Publications

[1] E. Azar, and C. Menassa, “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, pp. 841–853, 2012.

[2] K. Anderson, S. Lee, and C. Menassa, “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), pp. 30–39, 2014.

[3] E. Azar, and C. Menassa.” 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), pp. 63–78, 2014.

[4] S. D’Oca, V. Fabi, S. P. Corgnati, and R. K. Andersen, "Effect of thermostat and window opening occupant behavior models on energy use in homes," Building Simulation, vol. 7, no. 6, pp. 683-694, 2014.

[5] P. De Wilde, "The gap between predicted and measured energy performance of buildings: A framework for investigation," Automation in Construction, vol. 41, pp. 40-49, 2014.

[6] S. D'Oca and T. Hong, "A data-mining approach to discover patterns of window opening and closing behavior in offices," Building and Environment, vol. 82, pp. 726-739, 2014.

[7] B. Dong and K. P. Lam, "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, vol. 7, no. 1, pp. 89-106, 2014.

[8] R. Gulbinas and J. E. Taylor, "Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings," Energy and buildings, vol. 84, pp. 493-500, 2014.

[9] R. Gulbinas, R. Jain, J. Taylor, G. Peschiera, and M. Golparvar-Fard, “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), pp. 89-98, 2014.

[10] H. B. Gunay, W. O'Brien, I. Beausoleil-Morrison, R. Goldstein, S. Breslav, and A. Khan, "Coupling stochastic occupant models to building performance simulation using the discrete event system specification formalism," Journal of Building Performance Simulation, vol. 7, no. 6, pp. 457-478, 2014.

[11] H. B. Gunay, W. O'Brien, I. Beausoleil-Morrison, and B. Huchuk, "On adaptive occupant-learning window blind and lighting controls," Building Research & Information, vol. 42, no. 6, pp. 739-756, 2014.

[12] R. Jain, K. Smith, P. Culligan, and J. Taylor, “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, pp. 168-178, 2014.

[13] S. H. Jeong, R. Gulbinas, R. K. Jain, and J. E. Taylor, "The impact of combined water and energy consumption eco-feedback on conservation," Energy and Buildings, vol. 80, pp. 114-119, 2014.

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

[15] C. Li, T. Hong, and D. Yan, "An insight into actual energy use and its drivers in high-performance buildings," Applied Energy, vol. 131, pp. 394-410, 2014.

[16] C. 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.

[17] W. O'Brien and H. B. Gunay, "The contextual factors contributing to occupants' adaptive comfort behaviors in offices–A review and proposed modeling framework," Building and Environment, vol. 77, pp. 77-87, 2014.

[18] X. Ren, D. Yan, and C. Wang, "Air-conditioning usage conditional probability model for residential buildings," Building and Environment, vol. 81, pp. 172-182, 2014.

[19] Roetzel, A. Tsangrassoulis, and U. Dietrich, "Impact of building design and occupancy on office comfort and energy performance in different climates," Building and environment, vol. 71, pp. 165-175, 2014.

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

[21] K. Sun, D. Yan, T. Hong, and S. Guo, "Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration," Building and Environment, vol. 79, pp. 1-12, 2014.

[22] Q. Wang and J. E. Taylor, "Energy saving practice diffusion in online networks," Energy and Buildings, vol. 76, pp. 622-630, 2014.

[23] S. Wei, R. Jones, and P. de Wilde, "Driving factors for occupant-controlled space heating in residential buildings," Energy and Buildings, vol. 70, pp. 36-44, 2014.

[24] X. Xu, J. E. Taylor, and A. L. Pisello, "Network synergy effect: Establishing a synergy between building network and peer network energy conservation effects," Energy and Buildings, vol. 68, pp. 312-320, 2014.

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

[26] E. Azar and C. C. Menassa, "Evaluating the impact of extreme energy use behavior on occupancy interventions in commercial buildings," Energy and Buildings, vol. 97, pp. 205-218, 2015.

[27] H. Chandra-Putra, J. Chen, and C. J. Andrews, "Eco-Evolutionary Pathways Toward Industrial Cities," Journal of Industrial Ecology, vol. 19, no. 2, pp. 274-284, 2015.

