, D., & Blanc, S. L. (2000). Comparison of control options in private offices in an advanced lighting controls testbed. Journal of the Illuminating Engineering Society, 29(2), 39-60.
Jennings, J., Colak, N., & Rubinstein, F. (2002). Occupancy and time-based lighting controls in open offices. Journal of the Illuminating Engineering Society, 31(2), 86-100.
Jiefan, G., Peng, X., Zhihong, P., Yongbao, C., Ying, J., & Zhe, C. (2018). Extracting typical occupancy data of different buildings from mobile positioning data. Energy and Buildings, 180, 135-145.
Jin, M. J., & Spanos, C. (2017). Virtual occupancy sensing: Using smart meters to indicate your presence. IEEE Transactions on Mobile Computing (pp. 1-14). IEEE. doi:DOI 10.1109/TMC.2017.2684806
Jin, M., Jia, R., Kang, Z., Konstantakopoulos, I. C., & Spanos, C. J. (2014). Presencesense: Zero-training algorithm for individual presence detection based on power monitoring. In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings (pp. 1-10). ACM.
Justel, A., Peña, D., & Zamar, R. (1997). A multivariate Kolmogorov-Smirnov test of goodness of fit. Statistics & Probability Letters, 35(3), 251-259.
Kamthe, A., Jiang, L., Dudys, M., & Cerpa, A. (2009). Scopes: Smart cameras object position estimation system. In Wireless Sensor Networks (pp. 279-295). Berlin, Heidelberg: In European Conference on Wireless Sensor Networks, Springer.
Karjalainen, S. (2007). Gender differences in thermal comfort and use of thermostats in everyday thermal environments. Building and environment, 42(4), 1594-1603.
Kavulya, G., & Becerik-Gerber, B. (2012). Understanding the influence of occupant behavior on energy consumption patterns in commercial buildings. Computing in Civil Engineering, 569-576.
Khan, A., & Hornbæk, K. (2011). Big data from the built environment. Proceedings of the 2nd international workshop on Research in the large (pp. 29-32). Beijing, China: ACM.
Khan, A., Nicholson, J., Mellor, S., Jackson, D., Ladha, K., Ladha, C., . . . Plötz, T. (2014). Occupancy monitoring using environmental & context sensors and a hierarchical analysis framework . BuildSys’14 (pp. 90-99). Memphis, TN, USA: ACM.
Kim, J., Bauman, F., Raftery, P., Arens, E., Zhang, H., Fierro, G., & ... & Culler, D. (2019). Occupant comfort and behavior: High-resolution data from a 6-month field study of personal comfort systems with 37 real office workers. Building and Environment, 148, 348-360.
Kim, Y. S., & Srebric, J. (2015). Improvement of building energy simulation accuracy with occupancy schedules derived from hourly building electricity consumption. Ashrae Transactions, 121(1), 353-361.
Krishnamachari, B., Estrin, D., & Wicker, S. B. (2002). The Impact of Data Aggregation in Wireless Sensor Networks. Proceedings of the 22 nd International Conference on Distributed Computing Systems Workshops (ICDCSW’02). 578, pp. 1-8. ICDCS workshops.
Kuo, T. C., Chan, Y. C., & Chen, A. Y. (2017). An Occupant-Centered Integrated Lighting and Shading Control for Energy Saving and Individual Preferences. In Computing in Civil Engineering 2017 (pp. 207-214). Seattle, USA: IWCCE.
Labeodan, T., Aduda, K., Zeiler, W., & Hoving, F. (2016). Experimental evaluation of the performance of chair sensors in an office space for occupancy detection and occupancy-driven control. Energy and Buildings, 111, 195-206.
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,, 93, 303-314.
Lam, K. P., Höynck, M., Dong, B., Andrews, B., Chiou, Y. S., Zhang, R., . . . Choi, J. (2009). Occupancy detection through an extensive environmental sensor network in an open-plan office building. , 145,. IBPSA Building Simulation. 145, pp. 1452-1459. Glasgow, Scotland: Eleventh International IBPSA Conference.
Langevin, J., Wen, J., & Gurian, P. L. (2013). Modeling thermal comfort holistically: Bayesian estimation of thermal sensation, acceptability, and preference distributions for office building occupants. Building and Environment, 69, 206-226.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444.
