Skip to main content
SpringerLink
Log in

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

  • Review Article
  • Published:
Frontiers in Energy Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. United Nations Framework Convention on Climate Change (UNFCCC). 2017–11, http://unfccc.int/paris_agreement/items/9485.php

  2. RCN-SEES-SHBE. Predictive modeling network for Sustainable Human-Building Ecosystems. 2017–11, http://www.shbe.org/

  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): 301

    Google Scholar 

  4. Wetter M, Zuo W, Nouidui T S, Pang X. Modelica buildings library. Journal of Building Performance Simulation, 2014, 7(4): 253–270

    Google Scholar 

  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): 50–69

    Google Scholar 

  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): 16–28

    Google Scholar 

  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, 223–230

    Google Scholar 

  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: 673–682

    Google Scholar 

  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: 657–664

    Google Scholar 

  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: 95–99

    Google Scholar 

  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: 518–530

    Google Scholar 

  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: 93–104

    Google Scholar 

  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: 61–71

    Google Scholar 

  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: 1–11

    Google Scholar 

  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: 95–108

    Google Scholar 

  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, 2017

    Google Scholar 

  17. Nilsson A, Andersson K, Bergstad C. Energy behaviors at the office: an intervention study on the use of equipment. Applied Energy, 2015, 146: 434–441

    Google Scholar 

  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): 16043

    Google Scholar 

  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): 137–147

    Google Scholar 

  20. Stern P. New environmental theories: toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 2000, 56(3): 407–424

    Google Scholar 

  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): 273–291

    Google Scholar 

  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: 264–278

    Google Scholar 

  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, 2014

    Google Scholar 

  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: 275–287

    Google Scholar 

  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: 205–218

    Google Scholar 

  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: 726–739

    Google Scholar 

  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): 104–117

    Google Scholar 

  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, 130–138

    Google Scholar 

  29. D’Oca S, Hong T. Occupancy schedules learning process through a data mining framework. Energy and Building, 2015, 88: 395–408

    Google Scholar 

  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: 841–853

    Google Scholar 

  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: 493–500

    Google Scholar 

  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): 457–478

    Google Scholar 

  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: 168–178

    Google Scholar 

  34. Wang Q, Taylor J E. Energy saving practice diffusion in online networks. Energy and Building, 2014, 76: 622–630

    Google Scholar 

  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): 683–694

    Google Scholar 

  36. Wei S, Jones R, de Wilde P. Driving factors for occupantcontrolled space heating in residential buildings. Energy and Building, 2014, 70: 36–44

    Google Scholar 

  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): 89–98

    Google Scholar 

  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): 89–106

    Google Scholar 

  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): 739–756

    Google Scholar 

  40. de Wilde P. The gap between predicted and measured energy performance of buildings: a framework for investigation. Automation in Construction, 2014, 41: 40–49

    Google Scholar 

  41. de wilde P, Jones R. The building energy performance gap: up close and personal. In: CIBSE ASHRAE Technical Symposium. Dublin, Ireland, 2014

    Google Scholar 

  42. Li C, Hong T, Yan D. An insight into actual energy use and its drivers in high-performance buildings. Applied Energy, 2014, 131: 394–410

    Google Scholar 

  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: 165–175

    Google Scholar 

  44. Roetzel A. Occupant behavior simulation for cellular offices in early design stages—architectural and modelling considerations. Building Simulation, 2015, 8(2): 211–224

    Google Scholar 

  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: 1–12

    Google Scholar 

  46. Kingma B, van Marken Lichtenbelt W. Energy consumption in buildings and female thermal demand. Nature Climate Change, 2015, 5(12): 1054–1056

    Google Scholar 

  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: 341–355

    Google Scholar 

  48. Feng X, Yan D, Hong T. Simulation of occupancy in buildings. Energy and Building, 2015, 87: 348–359

    Google Scholar 

  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: 114–119

    Google Scholar 

  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: 77–87

    Google Scholar 

  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): 312–320

    Google Scholar 

  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: 13–22

    Google Scholar 

  53. Ren X, Yan D, Wang C. Air-conditioning usage conditional probability model for residential buildings. Building and Environment, 2014, 81: 172–182

