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

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.

Graphical abstract

Keywords

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

References

[1] United Nations Framework Convention on Climate Change (UNFCCC). 2017–11

[2] RCN-SEES-SHBE. Predictive modeling network for Sustainable Human-Building Ecosystems. 2017–11

[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

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

[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

[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

[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

[8] Asl M R, 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

[9] Kim H, Asl M R, 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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

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

[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

[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

[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

[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

[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

[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

[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

[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

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

[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

[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

[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

[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

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

[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

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

[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

[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

[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

[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

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

[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

[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

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

[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

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

[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

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

[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

[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

[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

[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

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

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

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

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

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

[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

[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

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

[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

[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

[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

[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

[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

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

[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

[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

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

[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

[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

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

[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

[74] ISO. Environmental management-life cycle assessment-principles and framework. 2006

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

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

[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

[78] Asif M, Davidson A, Muneer T. Life cycle of window materials—a comparative assessment. 2017–11

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

[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

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

[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

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

[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

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

[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

[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

[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

[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

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

[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

[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

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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[106] Spiegelhalter T, Vassigh S. Achieving best practice net-zero-energy building design instruction methods. In: 30th International PLEA 2014 Conference. Ahmedabad, Gujarat, India, 2014, 1: 25–33

[107] Spiegelhalter T. Energy-efficiency retrofitting and transformation of the FIU-college of architecture+ the arts into a net-zero-energy-building by 2018. Energy Procedia, 2014, 57: 1922–1930

[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

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

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

[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

[112] Tolone W J, Hadzikadic M, Shannon S. SOPHI observatory, UNC Charlotte. 2015

Acknowledgments

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

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