Lee, D., Sim, A., Choi, J., & Wu, K. (2016). Novel data reduction based on statistical similarity. Proceedings of the 28th International Conference on Scientific and Statistical Database Management (pp. 21-33). ACM.
Lejlic, E. (2017). The Advantages of Machine Learning. Lumagate.
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.
Li, S., Deng, K., & Zhou, M. (2016). Sensitivity analysis for building energy simulation model calibration via algorithmic differentiation. IEEE Transactions on Automation Science and Engineering , 14(2), 905-914.
Liang, X., Hong, T., & Shen, G. Q. (2016). Improving the accuracy of energy baseline models for commercial buildings with occupancy data. Applied energy, 179, 247-260.
Liao, C., & Barooah, P. (2010). An integrated approach to occupancy modeling and estimation in commercial buildings. In Proceedings of the 2010 American Control Conference (pp. 3130-3135). IEEE.
Lilis, G., Conus, G., Asadi, N., & Kayal, M. (2017). Towards the next generation of intelligent building: An assessment study of current automation and future IoT based systems with a proposal for transitional design. Sustainable cities and society, 28, 473-481.
Lim, B., Hijazi, H., Thiébaux, S., & van den Briel, M. (2016). Online HVAC-Aware Occupancy Scheduling with Adaptive Temperature Control. In International Conference on Principles and Practice of Constraint Programming (pp. 683-700). Springer International Publishing.
Lim, G. H., Keumala, N., & Ghafar, N. A. (2017). Energy saving potential and visual comfort of task light usage for offices in Malaysia. Energy and Buildings, 147, 166-175.
Liu, Z. (2017). Simulation of Local Climate Control in Shared Offices Based on Occupants Locations and Preferences. Montreal: Master Thesis, Concordia University.
Liu, Z., Salimi, S., & Hammad, A. (2016). Simulation of HVAC Local Control Based on Occupants Locations and Preferences. 33rd International Symposium on Automation and Robotics in Construction . Auburn, Alabama, USA: ISARC.
Lo, L. J., & Novoselac, A. (2010). Localized air-conditioning with occupancy control in an open office. Energy and Buildings, 42(7), 1120-1128.
Mackensen, E., Lai, M., & Wendt, T. M. (2012). Performance analysis of an Bluetooth Low Energy sensor system. In Wireless Systems (IDAACS-SWS), IEEE 1st International Symposium (pp. 62-66). IEEE.
Mahdavi, A., Mohammadi, A., Kabir, E., & Lambeva, L. (2008). Occupants' operation of lighting and shading systems in office buildings. Journal of building performance simulation, 1(1), 57-65.
Mahdavi, A., Mohammadi, A., Kabir, E., & Lambeva, L. (2008). Shading and lighting operation in office buildings in Austria: A study of user control behavior. In Building simulation, 1(2), 111-117.
Majcen, D., Itard, L. C., & Visscher, H. (2013a). Theoretical vs. actual energy consumption of labelled dwellings in the Netherlands: Discrepancies and policy implications. Energy policy, 54, 125-136.
Majcen, D., Itard, L., & Visscher, H. (2013b). Actual and theoretical gas consumption in Dutch dwellings: What causes the differences? Energy Policy, 61, 460-471.
Majumdar, A., Setter, J. L., Dobbs, J. R., Hencey, B. M., & Albonesi, D. H. (2014). Energy-comfort optimization using discomfort history and probabilistic occupancy prediction. In Green Computing Conference (IGCC) (pp. 1-10). IEEE.
Maniccia, D., Tweed, A., Bierman, A., & Von Neida, B. (2001). The effects of changing occupancy sensor time-out setting on energy savings, lamp cycling and maintenance costs. Journal of the Illuminating Engineering Society, 30(2), 97-110.
Manzoor, F., Linton, D., & Loughlin, M. (2012). Occupancy monitoring using passive RFID technology for efficient building lighting control. In RFID Technology (EURASIP RFID), 2012 Fourth International EURASIP Workshop (pp. 83-88). IEEE.
Mashuk, M. S., Pinchin, J., Siebers, P. O., & Moore, T. (2018). A smart phone based multi-floor indoor positioning system for occupancy detection. Position, Location and Navigation Symposium (PLANS) (pp. 216-227). IEEE.
Masoso, O. T., & Grobler, L. J. (2010). The dark side of occupants’ behaviour on building energy use.
