Frontiers in Energy

, Volume 12, Issue 2, pp 314–332 | Cite as

Energy modeling and data structure framework for Sustainable Human-Building Ecosystems (SHBE) — a review

  • Suraj Talele
  • Caleb Traylor
  • Laura Arpan
  • Cali Curley
  • Chien-Fei Chen
  • Julia Day
  • Richard Feiock
  • Mirsad Hadzikadic
  • William J. Tolone
  • Stan Ingman
  • Dale Yeatts
  • Omer T. Karaguzel
  • Khee Poh Lam
  • Carol Menassa
  • Svetlana Pevnitskaya
  • Thomas Spiegelhalter
  • Wei Yan
  • Yimin Zhu
  • Yong X. Tao
Review Article


This paper contributes an inclusive review of scientific studies in the field of sustainable human building ecosystems (SHBEs). Reducing energy consumption by making buildings more energy efficient has been touted as an easily attainable approach to promoting carbon-neutral energy societies. Yet, despite significant progress in research and technology development, for new buildings, as energy codes are getting more stringent, more and more technologies, e.g., LED lighting, VRF systems, smart plugs, occupancy-based controls, are used. Nevertheless, the adoption of energy efficient measures in buildings is still limited in the larger context of the developing countries and middle income/low-income population. The objective of Sustainable Human Building Ecosystem Research Coordination Network (SHBE-RCN) is to expand synergistic investigative podium in order to subdue barriers in engineering, architectural design, social and economic perspectives that hinder wider application, adoption and subsequent performance of sustainable building solutions by recognizing the essential role of human behaviors within building-scale ecosystems. Expected long-term outcomes of SHBE-RCN are collaborative ideas for transformative technologies, designs and methods of adoption for future design, construction and operation of sustainable buildings.


sustainability building energy modeling (BEM) occupant behaviors (OB) sustainable ecosystems System for the Observation of Populous Heterogeneous Information (SOPHI) 


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The support through a grant from US National Science Foundation (Award# 1338851) is greatly appreciated. The SHBERCN activities enjoy the broad supports from IEA Annex 66 group, US DOE’s Building Technology Office, and Lawrence Berkeley National Laboratories.


  1. 1.
    United Nations Framework Convention on Climate Change (UNFCCC). 2017–11, Scholar
  2. 2.
    RCN-SEES-SHBE. Predictive modeling network for Sustainable Human-Building Ecosystems. 2017–11, Scholar
  3. 3.
    Batista C, Ribeiro R M, Teixeira V. Synthesis and characterization of VO2-based thermochromic thin films for energy-efficient windows. Nanoscale Research Letters, 2011, 6(1): 301Google Scholar
  4. 4.
    Wetter M, Zuo W, Nouidui T S, Pang X. Modelica buildings library. Journal of Building Performance Simulation, 2014, 7(4): 253–270Google Scholar
  5. 5.
    Jeong W S, Kim J B, Clayton M J, Haberl J S, Yan W. A framework to integrate object-oriented physical modelling with building information modelling for building thermal simulation. Journal of Building Performance Simulation, 2016, 9(1): 50–69Google Scholar
  6. 6.
    Kim J B, Jeong W, Clayton M J, Haberl J S, Yan W. Developing a physical BIM library for building thermal energy simulation. Automation in Construction, 2015, 50(C): 16–28Google Scholar
  7. 7.
    Yan W, Asl M R, Su Z, Altabtabai J. Towards multi-objective optimization for sustainable buildings with both quantifiable and non-quantifiable design objectives. In: 1st International Symposium on Sustainable Human-Building Ecosystems. Pittsburgh, USA, 2015, 223–230Google Scholar
  8. 8.
    Asl MR, Stoupine A, Zarrinmehr S, Yan W. Optimo: a BIM-based multi-objective optimization tool utilizing visual programming for high performance building design. In: eCAADe 2015—the 33rd Annual Conference. Vienna, Austria, 2015, 1: 673–682Google Scholar
  9. 9.