[28] T. Cholewa and A. Siuta-Olcha, "Long term experimental evaluation of the influence of heat cost allocators on energy consumption in a multifamily building," Energy and Buildings, vol. 104, pp. 122-130, 2015.

[29] S. D’Oca and T. Hong, "Occupancy schedules learning process through a data mining framework," Energy and Buildings, vol. 88, pp. 395-408, 2015.

[30] X. Feng, D. Yan, and T. Hong, "Simulation of occupancy in buildings," Energy and Buildings, vol. 87, pp. 348-359, 2015.

[31] S. Guo, D. Yan, C. Peng, Y. Cui, X. Zhou, and S. Hu, "Investigation and analyses of residential heating in the HSCW climate zone of China: Status quo and key features," Building and Environment, vol. 94, pp. 532-542, 2015.

[32] J. Hetherington, A. Roetzel, and R. Fuller, "The impact of occupant behaviour on residential greenhouse gas emissions reduction," Journal of Green Building, vol. 10, no. 4, pp. 127-140, 2015.

[33] T. Hong, S. D'Oca, S. C. Taylor-Lange, W. J. Turner, Y. Chen, and 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, vol. 94, pp. 196-205, 2015.

[34] T. Hong, S. D'Oca, W. J. Turner, and S. C. Taylor-Lange, "An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework," Building and Environment, vol. 92, pp. 764-777, 2015.

[35] K. Bandurski and H. Koczyk, "Influence of stochastic internal heat gains in multifamily buildings on yearly energy demand " INSTAL （in Polish）, vol. 12, pp. 55-61, 2015.

[36] B. Kingma and W. van Marken Lichtenbelt, "Energy consumption in buildings and female thermal demand," Nature climate change, vol. 5, no. 12, pp. 1054-1056, 2015.

[37] X. Ren, D. Yan, and T. Hong, "Data mining of space heating system performance in affordable housing," Building and Environment, vol. 89, pp. 1-13, 2015.

[38] Roetzel, "Occupant behaviour simulation for cellular offices in early design stages—Architectural and modelling considerations," Building Simulation, vol. 8, no. 2, pp. 211-224, 2015.

[39] Y. Xiong, U. Krogmann, G. Mainelis, L. A. Rodenburg, and C. J. Andrews, "Indoor air quality in green buildings: A case-study in a residential high-rise building in the northeastern United States," Journal of Environmental Science and Health, Part A, vol. 50, no. 3, pp. 225-242, 2015.

[40] D. Yan, W. O’Brien, T. Hong, et al., "Occupant behavior modeling for building performance simulation: Current state and future challenges," Energy and Buildings, vol. 107, pp. 264-278, 2015.

[41] X. Zhou, D. Yan, T. Hong, and X. Ren, "Data analysis and stochastic modeling of lighting energy use in large office buildings in China," Energy and Buildings, vol. 86, pp. 275-287, 2015.

[42] K.-U. Ahn and C.-S. Park, "Correlation between occupants and energy consumption," Energy and Buildings, vol. 116, pp. 420-433, 2016.

[43] J. An, D. Yan, G. Deng, and R. Yu, "Survey and performance analysis of centralized domestic hot water system in China," Energy and Buildings, vol. 133, pp. 321-334, 2016.

[44] C. J. Andrews, "The Changing Socioeconomic Context of Buildings," Journal of Solar Energy Engineering, vol. 139, no. 1, pp. 011001-011001-10, 2016.

[45] C. J. Andrews, M. S. Allacci, J. Senick, H. C. Putra, and I. Tsoulou, "Using synthetic population data for prospective modeling of occupant behavior during design," Energy and Buildings, vol. 126, pp. 415-423, 2016.

[46] C. J. Andrews, D. Hattis, D. Listokin, J. A. Senick, G. B. Sherman, and J. Souder, "Energy-Efficient Reuse of Existing Commercial Buildings," Journal of the American Planning Association, vol. 82, no. 2, pp. 113-133, 2016.

[47] E. Azar and C. C. Menassa, "Optimizing the performance of energy-intensive commercial buildings: Occupancy-focused data collection and analysis approach," Journal of Computing in Civil Engineering, vol. 30, no. 5, p. C4015002, 2016.