Massart, D. L., & Smeyers-verbeke, A. J. (2005). Practical data handling visual presentation of data by means of box plots. LCGC Europe, 18(4), 215-2018.
Massey Jr, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association, 46(253), 68-78.
McCall, J. (2005). Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics, 184(1), 205-222.
Mellouk, L., Aaroud, A., Benhaddou, D., Zine-Dine, K., & Boulmalf, M. (2015). Overview of mathematical methods for energy management optimization in smart grids. In Renewable and Sustainable Energy Conference (IRSEC), 2015 3rd International (pp. 1-5). IEEE.
Meyn, S., Surana, A., Lin, Y., Oggianu, S. M., Narayanan, S., & Frewen, T. A. (2009). A sensor-utility-network method for estimation of occupancy in buildings . In Decision and Control, held jointly with the 28th Chinese Control Confere (pp. 1494-1500). Proceedings of the 48th IEEE Conference.
Milenkovic, M., & Amft, O. (2013). Recognizing energy-related activities using sensors commonly installed in office buildings. Procedia Computer Science, 19, 669-677.
Mohammadmoradi, H., Yin, S., & Gnawali, O. (2017). Room occupancy estimation through WiFi, UWB, and light sensors mounted on doorways . Proceedings of the 2017 International Conference on Smart Digital Environment (pp. 27-34). ACM.
Moreno, M. V., Dufour, L., Skarmeta, A. F., Jara, A. J., Genoud, D., Ladevie, B., & Bezian, J. J. (2016). Big data: the key to energy efficiency in smart buildings. Soft Computing, 20(5), 1749-1762.
Mustapa, M. S., Zaki, S. A., Rijal, H. B., Hagishima, A., & Ali, M. S. (2016). Thermal comfort and occupant adaptive behaviour in Japanese university buildings with free running and cooling mode offices during summer. Building and Environment, 105, 332-342.
Nagarathinam, S., Doddi, H., Vasan, A., Sarangan, V., Ramakrishna, P. V., & Sivasubramaniam, A. (2017). Energy efficient thermal comfort in open-plan office buildings. Energy and Buildings, 139, 476-486.
Nagy, Z., Yong, F. Y., & Schlueter, A. (2016). Occupant centered lighting control: A user study on balancing comfort, acceptance, and energy consumption. Energy and Buildings, 126, 310-322.
Nagy, Z., Yong, F. Y., Frei, M., & Schlueter, A. (2015). Occupant centered lighting control for comfort and energy efficient building operation. Energy and Buildings, 94, 100-108.
Nasir, N., Palani, K., Chugh, A., Prakash, V. C., Arote, U., Krishnan, A. P., & Ramamritham, K. (2015). Fusing sensors for occupancy sensing in smart buildings. In International Conference on Distributed Computing and Internet Technology (pp. 73-92). Springer, Cham.
Nesa, N., & Banerjee, I. (2017). IoT-based sensor data fusion for occupancy sensing using Dempster–Shafer evidence theory for smart buildings. IEEE Internet of Things Journal, 4(5), 1563-1570.
Newsham, G. R., & Birt, B. J. (2010). Building-level occupancy data to improve ARIMA-based electricity use forecasts. In Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building (pp. 13-18). ACM.
Newsham, G. R., Xue, H., Arsenault, C., Valdes, J. J., Burns, G. J., Scarlett, E., & ... & Shen, W. (2017). Testing the accuracy of low-cost data streams for determining single-person office occupancy and their use for energy reduction of building services. Energy and Buildings, 135, 137-147.
Nguyen, T. A., & Aiello, M. (2012). Beyond Indoor Presence Monitoring with Simple Sensors. PECCS, 5-14.
Nguyen, T. A., & Aiello, M. (2013). Energy intelligent buildings based on user activity: A survey. Energy and buildings, 56, 244-257.
NLPIP, N. L. (1998). Occupancy Sensors: Motion Sensors for Lighting Control. Troy (NY): Lighting Research Center.
NRCAN. (2012). Improving Energy Performance in Canada. Minister of Natural Resources Canada, Government of Canada.
NRCAN. (2016). Improving Energy Performance in Canada. Natural Resources Canada.
NRCAN. (2018). Energy Efficiency Trends Analysis Tables. Ottawa: Statistics Canada.
Oldewurtel, F., Sturzenegger, D., & Morari, M. (2013). Importance of occupancy information for building climate control. Applied energy, 101(1), 521-532.