    Kim H, Asl MR, Yan W. Parametric BIM-based energy simulation for buildings with complex kinetic façades. In: eCAADe 2015—the 33rd Annual Conference. Vienna, Austria, 2015, 1: 657–664Google Scholar
  10. 10.
    Sovacool B K, Ryan S E, Stern P C, Janda K, Rochlin G, Spreng D, Pasqualetti M J, Wilhite H, Lutzenhiser L. Integrating social science in energy research. Energy Research & Social Science, 2015, 6: 95–99Google Scholar
  11. 11.
    Hong T, Yan D, D’Oca S, Chen C F. Ten questions concerning occupant behavior in buildings: the big picture. Building and Environment, 2017, 114: 518–530Google Scholar
  12. 12.
    Chen C F, Xu X, Arpan L. Between the technology acceptance model and sustainable energy technology acceptance model: investigating smart meter acceptance in the United States. Energy Research & Social Science, 2017, 25: 93–104Google Scholar
  13. 13.
    Chen C F, Xu X, Day J. Thermal comfort or money saving? Exploring intentions to conserve energy among low-income households in the United States. Energy Research & Social Science, 2017, 26: 61–71Google Scholar
  14. 14.
    Chen C F, Xu X, Frey S. Who wants solar water heaters and alternative fuel vehicles? Assessing social-psychological predictors of adoption intention and policy support in China. Energy Research & Social Science, 2016, 15: 1–11Google Scholar
  15. 15.
    Xu X, Arpan L, Chen C F. The moderating role of individual differences in responses to benefit and temporal framing of messages promoting residential energy saving. Journal of Environmental Psychology, 2015, 44: 95–108Google Scholar
  16. 16.
    Xu X, Maki A, Chen C F, Dong B, Day J. Predicting workplace energy-saving intentions and communication: an application of the attitude-behavior-condition model. Energy Research and Social Science, 2017Google Scholar
  17. 17.
    Nilsson A, Andersson K, Bergstad C. Energy behaviors at the office: an intervention study on the use of equipment. Applied Energy, 2015, 146: 434–441Google Scholar
  18. 18.
    Stern P C, Janda K B, Brown M A, Steg L, Vine E L, Lutzenhiser L. Opportunities and insights for reducing fossil fuel consumption by households and organizations. Nature Energy, 2016, 1(5): 16043Google Scholar
  19. 19.
    Karatasou S, Laskari M, Santamouris M. Models of behavior change and residential energy use: a review of research directions and findings for behavior-based energy efficiency. Advances in Building Energy Research, 2014, 8(2): 137–147Google Scholar
  20. 20.
    Stern P. New environmental theories: toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 2000, 56(3): 407–424Google Scholar
  21. 21.
    Abrahamse W, Steg L, Vlek C, Rothengatter T. A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 2005, 25(3): 273–291Google Scholar
  22. 22.
    Yan D, O’Brien W, Hong T, Feng X, Burak Gunay H, Tahmasebi F, Mahdavi A. Occupant behavior modelling for building performance simulation: current state and future challenges. Energy and Building, 2015, 107: 264–278Google Scholar
  23. 23.
    Turner W, Hong T. A technical framework to describe occupant behavior for building energy simulations. In: the 2014 Behavior, Energy, and Climate Change ( BECC ) Conference. Washington, DC, USA, 2014Google Scholar
  24. 24.
    Zhou X, Yan D, Hong T, Ren X. Data analysis and stochastic modeling of lighting energy use in large office buildings in China. Energy and Building, 2015, 86: 275–287Google Scholar
  25. 25.
    Azar E, Menassa C C. Evaluating the impact of extreme energy use behavior on occupancy interventions in commercial buildings. Energy and Building, 2015, 97: 205–218Google Scholar
  26. 26.
    D’Oca S, Hong T. A data-mining approach to discover patterns of window opening and closing behavior in offices. Building and Environment, 2014, 82: 726–739Google Scholar
  27. 27.
    Kjaergaard M, Blunck H. Tool support for detection and analysis of following and leadership behavior of pedestrians from mobile sensing data. Pervasive and Mobile Computing, 2014, 10(Part A): 104–117Google Scholar
  28. 28.