    Google Scholar 

  54. Ren X, Yan D, Hong T. Data mining of space heating system performance in affordable housing. Building and Environment, 2015, 89: 1–13

    Google Scholar 

  55. Rogers E. New product adoption and diffusion. Journal of Consumer Research, 1976, 2(4): 290–301

    Google Scholar 

  56. Olson M. The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge: Harvard University Press, 1971

    Google Scholar 

  57. Feiock R C, Coutts C. Guest editor’s introduction: governing the sustainable city. Cityscape, 2013, 15(1): 1–7

    Google Scholar 

  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): 124–139

    Google Scholar 

  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): 1059–1080

    Google Scholar 

  60. Terman J, Feiock R. Third-party federalism: using local governments (and their contractors) to implement national policy. Publius, 2015, 45(2): 322–349

    Google Scholar 

  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): 176–195

    Google Scholar 

  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): 461–480

    Google Scholar 

  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, 1–27

    Google Scholar 

  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): 45–60

    Google Scholar 

  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): 581–596

    Google Scholar 

  66. Ding G K C. Sustainable construction–the role of environmental assessment tools. Journal of Environmental Management, 2008, 86 (3): 451–464

    Google Scholar 

  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): 28–39

    Google Scholar 

  68. Yi H, Feiock R C. Policy tool interactions and the adoption of state renewable portfolio standards. Review of Policy Research, 2012, 29(2): 193–206

    Google Scholar 

  69. Noailly J. Improving the energy efficiency of buildings: the impact of environmental policy on technical innovation. Energy Economics, 2012, 34(3): 795–806

    Google Scholar 

  70. Datta S, Gulati S. Utility rebates for ENERGY STAR appliances: are they effective? Journal of Environmental Economics and Management, 2014, 68(3): 480–506

    Google Scholar 

  71. Pevnitskaya S, Ryvkin D. Environmental context and termination uncertainty in games with a dynamic public bad. Environment and Development Economics, 2013, 18(01): 27–49

    Google Scholar 

  72. Pevnitskaya S, Ryvkin D. The effect of access to clean technology on pollution reduction: an experiment. Florida State University Working Paper, 2017

    Google Scholar 

  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): 701–720

    Google Scholar 

  74. ISO. Environmental management-life cycle assessment-principles and framework. 2006, https://www.iso.org/obp/ui/#iso:std: iso:14040:ed-2:v1:en

  75. Scientific Applications International Corporation. Life cycle assessment: principles and practice. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-06/060, 2006

    Google Scholar 

  76. Norris G A. Integrating life cycle cost analysis and LCA. International Journal of Life Cycle Assessment, 2001, 6(2): 118–120

    Google Scholar 

  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): 1642–1652

    Google Scholar 

  78. Asif M, Davidson A, Muneer T. Life cycle of window materials—a comparative assessment. 2017–11, https://www.envirology.co.nz/life-cycle-of-window-materials-a-comparative-assessment/

  79. Koroneos C, Dompros A. Environmental assessment of brick production in Greece. Building and Environment, 2007, 42(5): 2114–2123

    Google Scholar 

  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): 10–17

    Google Scholar 

  81. van Ooteghem K, Xu L. The life-cycle assessment of a singlestorey retail building in Canada. Building and Environment, 2012, 49(1): 212–226

    Google Scholar 

  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: 764–777

    Google Scholar 

  83. Hellweg S, Milà i Canals L. Emerging approaches, challenges and opportunities in life cycle assessment. Science, 2014, 344(6188): 1109–1113

    Google Scholar 

  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, 2002

    Google Scholar 

  85. Field F, Kirchain R, Clark J. Life-cycle assessment and temporal distributions of emissions. Journal of Industrial Ecology, 2000, 4 (2): 71–91

    Google Scholar 

  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): 290–300

    Google Scholar 

  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): 5–15

    Google Scholar 

  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): 2141–2149

    Google Scholar 

  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, 119–130

    Google Scholar 

  90. Lindeijer E. Review of land use impact methodologies. Journal of Cleaner Production, 2000, 8(4): 273–281

    Google Scholar 

  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: 77–83

    Google Scholar 

  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): 204–209

    Google Scholar 

  93. Pehnt M. Dynamic life cycle assessment (LCA) of renewable energy technologies. Renewable Energy, 2006, 31(1): 55–71

    Google Scholar 

  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): 182–190

    Google Scholar 

  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: 339–348

    Google Scholar 

  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): 199–207

    Google Scholar 

  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): 538–552

    Google Scholar 

  98. Mutel C L, Hellweg S. Regionalized life cycle assessment: computational methodology and application to inventory databases. Environmental Science & Technology, 2009, 43(15): 5797–5803