Ott, R. L., & Longnecker, M. T. (2015). An introduction to statistical methods and data analysis. Nelson Education.
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.
Panda, S., & Padhy, N. P. (2008). Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Applied soft computing, 8(4), 1418-1427.
Pandharipande, A., & Caicedo, D. (2011). Daylight integrated illumination control of LED systems based on enhanced presence sensing. Energy and Buildings, 43(4), 944-950.
Pandharipande, A., & Caicedo, D. (2015). Smart indoor lighting systems with luminaire-based sensing: A review of lighting control approaches. Energy and Buildings, 104, 369-377.
Pang, Z., Xu, P., O'Neill, Z., Gu, J., Qiu, S., Lu, X., & Li, X. (2018). Application of mobile positioning occupancy data for building energy simulation: An engineering case study. Building and Environment, 141, 1-15.
Papantoniou, S., Kolokotsa, D., & Kalaitzakis, K. (2015). Building optimization and control algorithms implemented in existing BEMS using a web based energy management and control system. Energy and Buildings, 98, 45-55.
Pasini, D., Ventura, S. M., Rinaldi, S., Bellagente, P., Flammini, A., & Ciribini, A. L. (2016). Exploiting Internet of Things and building information modeling framework for management of cognitive buildings. In Smart Cities Conference (ISC2) (pp. 1-6). IEEE.
Patel, K. K., Patel, S. M., & Scolar, P. G. (2016). Internet of Things-IOT: Definition, Characteristics, Architecture, Enabling Technologies, Application & Future Challenges. International Journal of Engineering Science, 6122-6131.
Pavlovas, V. (2004). Demand controlled ventilation: A case study for existing Swedish multifamily buildings. Energy and buildings, 36(10), 1029-1034.
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.
Peruffo, A., Pandharipande, A., Caicedo, D., & Schenato, L. (2015). Lighting control with distributed wireless sensing and actuation for daylight and occupancy adaptation. Energy and Buildings, 97, 13-20.
Pfafferott, J., & Herkel, S. (2007). Statistical simulation of user behaviour in low-energy office buildings. Solar Energy, 81(5), 676-682.
Pingel, J. (2017). Introduction to Deep Learning: Machine Learning vs. Deep Learning. MathWorks.
Piselli, C., & Pisello, A. L. (2019). Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance. Energy, 176, 667-681.
Pradhan, A., Ergen, E., & Akinci, B. (2009). Technological assessment of radio frequency identification technology for indoor localization. Journal of Computing in Civil Engineering, 23(4), 230-238.
Purdon, S., Kusy, B., Jurdak, R., & Challen, G. (2013). Model-free HVAC control using occupant feedback. In Local Computer Networks Workshops (LCN Workshops) (pp. 84-92). IEEE.
Quuppa. (2017). One for all. Retrieved from Quuppa Do More With Location: http://quuppa.com/applications/
Quuppa Intelligent Locating System™. (2016). Unique Technology. Retrieved from Quuppa Do More With Location: http://quuppa.com/technology/
Raji, B., Tenpierik, M. J., & van den Dobbelsteen, A. (2017). Early-stage design considerations for the energy-efficiency of high-rise office buildings. Sustainability, 9(4), 623.
Richardson, I., Thomson, M., & Infield, D. (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and buildings, 40(8), 1560-1566.
Roselli, L., Mariotti, C., Mezzanotte, P., Alimenti, F., Orecchini, G., Virili, M., & Carvalho, N. B. (2015). Review of the present technologies concurrently contributing to the implementation of the Internet of Things (IoT) paradigm: RFID, Green Electronics, WPT and Energy Harvesting. IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet) (pp. 1-3). IEEE.
Rossi, M., Pandharipande, A., Caicedo, D., Schenato, L., & Cenedese, A. (2015). Personal lighting control with occupancy and daylight adaptation. Energy and Buildings, 105, 263-272.
Rubinstein, F., & Enscoe, A. (2010). Saving energy with highly-controlled lighting in an open-plan office. Leukos, 7(1),, 7(1), 21-36.