    Ruiz-Ruiz A J, Blunck H, Prentow T S, Stisen A, Kjaergaard M B. Analysis methods for extracting knowledge from large-scale WIFI monitoring to inform building facility planning. In: 2014 12th IEEE International Conference on Pervasive Computing and Communications (PerCom 2014). Budapest, Hungary, 2014, 130–138Google Scholar
  29. 29.
    D’Oca S, Hong T. Occupancy schedules learning process through a data mining framework. Energy and Building, 2015, 88: 395–408Google Scholar
  30. 30.
    Azar E, Menassa C C. A comprehensive analysis of the impact of occupant parameters in energy simulation of office buildings. Energy and Building, 2012, 55: 841–853Google Scholar
  31. 31.
    Gulbinas R, Taylor J E. Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings. Energy and Building, 2014, 84: 493–500Google Scholar
  32. 32.
    Gunay H B, O’Brien W, Beausoleil-Morrison I, Goldstein R, Breslav S, Khan A. Coupling stochastic occupant models to building performance simulation using the discrete event system specification formalism. Journal of Building Performance Simulation, 2014, 7(6): 457–478Google Scholar
  33. 33.
    Jain R K, Smith K M, Culligan P J, Taylor J E. 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, 2014, 123: 168–178Google Scholar
  34. 34.
    Wang Q, Taylor J E. Energy saving practice diffusion in online networks. Energy and Building, 2014, 76: 622–630Google Scholar
  35. 35.
    D’Oca S, Fabi V, Corgnati S P, Andersen R K. Effect of thermostat and window opening occupant behavior models on energy use in homes. Building Simulation, 2014, 7(6): 683–694Google Scholar
  36. 36.
    Wei S, Jones R, de Wilde P. Driving factors for occupantcontrolled space heating in residential buildings. Energy and Building, 2014, 70: 36–44Google Scholar
  37. 37.
    Gulbinas R, Jain R K, Taylor J E, Peschiera G, Golparvar-Fard M. Network ecoinformatics: development of a social ecofeedback system to drive energy efficiency in residential buildings. Journal of Computing in Civil Engineering, 2014, 28(1): 89–98Google Scholar
  38. 38.
    Dong B, Lam K P. 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, 2014, 7(1): 89–106Google Scholar
  39. 39.
    Gunay H B, O’Brien W, Beausoleil-Morrison I, Huchuk B. On adaptive occupant-learning window blind and lighting controls. Building Research and Information, 2014, 42(6): 739–756Google Scholar
  40. 40.
    de Wilde P. The gap between predicted and measured energy performance of buildings: a framework for investigation. Automation in Construction, 2014, 41: 40–49Google Scholar
  41. 41.
    de wilde P, Jones R. The building energy performance gap: up close and personal. In: CIBSE ASHRAE Technical Symposium. Dublin, Ireland, 2014Google Scholar
  42. 42.
    Li C, Hong T, Yan D. An insight into actual energy use and its drivers in high-performance buildings. Applied Energy, 2014, 131: 394–410Google Scholar
  43. 43.
    Roetzel A, Tsangrassoulis A, Dietrich U. Impact of building design and occupancy on office comfort and energy performance in different climates. Building and Environment, 2014, 71: 165–175Google Scholar
  44. 44.
    Roetzel A. Occupant behavior simulation for cellular offices in early design stages—architectural and modelling considerations. Building Simulation, 2015, 8(2): 211–224Google Scholar
  45. 45.
    Sun K, Yan D, Hong T, Guo S. Stochastic modelling of overtime occupancy and its application in building energy simulation and calibration. Building and Environment, 2014, 79: 1–12Google Scholar
  46. 46.
    Kingma B, van Marken Lichtenbelt W. Energy consumption in buildings and female thermal demand. Nature Climate Change, 2015, 5(12): 1054–1056Google Scholar
  47. 47.
    Zhao J, Lasternas B, Lam K P, Yun R, Loftness V. Occupant behavior and schedule modelling for building energy simulation through office appliance power consumption data mining. Energy and Building, 2014, 82: 341–355Google Scholar
  48. 48.
    Feng X, Yan D, Hong T. Simulation of occupancy in buildings. Energy and Building, 2015, 87: 348–359Google Scholar
  49. 49.
