EBC Annex 66 Text

Definition and Simulation of Occupant Behavior in Buildings

EBC Annex 66 Factsheet


[1] Annex 66 Newsletter No. 4 November 2016 

[2] Annex 66 Newsletter No. 3 March 2016 

[3] Annex 66 Newsletter No. 2 July 2015 (in English); Annex 66 Newsletter No.2 July 2015 (in German); Annex 66 Newsletter No. 2 July 2015 (in French): Thank you to Quentin Darakdjian and Sebastian Wolf for the French and German translations, respectivley. 

[4] Annex 66 Newsletter No. 1 October 2014 

Journal Publications

[1] Y. Chen, T. Hong, X. Luo. An agent-based stochastic occupancy simulator. Building Simulation. 2017.

[2] K. Sun, T. Hong. A Framework for Quantifying the Impact of Occupant Behavior on Energy Savings of Energy Conservation Measures. Energy and Buildings, 2017.

[3] Y. Chen, X. Liang, T. Hong, X. Luo. Simulation and visualization of energy-related occupant behavior in office buildings. Building Simulation, 2017.

[4] X. Luo, K.P. Lam, Y. Chen, T. Hong. Performance Evaluation of an Agent-based Occupancy Simulation Model. Building and Environment, 2017.

[5] T. Hong, D. Yan, S. D’Oca, C. Chen. Ten questions concerning occupant behavior in buildings: The big picture. Building and Environment, 2017.

[6] Z. Belafi, T. Hong, A. Reith. Smart building management VS. Intuitive human control — Lessons learnt from an office building in Hungary. Building Simulation, 2017.

[7] K. Sun, T. Hong. A Simulation Approach to Estimate Energy Savings Potential of Occupant Behavior Measures, Energy and Buildings, 2016.

[8] X. Yu, D. Yan, K. Sun, T. Hong, D. Zhu. Comparative Study of the Cooling Energy Performance of Variable Refrigerant Flow Systems and Variable Air Volume Systems in Office Buildings. Applied Energy, 2016.

[9] X. Liang, T. Hong, Q. Shen. Improving the accuracy of energy baseline models for commercial buildings with occupancy data. Applied Energy, 2016. 

[10] X. Liang, T. Hong, G. Shen. Occupancy data analytics and prediction: A case study. Building and Environment, 2016.

[11]  Kim J, de Dear R, Parkinson T, et al. Understanding patterns of adaptive comfort behaviour in the Sydney mixed-mode residential context[J]. Energy and Buildings, 2017, 141: 274-283 (link).

[12] T. Hong, Y. Chen, S.C. Taylor-Lange, H. Sun, D. Yan. An occupant behavior modeling tool for co-simulation. Energy and Buildings, 2015 (link).

[13] T. Hong, S. D’Oca, S.C. Taylor-Lange, W. J.N. Turner, Y. Chen, S. P. Corgnati. An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAs Framework using an XML schema. Building and Environment, 2015 (link).

[14] D. Yan, W. O’Brien, T. Hong, X. Feng, H. B. Gunay, F. Tahmasebi, A. Mahdavi. Occupant behavior modeling for building performance simulation: current state and future challenges. Energy and Buildings, 2015 (link).

[15] T. Hong, S.C. Taylor-Lange, S. D’Oca, D. Yan, S. Corgnati. Advances in Research and Applications of Energy-Related Occupant Behavior in Buildings. Energy and Buildings, Engineering Advances, 2015 (link).

[16] Roetzel, Astrid 2015, Occupant behaviour simulation for cellular offices in early design stages - Architectural and modelling considerations, Building Simulation, 8(2), 211-224 (link).

[17] D’Oca S and Hong T (2015) Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88, 395-408 (link).

[18] D’Oca S., Fabi V, Corgnati SP, Andersen RK, (2015) Effect of thermostat and window opening occupant behavior models on energy use in homes, Building Simulation, 7(6), 683-694 (link).

[19] Hong T, D’Oca S, Turner WJN, Taylor-Lange SC, (2015) An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework, Building and Environment, 92, 764-777 (link).

[20] Ren X, Yan D, Hong T. (2015) Data Mining of Space Heating System Performance in Affordable Housing. Building and Environment, 89, 1-13 (link).

[21] Feng X, Yan D, Hong T. (2015) Simulation of occupancy in buildings. Energy and Buildings, 87, 348-359 (link).

[22] D’Oca S., Hong T. (2014) A data-mining approach to discover patterns of window opening and closing behavior in offices, Building and Environment, 82, 726-739 (link)

[23] Roetzel, Astrid, Tsangrassoulis, Aris and Dietrich, Udo 2014, Impact of building design and occupancy on office comfort and energy performance in different climates, Building and environment, 71, 165-175 (link).

[24] Li C., Hong T., Yan D. (2014) An insight into actual energy use and its drivers in high-performance buildings, Applied Energy, 131, 394-410 (link).

[25] de Wilde, P. (2014) The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in Construction, 41, 40-49 (link).

[26] Wei S., Jones R., de Wilde P. (2014) Driving factors for occupant-controlled space heating in residential buildings. Energy and Buildings, 70, 36-44 (link).