    Google Scholar 

  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, 160–167

    Google Scholar 

  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): 15–27

    Google Scholar 

  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, 1136–1143

    Google Scholar 

  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: 549–554

    Google Scholar 

  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: 391–403

    Google Scholar 

  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: 160–172

    Google Scholar 

  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: 43–48

    Google Scholar 

  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: 25–33

    Google Scholar 

  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: 1922–1930

    Google Scholar 

  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: 383–396

    Google Scholar 

  109. Sun K, Hong T. A simulation approach to estimate energy savings potential of occupant behavior measures. Energy and Building, 2017, 136: 43–62

    Google Scholar 

  110. Chen Y, Hong T, Luo X. An agent-based stochastic occupancy simulator. Building Simulation, 2018, 11(1): 37–49

    Google Scholar 

  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): 785–798

    Google Scholar 

  112. Tolone W J, Hadzikadic M, Shannon S. SOPHI observatory, UNC Charlotte. 2015, http://sophi.uncc.edu/

    Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Department of Mechanical and Energy Engineering, University of North Texas, Denton, Texas, 76203, USA

    Suraj Talele & Caleb Traylor

  2. College of Communication & Information, Florida State University, Tallahassee, FL, 32306, USA

    Laura Arpan

  3. School of Public and Environmental Affairs, Indiana University–Purdue University Indianapolis, Indianapolis, IN, 46202, USA

    Cali Curley

  4. Department of Sociology, University of Tennessee Knoxville, Knoxville, TN, 37996, USA

    Chien-Fei Chen

  5. Human Ecology, Department of Construction Management, Washington State University, Pullman, WA, 99164, USA

    Julia Day

  6. Reubin O’D. Askew School of Public Administration and Policy, Florida State University, Tallahassee, FL, 32306, USA

    Richard Feiock

  7. College of Computing and Informatics, University of North Carolina, Charlotte, Charlotte, NC, 28223, USA

    Mirsad Hadzikadic & William J. Tolone

  8. Department of Gerontology, University of North Texas, Denton, TX, 76203, USA

    Stan Ingman

  9. Department of Gerontology and Department of Sociology, University of North Texas, Denton, TX, 76203, USA

    Dale Yeatts

  10. CMU School of Architecture, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Omer T. Karaguzel

  11. School of Design & Environment, National University of Singapore, Singapore, 117566, Singapore

    Khee Poh Lam

  12. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

    Carol Menassa

  13. Department of Economics, Florida State University, Tallahassee, FL, 32306, USA

    Svetlana Pevnitskaya

  14. Department of Architecture, Florida International University, Miami, FL, 33199, USA

    Thomas Spiegelhalter

  15. College of Architecture, Texas A & M University, College Station, TX, 77843, USA

    Wei Yan

  16. Bert S. Turner Department of Construction Management, Louisiana State University, Baton Rogue, LA, 70803, USA

    Yimin Zhu

  17. College of Engineering and Computing, Nova Southeastern University, Davie, FL, 33314, USA

    Yong X. Tao

Authors
  1. Suraj Talele
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Caleb Traylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Arpan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cali Curley
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chien-Fei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Julia Day
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Richard Feiock
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mirsad Hadzikadic
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. William J. Tolone
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Stan Ingman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Dale Yeatts
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Omer T. Karaguzel
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Khee Poh Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Carol Menassa
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Svetlana Pevnitskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Thomas Spiegelhalter
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Wei Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yimin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Yong X. Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong X. Tao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Talele, S., Traylor, C., Arpan, L. et al. Energy modeling and data structure framework for Sustainable Human-Building Ecosystems (SHBE) — a review. Front. Energy 12, 314–332 (2018). https://doi.org/10.1007/s11708-017-0530-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11708-017-0530-2

Keywords

Search

Navigation