    Jeong S H, Gulbinas R, Jain R K, Taylor J E. The impact of combined water and energy consumption eco-feedback on conservation. Energy and Building, 2014, 80: 114–119Google Scholar
  50. 50.
    O’Brien W, Gunay H B. The contextual factors contributing to occupants’ adaptive comfort behaviors in offices–a review and proposed modeling framework. Building and Environment, 2014, 77: 77–87Google Scholar
  51. 51.
    Xu X, Taylor J E, Pisello A L. Network synergy effect: establishing a synergy between building network and peer network energy conservation effects. Energy and Buildings, 2014, 68(PartA): 312–320Google Scholar
  52. 52.
    Xu X, Maki A, Chen C F, Dong B, Day J K. Investigating willingness to save energy and communication about energy use in the American workplace with the attitude-behavior-context model. Energy Research & Social Science, 2017, 32: 13–22Google Scholar
  53. 53.
    Ren X, Yan D, Wang C. Air-conditioning usage conditional probability model for residential buildings. Building and Environment, 2014, 81: 172–182Google Scholar
  54. 54.
    Ren X, Yan D, Hong T. Data mining of space heating system performance in affordable housing. Building and Environment, 2015, 89: 1–13Google Scholar
  55. 55.
    Rogers E. New product adoption and diffusion. Journal of Consumer Research, 1976, 2(4): 290–301Google Scholar
  56. 56.
    Olson M. The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge: Harvard University Press, 1971Google Scholar
  57. 57.
    Feiock R C, Coutts C. Guest editor’s introduction: governing the sustainable city. Cityscape, 2013, 15(1): 1–7Google Scholar
  58. 58.
    Terman J N, Kassekert A, Feiock R C, Yang K. Walking in the shadow of Pressman and Wildavsky: expanding fiscal federalism and goal congruence theories to single-shot games. Review of Policy Research, 2016, 33(2): 124–139Google Scholar
  59. 59.
    Terman J, Feiock R C. Improving outcomes in fiscal federalism: local political leadership and administrative capacity. Journal of Public Administration: Research and Theory, 2015, 25(4): 1059–1080Google Scholar
  60. 60.
    Terman J, Feiock R. Third-party federalism: using local governments (and their contractors) to implement national policy. Publius, 2015, 45(2): 322–349Google Scholar
  61. 61.
    Krause R M, Yi H, Feiock R C. Applying policy termination theory to the abandonment of climate protection initiatives by U.S. local governments. Policies Studies Journal, 2016, 44(2): 176–195Google Scholar
  62. 62.
    Feiock R C, Tavares A F, Lubell M. Policy instrument choices for growth management and land use regulation. Policy Studies Journal: the Journal of the Policy Studies Organization, 2008, 36 (3): 461–480Google Scholar
  63. 63.
    Gerber E R, Henry A, Lubell M. The political logic of local collaboration in regional planning in California. In: 3rd Annual Political Networks Conference. Duke University, 2010, 1–27Google Scholar
  64. 64.
    Krause R M. Policy innovation, intergovernmental relations, and the adoption of climate protection initiatives by U.S. cities. Journal of Urban Affairs, 2011, 33(1): 45–60Google Scholar
  65. 65.
    Deslatte A, Swann W L. Is the price right? Gauging the marketplace for local sustainable policy tools. Journal of Urban Affairs, 2016, 38(4): 581–596Google Scholar
  66. 66.
    Ding G K C. Sustainable construction–the role of environmental assessment tools. Journal of Environmental Management, 2008, 86 (3): 451–464Google Scholar
  67. 67.
    Ortiz O, Castells F, Sonnemann G. Sustainability in the construction industry: a review of recent developments based on LCA. Construction & Building Materials, 2009, 23(1): 28–39Google Scholar
  68. 68.
    Yi H, Feiock R C. Policy tool interactions and the adoption of state renewable portfolio standards. Review of Policy Research, 2012, 29(2): 193–206Google Scholar
  69. 69.