[27] Zhao J., Lasternas B., Lam K.P., Yun R., Loftness V. (2014) Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energy and Buildings, 82, 341-355 (link).

[28] 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 (link).

[29] Zhou X., Yan D., Hong T., Ren X. (2014) Data analysis and stochastic modeling of lighting energy use in large office buildings in China. Energy and Buildings, 86, 275-287 (link).

[30] Ren X., Yan D., Wang C. (2014) Air-conditioning Usage Conditional Probability Model for Residential Buildings. Building and Environment, 81, 172-182 (link).

[31] D'Oca S., Fabi V., Corgnati S.P., Andersen R.K. (2014) Effect of thermostat and window opening occupant behavior models on energy use in homes. Building Simulation: An International Journal, 7, 683-694 (link).

[32] Gulbinas R. and Taylor J. (2014) Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings. Energy and Buildings, 84, 493-500 (link).

[33] Jeong S., Gulbinas R., Jain R. and Taylor J. (2014) The impact of combined water and energy consumption eco-feedback on conservation. Energy and Buildings, 80, 114-119 (link).

[34] Wang Q. and Taylor J. (2014) Energy saving practice diffusion in online networks, Energy and Buildings. 76, 622-630 (link).

[35] Jain R., Smith K., Culligan P. and Taylor J. (2014) Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123, 168-178 (link).

[36] Xu X., Taylor J. and Pisello A. (2014) Network synergy effect: Establishing a synergy between building network and peer network energy conservation effects. Energy and Buildings, 68A, 312-320 (link).

[37] Gulbinas R., Jain R., Taylor J., Peschiera G., and Golparvar-Fard M. (2014) Network eco-informatics: Development of a social eco-feedback system to drive energy efficiency in residential buildings. ASCE Journal of Computing in Civil Engineering, 28(1): 89-98 (link).

[38] Kjærgaard M.B., Blunck H. (2014) Tool support for detection and analysis of following and leadership behavior of pedestrians from mobile sensing data. Pervasive and Mobile Computing, 10, 104-117 (link).

[39] Ruiz A.J., Blunck H., Prentow T.S., Stisen A., Kjærgaard M.B. (2014) Analysis methods for extracting knowledge from large-scale WiFi monitoring to inform building facility planning. PerCom, 130-138.

[40] Gunay H.B., O'Brien W., Beausoleil-Morrison I., Huchuk B. (2014) On adaptive occupant-learning window blind and lighting controls. Building Research & Information, 1-18 (link).

[41] Gunay H.B., O'Brien W., Beausoleil-Morrison I., Goldstein R., Breslav R., Khan A. (2014) Coupling Stochastic Occupant Models to Building Performance Simulation using the Discrete Event System Specification (DEVS) Formalism. Journal of Building Performance Simulation, 7, 457-478 (link).

[42] O'Brien W. and Gunay H.B. (2014) The contextual factors contributing to occupants' adaptive comfort behaviors in offices: A review and proposed modeling framework. Building and Environment, 77, 77-87 (link).

[43] Dong B. and Lam K.P. (2014) A real-time 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 (link).

[44] Cholewa T. and Siuta-Olcha A. (2015) Long term experimental evaluation of the influence of heat cost allocators on energy consumption in a multifamily building. Energy and Buildings, (accepted). 

[45] Azar, E. and Menassa, C. (2015). Optimizing the performance of energy-intensive commercial buildings: An occupancy-focused data collection and analysis approach. Journal of Computing in Civil Engineering, American Society of Civil Engineers (ASCE). (accepted).

[46] Azar, E. and Menassa, C. (2015). Evaluating the impact of extreme energy use behavior on occupancy interventions in commercial buildings. Energy and Buildings, 97, 205-218 (link).

[47] Menassa, C., Kamat, V., Lee, S., Azar, E., Feng, C. and Anderson, K. (2014). A conceptual framework to optimize building energy consumption by coupling distributed energy simulation and occupancy models. Journal of Computing in Civil Engineering –Special Issue on Computational Approaches to Understand and Reduce Energy Consumption in the Built Environment, ASCE. 28 (1), 50-62 (link).

[48] Azar, E. and Menassa, C. (2014). A framework to evaluate energy saving potential from occupancy interventions in typical US commercial buildings. Journal of Computing in Civil Engineering - Special Issue on Computational Approaches to Understand and Reduce Energy Consumption in the Built Environment, ASCE. 28 (1), 63–78 (link).

[49] Anderson, K., Lee, S. and Menassa, C. (2014). Impact of social network type and structure on modeling normative energy use behavior interventions. Journal of Computing in Civil Engineering –Special Issue on Computational Approaches to Understand and Reduce Energy Consumption in the Built Environment, ASCE. 28 (1), 30–39 (link).

[50] Azar, E. and Menassa, C. (2012). A comprehensive analysis of the impact of occupancy parameters in energy simulation of commercial buildings. Energy and Buildings – Special Issue: Cool Roofs, Cool Pavements, Cool Cities, and Cool World, 55 , 841–853 (link).