Salimi, S., & Hammad, A. (2018). Critical Review and Research Roadmap of Office Building Energy Management Based on Occupancy Monitoring. Energy and Buildings, 182, 214-241. doi:10.1016/j.enbuild.2018.10.007
Salimi, S., Liu, Z., & Hammad, A. (2017). Simulation-based Optimization of Energy Consumption and Discomfort in Multi-Occupied Offices Considering Occupants Locations and Preferences. Proceedings of the 15th IBPSA Conference. San Francisco, CA, USA.
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.
Sangogboye, F. C., Arendt, K., Jradi, M., Veje, C., Kjærgaard, M. B., & Jørgensen, B. N. (2018). The impact of occupancy resolution on the accuracy of building energy performance simulation. Proceedings of the 5th Conference on Systems for Built Environments (pp. 103-106). Shenzen, China: ACM.
Santucci, G. (2010). The Internet of Things: Between the Revolution of the Internet and the Metamorphosis of Objects. Vision and Challenges for Realising the Internet of Things, 11-24.
Sehar, F., Pipattanasomporn, M., & Rahman, S. (2016). A peak-load reduction computing tool sensitive to commercial building environmental preferences. Applied energy, 161, 279-289.
Sehar, F., Pipattanasomporn, M., & Rahman, S. (2017). Integrated automation for optimal demand management in commercial buildings considering occupant comfort. Sustainable Cities and Society, 28, 16-29.
Serfozo, R. (2009). Basics of Applied Stochastic Processes, Probability and its Applications. Verlag Berlin Heidelberg: Springer.
Shaikh, P. H., Nor, N. B., Nallagownden, P., & Elamvazuthi, I. (2016). Intelligent multi-objective optimization for building energy and comfort management. Journal of King Saud University-Engineering Sciences, 1-10.
Shen, W., Newsham, G., & Gunay, B. (2017). Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review. Advanced Engineering Informatics, 1-13.
Shi, Y. (2001). Particle swarm optimization: developments, applications and resources. In evolutionary computation (pp. 81-86). IEEE.
Shih, H. C. (2014). A robust occupancy detection and tracking algorithm for the automatic monitoring and commissioning of a building. Energy and Buildings, 77, 270-280.
Si, W., Pan, X., & Ogai, H. (2017). Real-time Daylight Modeling Method for Lighting Systems Based on RBFNN. In Proceedings of the 9th International Conference on Computer and Automation Engineering (pp. 318-325). ACM.
Singhvi, V., Krause, A., Guestrin, C., Garrett Jr, J. H., & Matthews, H. S. (2005). Intelligent light control using sensor networks. In Proceedings of the 3rd international conference on Embedded networked sensor systems. (pp. 218-229). ACM.
Soebarto, V. I., & Williamson, T. J. (2001). Multi-criteria assessment of building performance: theory and implementation . Building and environment, 36(6), 681-690.
Soltani, M. M., Motamedi, A., & Hammad, A. (2015). Enhancing Cluster-based RFID Tag Localization using artificial neural networks and virtual reference tags. Automation in Construction, 54, 93-105.
Somayajulu, S. (2014, 10 30). Automatic Vs. Smart Buildings. Retrieved from XCHANGING BLOG : http://www.xchanging.com/blog/2014/10/30/automatic-vs-smart-buildings
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. . Evolutionary computation, 2(3), 221-248.
Srivastava, T. (2015). Difference between Machine Learning & Statistical Modeling. Analytics Vidhya - Learn everything about Analytics.
Sun, J., Feng, B., & Xu, W. (2004). Particle swarm optimization with particles having quantum behavior. In Evolutionary Computation, CEC2004 (pp. 325-331). IEEE.
Sun, K., Yan, D., Hong, T., & Guo, S. (2014). Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration. Building and Environment, 79, 1-12.
SUSRIS. (2013, July 25). International Energy Outlook 2013. Retrieved from Saudi-US Relations Inforamtion Service: http://susris.com/2013/07/25/international-energy-outlook-2013/
Tabak, V. (2008). User simulation of space utilisation. Eindhoven University Press.
Tabak, V., & de Vries, B. (2010). Methods for the prediction of intermediate activities by office occupants. Building and Environment, 45(6), 1366-1372.
Tahmasebi, F., & Mahdavi, A. (2015). A systematic assessment of the sensitivity of building performance simulation results with regard to Occupancy-related input assumptions. 14th International Conference of the International Building Performance Simulation Association (BS 2015), (pp. 1397-1403). Hyderabad, India.