    Noailly J. Improving the energy efficiency of buildings: the impact of environmental policy on technical innovation. Energy Economics, 2012, 34(3): 795–806Google Scholar
  70. 70.
    Datta S, Gulati S. Utility rebates for ENERGY STAR appliances: are they effective? Journal of Environmental Economics and Management, 2014, 68(3): 480–506Google Scholar
  71. 71.
    Pevnitskaya S, Ryvkin D. Environmental context and termination uncertainty in games with a dynamic public bad. Environment and Development Economics, 2013, 18(01): 27–49Google Scholar
  72. 72.
    Pevnitskaya S, Ryvkin D. The effect of access to clean technology on pollution reduction: an experiment. Florida State University Working Paper, 2017Google Scholar
  73. 73.
    Rebitzer G, Ekvall T, Frischknecht R, Hunkeler D, Norris G, Rydberg T, Schmidt W P, Suh S, Weidema B P, Pennington D W. Life cycle assessment part 1: framework, goal and scope definition, inventory analysis, and applications. Environment International, 2004, 30(5): 701–720Google Scholar
  74. 74.
    ISO. Environmental management-life cycle assessment-principles and framework. 2006, iso:14040:ed-2:v1:enGoogle Scholar
  75. 75.
    Scientific Applications International Corporation. Life cycle assessment: principles and practice. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-06/060, 2006Google Scholar
  76. 76.
    Norris G A. Integrating life cycle cost analysis and LCA. International Journal of Life Cycle Assessment, 2001, 6(2): 118–120Google Scholar
  77. 77.
    Parent J, Cucuzzella C, Revéret J P. Revisiting the role of LCA and SLCA in the transition towards sustainable production and consumption. International Journal of Life Cycle Assessment, 2013, 18(9): 1642–1652Google Scholar
  78. 78.
    Asif M, Davidson A, Muneer T. Life cycle of window materials—a comparative assessment. 2017–11, Scholar
  79. 79.
    Koroneos C, Dompros A. Environmental assessment of brick production in Greece. Building and Environment, 2007, 42(5): 2114–2123Google Scholar
  80. 80.
    Junnila S, Horvath A, Guggemos A A. Life-cycle assessment of office buildings in Europe and the United States. Journal of Infrastructure Systems, 2006, 12(1): 10–17Google Scholar
  81. 81.
    van Ooteghem K, Xu L. The life-cycle assessment of a singlestorey retail building in Canada. Building and Environment, 2012, 49(1): 212–226Google Scholar
  82. 82.
    Hong T, D’Oca S, Turner W J N, Taylor-Lange S C. An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 2015, 92: 764–777Google Scholar
  83. 83.
    Hellweg S, Milà i Canals L. Emerging approaches, challenges and opportunities in life cycle assessment. Science, 2014, 344(6188): 1109–1113Google Scholar
  84. 84.
    de Haes H U, Finnveden G, Goedkoop M, Hauschild M, Hertwich E, Hofstetter P, Jolliet O, Klopffer W, Krewitt W, Lindeijer E, Mueller-Wenk R, Olsen S, Pennington D, Potting J, Steen B. Life-Cycle Impact Assessment: Striving towards Best Practice. Pensacola: SETAC Press, 2002Google Scholar
  85. 85.
    Field F, Kirchain R, Clark J. Life-cycle assessment and temporal distributions of emissions. Journal of Industrial Ecology, 2000, 4 (2): 71–91Google Scholar
  86. 86.
    Reap J, Roman F, Duncan S, Bras B. A survey of unresolved problems in life cycle assessment. International Journal of Life Cycle Assessment, 2008, 13(4): 290–300Google Scholar
  87. 87.
    Milà I, Canals L, Bauer C, Depestele J, Dubreuil A, Freiermuth Knuchel R, Gaillard G, Michelsen O, Müller-Wenk R, Rydgren B. Key elements in a framework for land use impact assessment within LCA. International Journal of Life Cycle Assessment, 2007, 12(1): 5–15Google Scholar
  88. 88.
    Levasseur M, Richard L, Gauvin L, Raymond E. Inventory and analysis of definitions of social participation found in the aging literature: proposed taxonomy of social activities. Social Science & Medicine, 2010, 71(12): 2141–2149Google Scholar
  89. 89.