Conference Proceedings

[1]     Roetzel, Astrid 2014, Considerations for occupant behaviour modelling in early design stages, in Proceedings of 8th Windsor Conference : Counting the Cost of Comfort in a Changing World, NCEUB : Network for Comfort and Energy Use in buildings, London, UK, pp. 1-16. 

[2]     Rafiq M., Wei S., Guest R., Stone R., de Wilde P. (2014) Applying Artificial Neural Networks to promote behaviour change for saving residential energy. In: Madani, ed. ANNIIP - International Workshop on Artificial Neural Networks and Intelligent Information Processing, Austria, 1-10.

[3]     Wei S., Rafiq Y., de Wilde P. (2014) Using artificial neural networks to assess reduction in residential energy demand by changing occupant behaviour. In: Li, Rafiq and de Wilde, eds. EG-ICE 2014, Conference on Intelligent Computing in Engineering, UK.

[4]     Wei S., Jones R., de Wilde P. (2014) Extending the UK's Green Deal with the consideration of occupant behaviour. In: Malki- Epsthein, Spataru, Marjanovic- Halburd and Mumovic, eds. Building Simulation and Optimisation UK.

[5]     Wei. S., Jones R., de Wilde P. (2014) Using building performance simulation to quantify the impact of energy saving behaviour change for a UK house. In: Nicol, Road, Brotas and Humhreys, eds. NCEUB Windsor Conference, UK.

[6]      de Wilde P. and Jones R. (2014) The energy performance gap: up close and personal. CIBSE ASHRAE Technical Symposium, Ireland.

[7]   Lam K.P., Zhao J., Ydstie E.B., Wirick J., Qi M., Park J. (2014) An EnergyPlus whole building energy model calibration method for office buildings using occupant behavior data mining and empirical data.Proceedings of 2014 ASHRAE/IBPSA-USA Building Simulation Conference, GA, 160-167.

[8]   Yun R. (2014) Persistent Workplace Plug-load Energy Savings and Awareness through Energy Dashboards: Feedback, Control, and Automation. In CHI'14 Extended Abstracts on Human Factors in Computing Systems, ACM.

[9]   Yun R., Aziz A., Lasternas B., Loftness V., Scupelli P., Zhang C., Mo Y., Zhao J. (2014) The Design and Evaluation of Intelligent Energy Display for Sustainability in the Workplace. In Proceedings, HCI International 2014, Greece.

[10]   Yun R., Scupelli P., Aziz A., Lasternas B., Loftness V. (2014) Investigating Sustainability Stages in the Workplace. In Proceedings, HCI International 2014, Greece.

[11]   Corgnati S.P., D'Oca S., Fabi V., Andersen R.K (2014) Leverage of Behavioural Patterns of Window Opening and Heating Set Point Adjustments on Energy Consumption and Thermal Comfort in Residential Buildings, Proceedings of the 8th International Symposium on Heating, Ventilation and Air Conditioning.

[12]   Fabi V., Camisassi V., Causone F., Corgnati S.P., Andersen R.K. (2014) Light switch behaviour: occupant behaviour stochastic models in office buildings, Proceedings of 8th Windsor Conference: Counting the Cost of Comfort in a changing world Cumberland Lodge, UK.

[13]   Fabi V., Maggiora V., Corgnati S.P., Andersen R. (2014) Occupants??behaviour in office building: stochastic models for window, Proceedings of 8th Windsor Conference: Counting the Cost of Comfort in a changing world Cumberland Lodge, UK.

[14]   Kitazawa S., Andersen R.K., Wargocki P., Kolarik J., Schweiker M. (2014) Seasonal differences in human responses to slowly increasing temperatures, Indoor Air 2014: 13th International Conference on Indoor Air Quality and Climate, Hong Kong.

[15]  Xu X., Culligan P. and Taylor J. (2014) Energy Saving Alignment Strategy: Achieving Energy Efficiency in Urban Buildings by Matching Occupant Temperature Preferences with a Building's Indoor Thermal Environment, Applied Energy, 123, 209-219.

[16]  Prentow T.S., Blunck H., Grønbæk K., Kjærgaard M.B. (2014) Estimating Common Pedestrian Routes through Indoor Path Networks using Position Traces. IEEE International Conference on Mobile Data Management. Proceedings.

[17]  Duan Y.F. and Dong B. (2014) The impact of occupancy behavior on energy consumption in low income residential buildings, 2014 Purdue High Performance Building Conference. 

[18]  Lasternas B., Zhao J., Yun R., Zhang C., Wang H., Aziz A., Lam K.P., Loftness V. (2014) Behavior Oriented Metrics for Plug Load Energy Savings in Office Environment. Proceedings of 2014 American Council for an Energy-Efficient Economy (ACEEE) Summer Study on Energy Efficiency in Buildings, CA, 7, 160-172.

[19] Marco Baratieri, Vincenzo Corrado, Andrea Gasparella, Francesco Patuzzi (editors), Building Simulation Applications BSA 2015. 2nd IBPSA-Italy conference, Bozen-Bolzano 4th-6th  February 2015. (link