[48] V. M. Barthelmes, C. Becchio, and S. P. Corgnati, "Occupant behavior lifestyles in a residential nearly zero energy building: Effect on energy use and thermal comfort," Science and Technology for the Built Environment, vol. 22, no. 7, pp. 960-975, 2016.

[49] S. Carlucci, G. Lobaccaro, Y. Li, E. C. Lucchino, and R. Ramaci, "The effect of spatial and temporal randomness of stochastically generated occupancy schedules on the energy performance of a multiresidential building," Energy and Buildings, vol. 127, pp. 279-300, 2016.

[50] X. Feng, D. Yan, and C. Wang, "On the simulation repetition and temporal discretization of stochastic occupant behaviour models in building performance simulation," Journal of Building Performance Simulation, pp. 1-13, 2016.

[51] X. Feng, D. Yan, C. Wang, and H. Sun, "A preliminary research on the derivation of typical occupant behavior based on large-scale questionnaire surveys," Energy and Buildings, vol. 117, pp. 332-340, 2016.

[52] I. Gaetani, P.-J. Hoes, and J. L. Hensen, "On the sensitivity to different aspects of occupant behaviour for selecting the appropriate modelling complexity in building performance predictions," Journal of Building Performance Simulation, pp. 1-11, 2016.

[53] I. Gaetani, P.-J. Hoes, and J. L. Hensen, "Occupant behavior in building energy simulation: towards a fit-for-purpose modeling strategy," Energy and Buildings, vol. 121, pp. 188-204, 2016.

[54] E. L. Hewitt, C. J. Andrews, J. A. Senick, R. E. Wener, U. Krogmann, and M. Sorensen Allacci, "Distinguishing between green building occupants’ reasoned and unplanned behaviours," Building Research & Information, vol. 44, no. 2, pp. 119-134, 2016.

[55] T. Hong, H. Sun, Y. Chen, S. C. Taylor-Lange, and D. Yan, "An occupant behavior modeling tool for co-simulation," Energy and Buildings, vol. 117, pp. 272-281, 2016.

[56] T. Hong, S. C. Taylor-Lange, S. D’Oca, D. Yan, and S. P. Corgnati, "Advances in research and applications of energy-related occupant behavior in buildings," Energy and Buildings, vol. 116, pp. 694-702, 2016.

[57] S. Hu, D. Yan, Y. Cui, and S. Guo, "Urban residential heating in hot summer and cold winter zones of China—Status, modeling, and scenarios to 2030," Energy Policy, vol. 92, pp. 158-170, 2016.

[58] B. R. Kingma, "The link between autonomic and behavioral thermoregulation," Temperature: Multidisciplinary Biomedical Journal, vol. 3, no. 2, p. 195, 2016.

[59] X. Liang, T. Hong, and G. Q. Shen, "Improving the accuracy of energy baseline models for commercial buildings with occupancy data," Applied Energy, vol. 179, pp. 247-260, 2016.

[60] X. Liang, T. Hong, and G. Q. Shen, "Occupancy data analytics and prediction: a case study," Building and Environment, vol. 102, pp. 179-192, 2016.

[61] W. O’Brien, I. Gaetani, S. Gilani, S. Carlucci, P.-J. Hoes, and J. Hensen, "International survey on current occupant modelling approaches in building performance simulation," Journal of Building Performance Simulation, pp. 1-19, 2016.

[62] W. O’Brien, H. B. Gunay, F. Tahmasebi, and A. Mahdavi, "A preliminary study of representing the inter-occupant diversity in occupant modelling," Journal of Building Performance Simulation, pp. 1-18, 2016.

[63] Patton et al., "Airborne Particulate Matter in Two Multi-Family Green Buildings: Concentrations and Effect of Ventilation and Occupant Behavior," International Journal of Environmental Research and Public Health, vol. 13, no. 1, p. 144, 2016.

[64] A. Roetzel, "Variability of building simulation results depending on selected weather files and conditioning set points–a case study for a residential building in Victoria, Australia," Journal of Green Building, vol. 11, no. 4, pp. 91-108, 2016.

[65] W. Tian et al., "Relative importance of factors influencing building energy in urban environment," Energy, vol. 111, pp. 237-250, 2016.