    Collet P, Hélias A, Lardon L, Steyer J P. Time and life cycle assessment: how to take time into account in the inventory step? Towards Life Cycle Sustainability Management, 2011, 119–130Google Scholar
  90. 90.
    Lindeijer E. Review of land use impact methodologies. Journal of Cleaner Production, 2000, 8(4): 273–281Google Scholar
  91. 91.
    Reap J, Bras B, Newcomb P J, Carmichael C. Improving life cycle assessment by including spatial, dynamic and place-based modeling. In: Proceedings of the ASME Design Engineering Technical Conference. Chicago, USA, 2003, 3: 77–83Google Scholar
  92. 92.
    Struijs J, van Dijk A, Slaper H, van Wijnen H J, Velders G J, Chaplin G, Huijbregts M A. Spatial- and time-explicit human damage modeling of ozone depleting substances in life cycle impact assessment. Environmental Science & Technology, 2010, 44(1): 204–209Google Scholar
  93. 93.
    Pehnt M. Dynamic life cycle assessment (LCA) of renewable energy technologies. Renewable Energy, 2006, 31(1): 55–71Google Scholar
  94. 94.
    Collinge W, Landis A E, Jones A K, Schaefer L A, Bilec M M. Indoor environmental quality in a dynamic life cycle assessment framework for whole buildings: focus on human health chemical impacts. Building and Environment, 2013, 62(1): 182–190Google Scholar
  95. 95.
    Collinge W O, Landis A E, Jones A K, Schaefer L A, Bilec M M. Productivity metrics in dynamic LCA for whole buildings: using a post-occupancy evaluation of energy and indoor environmental quality tradeoffs. Building and Environment, 2014, 82: 339–348Google Scholar
  96. 96.
    Stasinopoulos P, Compston P, Newell B, Jones H M. A system dynamics approach in LCA to account for temporal effects —a consequential energy LCI of car body-in-whites. International Journal of Life Cycle Assessment, 2012, 17(2): 199–207Google Scholar
  97. 97.
    Collinge W O, Landis A E, Jones A K, Schaefer L A, Bilec M M. Dynamic life cycle assessment: framework and application to an institutional building. International Journal of Life Cycle Assessment, 2013, 18(3): 538–552Google Scholar
  98. 98.
    Mutel C L, Hellweg S. Regionalized life cycle assessment: computational methodology and application to inventory databases. Environmental Science & Technology, 2009, 43(15): 5797–5803Google Scholar
  99. 99.
    Lam K P, Zhao J, Ydstie B E, Wirick J, Qi M. An EnergyPlus whole building energy model calibration method for office buildings using occupant behavior data mining and empirical data. In: ASHRAE/IBPSA-USA Building Simulation Conference. Atlanta, USA, 2014, 160–167Google Scholar
  100. 100.
    Oldewurtel F, Parisio A, Jones C N, Gyalistras D, Gwerder M, Stauch V, Lehmann B, Morari M. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Building, 2012, 45(1): 15–27Google Scholar
  101. 101.
    Ramesh S, Lam K P, Baird N, Johnstone H. Urban energy information modelling: An interactive platform to communicate simulation based high fidelity building energy analysis using geographical information systems (GIS). In: 13th Conference of the International Building Performance Simulation Association (BS 2013). Chambery, France, 2013, 1136–1143Google Scholar
  102. 102.
    Zhao J, Yun R, Lasternas B, Want H, Lam K, Aziz A, Loftness V. Occupant behavior and schedule prediction based on office appliance energy consumption data mining. In: CISBAT 2013. Lausanne, Switzerland, 2013, 1: 549–554Google Scholar
  103. 103.
    Kota S, Haberl J S, Clayton M J, Yan W. Building Information Modeling (BIM)-based daylighting simulation and analysis. Energy and Building, 2014, 81: 391–403Google Scholar
  104. 104.
    Lasternas B, Zhao J, Yun R, Zhang C, Wang H, Aziz A, Lam K P, Loftness V. Behavior oriented metrics for plug load energy savings in office environment. ACEEE Summer Study on Energy Efficiency in Buildings, 2014, 7: 160–172Google Scholar
  105. 105.