Tahmasebi, F., & Mahdavi, A. (2018). On the utility of occupants’ behavioural diversity information for building performance simulation: An exploratory case study. Energy and Buildings, 176, 380-389.
Tanimoto, J., Hagishima, A., & Sagara, H. (2008). Validation of methodology for utility demand prediction considering actual variations in inhabitant behaviour schedules. Journal of Building Performance Simulation, 1(1),, 1(1), 31-42.
The Crystal. (2016). One of the World's Most Sustainable Buildings. (Siemens plc) Retrieved from https://www.thecrystal.org/
Thomas, V. C. (2018). Heat gains and losses: roofs and walls.
Tian, Z., Yang, J., Lei, Y. P., & Yang, L. (2019). Sensitivity Analysis of Infiltration Rates Impact on Office Building Energy Performance. IOP Conference Series: Earth and Environmental Science. 238, p. 012019. IOP Publishing.
Tiller, D. K., Guo, X., Henze, G. P., & Waters, C. E. (2009). The application of sensor networks to lighting control. Leukos, 5(4),, 5(4), 313-325.
TRNSYS. (2013). A TRaNsient SYstem simulation program, Version 17.1. Madison, Wisconsin: Copyright of the Board of Regents of the University of Wisconsin.
Tsanas, A., & Xifara, A. (2012). Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49, 560-567.
U.S. EIA. (2019). Annual Energy Outlook 2019 with projections to 2050. Washington, DC: U.S. Department of Energy.
van de Meugheuvel, N., Pandharipande, A., Caicedo, D., & Van Den Hof, P. P. (2014). Distributed lighting control with daylight and occupancy adaptation. Energy and Buildings, 75, 321-329.
Virote, J., & Neves-Silva, R. (2012). Stochastic models for building energy prediction based on occupant behavior assessment. Energy and Buildings, 53, 183-193.
Von Neida, B., Manicria, D., & Tweed, A. (2001). An analysis of the energy and cost savings potential of occupancy sensors for commercial lighting systems. Journal of the Illuminating Engineering Society, 30(2), 111-125.
Wang, C., Yan, D., & Jiang, Y. (2011). A novel approach for building occupancy simulation. In Building simulation , 4(2), 149-167.
Wang, D., Federspiel, C. C., & Rubinstein, F. (2005). Modeling occupancy in single person offices. Energy and buildings, 37(2), 121-126.
Wang, J., Zhai, Z. J., Jing, Y., Zhang, X., & Zhang, C. (2011). Sensitivity analysis of optimal model on building cooling heating and power system. Applied Energy, 88(12), 5143-5152.
Wang, R. (2016). An Improved Nondominated Sorting Genetic Algorithm for Multiobjective Problem. Mathematical Problems in Engineering (pp. 1-7). Hindawi Publishing Corporation.
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.
Wang, W., Chen, J., Huang, G., & Lu, Y. (2017). Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution. Applied Energy, 1-19.
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.
Wang, Y., & Shao, L. (2017a). Understanding occupancy and user behaviour through Wi-Fi-based indoor positioning. Building Research & Information, 1-13.
Wang, Y., & Shao, L. (2017b). Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning. Building and Environment, 114, 106-117.
Wang, Z., & Ding, Y. (2015). An occupant-based energy consumption prediction model for office equipment. Energy and Buildings, 109, 12-22.
Ward, A. M. (2007). In-Building Location Systems. Proceedings of the the Institution of Engineering and Technology Seminar on Location Technologies, (pp. 1-18). London, U.K.
Wei, S., Buswell, R., & Loveday, D. (2010). Probabilistic modelling of human adaptive behaviour in non-airconditioned buildings. Adapting to Change: New Thinking on Comfort (pp. 1-17). Cumberland Lodge, Windsor, UK: Network for Comfort and Energy Use in Buildings.
Wei, S., Yong, J., Ng, B., Tindall, J., Lu, Q., & Du, H. (2018). Occupant Adaptive Behaviour: an Effective Method towards Energy Efficient Buildings. CIBSE Technical Symposium. London, UK.
Wen, Y. J., & Agogino, A. M. (2008). Wireless networked lighting systems for optimizing energy savings and user satisfaction. In Wireless Hive Networks Conference, 2008. WHNC 2008. IEEE (pp. 1-7). IEEE.
Wen, Y. J., & Agogino, A. M. (2011). Control of wireless-networked lighting in open-plan offices. Lighting Research & Technology, 43(2), 235-248.