[66] C. Wang, D. Yan, H. Sun, and Y. Jiang, "A generalized probabilistic formula relating occupant behavior to environmental conditions," Building and Environment, vol. 95, pp. 53-62, 2016.

[67] D. Yan, Y. Jiang, and X. Shi, "Influence of asynchronous demand behavior on overcooling in multiple zone AC systems," Building and Environment, vol. 110, pp. 65-75, 2016.

[68] X. Yu, D. Yan, K. Sun, T. Hong, and D. Zhu, "Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings," Applied Energy, vol. 183, pp. 725-736, 2016.

[69] X. Zhou, D. Yan, X. Feng, G. Deng, Y. Jian, and Y. Jiang, "Influence of household air-conditioning use modes on the energy performance of residential district cooling systems," Building Simulation, vol. 9, no. 4, pp. 429-441, 2016.

[70] K.-U. Ahn, D.-W. Kim, C.-S. Park, and P. de Wilde, "Predictability of occupant presence and performance gap in building energy simulation," Applied Energy, 2017.

[71] J. An, D. Yan, T. Hong, and K. Sun, "A novel stochastic modeling method to simulate cooling loads in residential districts," Applied Energy, vol. 206, pp. 134-149, 2017.

[72] Z. Belafi, T. Hong, and A. Reith, "Smart building management vs. intuitive human control—Lessons learnt from an office building in Hungary," Building Simulation, pp. 1-18, 2017.

[73] H. Chandra Putra, C. J. Andrews, and J. A. Senick, "An agent-based model of building occupant behavior during load shedding," Building Simulation, journal article June 23 2017.

[74] Y. Chen, T. Hong, and X. Luo, "An agent-based stochastic Occupancy Simulator," Building Simulation, pp. 1-13, 2017.

[75] Y. Chen, X. Liang, T. Hong, and X. Luo, "Simulation and visualization of energy-related occupant behavior in office buildings," Building Simulation, pp. 1-14, 2017.

[76] S. D’Oca, C.-F. Chen, T. Hong, and Z. Belafi, "Synthesizing building physics with social psychology: An interdisciplinary framework for context and occupant behavior in office buildings," Energy Research & Social Science, vol. 34, pp. 240-251, 2017.

[77] X. Feng, D. Yan, R. Yu, and Y. Gao, "Investigation and modelling of the centralized solar domestic hot water system in residential buildings," Building Simulation, vol. 10, no. 1, pp. 87-96, 2017.

[78] N. Ghiassi, F. Tahmasebi, and A. Mahdavi, "Harnessing buildings’ operational diversity in a computational framework for high-resolution urban energy modeling," Building Simulation, pp. 1-17, 2017.

[79] T. Hong, Y. Chen, Z. Belafi, and S. D’Oca, "Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs," Building Simulation, pp. 1-14, 2017.

[80] T. Hong, D. Yan, S. D'Oca, and C.-f. Chen, "Ten questions concerning occupant behavior in buildings: the big picture," Building and Environment, vol. 114, pp. 518-530, 2017.

[81] S. Hu, D. Yan, S. Guo, Y. Cui, and B. Dong, "A survey on energy consumption and energy usage behavior of households and residential building in urban China," Energy and Buildings, vol. 148, pp. 366-378, 2017.

[82] K. Katić, R. Li, B. Kingma, and W. Zeiler, "Modelling hand skin temperature in relation to body composition," Journal of Thermal Biology, vol. 69, pp. 139-148, 2017.

[83] J. Kim, R. de Dear, T. Parkinson, and C. Candido, "Understanding patterns of adaptive comfort behaviour in the Sydney mixed-mode residential context," Energy and Buildings, vol. 141, pp. 274-283, 2017.

[84] B. Kingma, M. Schweiker, A. Wagner, and W. van Marken Lichtenbelt, "Exploring internal body heat balance to understand thermal sensation," Building Research & Information, pp. 1-11, 2017.

[85] R. Kramer, L. Schellen, H. Schellen, and B. Kingma, "Improving rational thermal comfort prediction by using subpopulation characteristics: a case study at Hermitage Amsterdam," Temperature, pp. 1-11, 2017.