    Prentow T S, Blunch H, Gr¢nbæk K, Kjærgaard M B. Estimating common pedestrian routes through indoor path networks using position traces. In: 15th IEEE International Conference on Mobile Data Management (IEEE MDM 2014). Brisbane, Australia, 2014, 1: 43–48Google Scholar
  106. 106.
    Spiegelhalter T, Vassigh S. Achieving best practice net-zeroenergy building design instruction methods. In: 30th International PLEA 2014 Conference. Ahmedabad, Gujarat, India, 2014, 1: 25–33Google Scholar
  107. 107.
    Spiegelhalter T. Energy-efficiency retrofitting and transformation of the FIU-college of architecture + the arts into a net-zero-energybuilding by 2018. Energy Procedia, 2014, 57: 1922–1930Google Scholar
  108. 108.
    Sun K, Hong T. A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures. Energy and Building, 2017, 146: 383–396Google Scholar
  109. 109.
    Sun K, Hong T. A simulation approach to estimate energy savings potential of occupant behavior measures. Energy and Building, 2017, 136: 43–62Google Scholar
  110. 110.
    Chen Y, Hong T, Luo X. An agent-based stochastic occupancy simulator. Building Simulation, 2018, 11(1): 37–49Google Scholar
  111. 111.
    Chen Y, Liang X, Hong T, Luo X. Simulation and visualization of energy-related occupant behavior in office buildings. Building Simulation, 2017, 10(6): 785–798Google Scholar
  112. 112.
    Tolone W J, Hadzikadic M, Shannon S. SOPHI observatory, UNC Charlotte. 2015, Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Suraj Talele
    • 1
  • Caleb Traylor
    • 1
  • Laura Arpan
    • 2
  • Cali Curley
    • 3
  • Chien-Fei Chen
    • 4
  • Julia Day
    • 5
  • Richard Feiock
    • 6
  • Mirsad Hadzikadic
    • 7
  • William J. Tolone
    • 7
  • Stan Ingman
    • 8
  • Dale Yeatts
    • 9
  • Omer T. Karaguzel
    • 10
  • Khee Poh Lam
    • 11
  • Carol Menassa
    • 12
  • Svetlana Pevnitskaya
    • 13
  • Thomas Spiegelhalter
    • 14
  • Wei Yan
    • 15
  • Yimin Zhu
    • 16
  • Yong X. Tao
    • 17
  1. 1.Department of Mechanical and Energy EngineeringUniversity of North TexasDentonUSA
  2. 2.College of Communication & InformationFlorida State UniversityTallahasseeUSA
  3. 3.School of Public and Environmental AffairsIndiana University–Purdue University IndianapolisIndianapolisUSA
  4. 4.Department of SociologyUniversity of Tennessee KnoxvilleKnoxvilleUSA
  5. 5.Human Ecology, Department of Construction ManagementWashington State UniversityPullmanUSA
  6. 6.Reubin O’D. Askew School of Public Administration and PolicyFlorida State UniversityTallahasseeUSA
  7. 7.College of Computing and InformaticsUniversity of North Carolina, CharlotteCharlotteUSA
  8. 8.Department of GerontologyUniversity of North TexasDentonUSA
  9. 9.Department of Gerontology and Department of SociologyUniversity of North TexasDentonUSA
  10. 10.CMU School of ArchitectureCarnegie Mellon UniversityPittsburghUSA
  11. 11.School of Design & EnvironmentNational University of SingaporeSingaporeSingapore
  12. 12.Department of Civil and Environmental EngineeringUniversity of MichiganAnn ArborUSA
  13. 13.Department of EconomicsFlorida State UniversityTallahasseeUSA
  14. 14.Department of ArchitectureFlorida International UniversityMiamiUSA
  15. 15.College of ArchitectureTexas A & M UniversityCollege StationUSA
  16. 16.Bert S. Turner Department of Construction ManagementLouisiana State UniversityBaton RogueUSA
  17. 17.College of Engineering and ComputingNova Southeastern UniversityDavieUSA

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