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.
Wickham, H., & Stryjewski, L. (2011). 40 years of boxplots. Statistician, 1-17.
Wong, J. K., Li, H., & Wang, S. W. (2005). Intelligent building research: a review. Automation in Construction, 14(1), 143-159.
Yamaguchi, Y., Shimoda, Y., & Mizuno, M. (2003). Development of district energy system simulation model based on detailed energy demand model. In Proceeding of Eighth International IBPSA Conference, (pp. 1443-1450). Eindhoven, Netherlands.
Yan, D., O’Brien, W., Hong, T., Feng, X., Gunay, H. B., Tahmasebi, F., & Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107, 264-278.
Yang, J., Zou, H., Jiang, H., & Xie, L. (2018). Device-free occupant activity sensing using wifi-enabled iot devices for smart homes. IEEE Internet of Things Journal, 5(5), 3991-4002.
Yang, Z., & Becerik-Gerber, B. (2014). Modeling personalized occupancy profiles for representing long term patterns by using ambient context. Building and Environment, 78, 23-35.
Yang, Z., Li, N., Becerik-Gerber, B., & Orosz, M. (2012). A multi-sensor based occupancy estimation model for supporting demand driven HVAC operations. In Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design. Society for Computer Simulation International.
Yip, S., Athienitis, A., & Lee, B. (2019). Sensitivity analysis of building form and BIPVT energy performance for net-zero energy early-design stage consideration. IOP Conference Series: Earth and Environmental Science, 238, p. 012065.
Yu, T. (2010). Modeling occupancy behavior for energy efficiency and occupants comfort management in intelligent buildings. In Machine Learning and Applications (ICMLA), Ninth International Conference (pp. 726-731). IEEE.
Yu, Z., Fung, B. C., Haghighat, F., Yoshino, H., & Morofsky, E. (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43(6), 1409-1417.
Yun, G. Y., Kim, H., & Kim, J. T. (2012). Effects of occupancy and lighting use patterns on lighting energy consumption. Energy and Buildings, 46, 152-158.
Zhang, B., Liu, Y., Rai, R., & Krovi, V. (2017). Invariant probabilistic sensitivity analysis for building energy models. Journal of Building Performance Simulation, 10(4), 392-405.
Zhang, Y. (2009). Parallel EnergyPlus and the development of a parametric analysis tool. 11th Conference of International Building Performance Association (IBPSA), (pp. 1382-1388). Glasgow, UK.
Zhang, Y. (2012). Use jEPlus as an efficient building design optimisation tool. CIBSE ASHRAE Technical Symposium. London. Retrieved from https://www.jeplus.org/wiki/doku.php?id=docs:jeplus_ea:start
Zhao, R., Sun, S., & Ding, R. (2004). Conditioning strategies of indoor thermal environment in warm climates. Energy and buildings, 36(12), 1281-1286.
Zhao, Y., Zeiler, W., Boxem, G., & Labeodan, T. (2015). Virtual occupancy sensors for real-time occupancy information in buildings. Building and Environment, 93, 9-20.
Zhao, Z., Kuendig, S., Carrera, J., Carron, B., Braun, T., & Rolim, J. (2017). Indoor location for smart environments with wireless sensor and actuator networks. IEEE 42nd Conference on Local Computer Networks (LCN) (pp. 535-538). IEEE.
Zhao, Z., Min, G., Gao, W., Wu, Y., Duan, H., & Ni, Q. (2018). Deploying Edge Computing Nodes for Large-scale IoT: A Diversity Aware Approach. IEEE Internet of Things Journal, 1-8.
Zhen, Z. N., Jia, Q. S., Song, C., & Guan, X. (2008). An indoor localization algorithm for lighting control using RFID. In Energy 2030 Conference. ENERGY 2008. (pp. 1-6). IEEE.
Zhu, P., Gilbride, M., Yan, D., Sun, H., & Meek, C. (2017). Lighting energy consumption in ultra-low energy buildings: Using a simulation and measurement methodology to model occupant behavior and lighting controls. Building Simulation, 10(6), 799-810.
Zhu, Y., Hinds, W. C., Krudysz, M., Kuhn, T., Froines, J., & Sioutas, C. (2005). Penetration of freeway ultrafine particles into indoor environments. Journal of Aerosol Science, 36(3), 303-322.
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.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top