[86] J. G. C. Laurent, H. W. Samuelson, and Y. Chen, "The impact of window opening and other occupant behavior on simulated energy performance in residence halls," Building Simulation, pp. 1-14, 2017.

[87] J. Lindner, S. Park, and M. Mitterhofer, "Determination of requirements on occupant behavior models for the use in building performance simulations," Building Simulation, pp. 1-14, 2017.

[88] X. Luo, K. P. Lam, Y. Chen, and T. Hong, "Performance evaluation of an agent-based occupancy simulation model," Building and Environment, vol. 115, pp. 42-53, 2017.

[89] W. T. O'Brien, I. Gaetani, S. Carlucci, P.-J. Hoes, and J. Hensen, "On occupant-centric building performance metrics," Building and Environment, 2017.

[90] H. Pallubinsky, B. R. Kingma, L. Schellen, B. Dautzenberg, M. A. van Baak, and W. D. van Marken Lichtenbelt, "The effect of warmth acclimation on behaviour, thermophysiology and perception," Building Research & Information, pp. 1-8, 2017.

[91] H. Pallubinsky, L. Schellen, B. Kingma, B. Dautzenberg, M. van Baak, and W. van Marken Lichtenbelt, "Thermophysiological adaptations to passive mild heat acclimation," Temperature, pp. 1-11, 2017.

[92] S. Pan et al., "Cluster analysis for occupant-behavior based electricity load patterns in buildings: A case study in Shanghai residences," Building Simulation, pp. 1-10, 2017.

[93] M. Schweiker, B. Kingma, and A. Wagner, "Evaluating the performance of thermal sensation prediction with a biophysical model," Indoor Air, 2017.

[94] K. Sun and T. Hong, "A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures," Energy and Buildings, vol. 146, pp. 383-396, 2017.

[95] K. Sun and T. Hong, "A simulation approach to estimate energy savings potential of occupant behavior measures," Energy and Buildings, vol. 136, pp. 43-62, 2017.

[96] W. van Marken Lichtenbelt, M. Hanssen, H. Pallubinsky, B. Kingma, and L. Schellen, "Healthy excursions outside the thermal comfort zone," Building Research & Information, pp. 1-9, 2017.

[97] S. Veselá, B. Kingma, and A. Frijns, "Local thermal sensation modeling—a review on the necessity and availability of local clothing properties and local metabolic heat production," Indoor air, vol. 27, no. 2, pp. 261-272, 2017.

[98] P. Xue, T. Hong, B. Dong, and C. Mak, "A preliminary investigation of water usage behavior in single-family homes," Building Simulation, pp. 1-14, 2017.

[99] Yan, T. Hong, C. Li, Q. Zhang, J. An, and S. Hu, "A thorough assessment of China’s standard for energy consumption of buildings," Energy and Buildings, vol. 143, pp. 114-128, 2017.

[100] S. Yilmaz, S. K. Firth, and D. Allinson, "Occupant behaviour modelling in domestic buildings: the case of household electrical appliances," Journal of Building Performance Simulation, pp. 1-19, 2017.

[101] Q. Zhang, D. Yan, J. An, T. Hong, W. Tian, and K. Sun, "Spatial distribution of internal heat gains: A probabilistic representation and evaluation of its influence on cooling equipment sizing in large office buildings," Energy and Buildings, vol. 139, pp. 407-416, 2017.

[102] X. Zhou and D. Yan, "Influence of load feature on the water distribution system in a centralized air-conditioning system," Science and Technology for the Built Environment, vol. 23, no. 2, pp. 277-284, 2017.

[103] X. Zhou, D. Yan, and X. Shi, "Comparative research on different air conditioning systems for residential buildings," Frontiers of Architectural Research, vol. 6, no. 1, pp. 42-52, 2017.

[104] P. Zhu, M. Gilbride, D. Yan, H. Sun, and C. Meek, "Lighting energy consumption in ultra-low energy buildings: Using a simulation and measurement methodology to model occupant behavior and lighting controls," Building Simulation, pp. 1-12, 2017.

[105] S. D’Oca, T. Hong, and J. Langevin, "The human dimensions of energy use in buildings: A review," Renewable and Sustainable Energy Reviews, vol. 81, pp. 731-742, 2018.

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 8^th 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 8^th 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: 13^th 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)

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