Energy modeling and data structure framework for Sustainable Human-Building Ecosystems (SHBE) — a review
Suraj TALELE1, Caleb TRAYLOR1, Laura ARPAN2, Cali CURLEY3, Chien-Fei CHEN4, Julia DAY5, Richard FEIOCK6, Mirsad HADZIKADIC7, William J. TOLONE7, Stan INGMAN8, Dale YEATTS9, Omer T. KARAGUZEL10, Khee Poh LAM11, Carol MENASSA12, Svetlana PEVNITSKAYA13, Thomas SPIEGELHALTER14, Wei YAN15, Yimin ZHU16, Yong X. TAO17()
1. Department of Mechanical and Energy Engineering, University of North Texas, Denton, Texas 76203, USA 2. College of Communication & Information, Florida State University, Tallahassee, FL 32306, USA 3. School of Public and Environmental Affairs, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA 4. Department of Sociology, University of Tennessee Knoxville, Knoxville, TN 37996, USA 5. Human Ecology, Kansas State University Department of Construction Management, Washington State University, Pullman, WA 99164, USA 6. Reubin O’D. Askew School of Public Administration and Policy, Florida State University, Tallahassee, FL 32306, USA 7. College of Computing and Informatics, University of North Carolina, Charlotte, Charlotte, NC 28223, USA 8. Department of Gerontology, University of North Texas, Denton, TX 76203, USA 9. Department of Gerontology and Department of Sociology, University of North Texas, Denton, TX 76203, USA 10. CMU School of Architecture, Carnegie Mellon University, Pittsburgh, PA 15213 11. School of Design & Environment, National University of Singapore, Singapore 117566, Singapore 12. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA 13. Department of Economics, Florida State University, Tallahassee, FL 32306, USA 14. Department of Architecture, Florida International University, Miami, FL 33199, USA 15. College of Architecture, Texas A & M University, College Station, TX 77843, USA 16. Bert S. Turner Department of Construction Management, Louisiana State University, Baton Rogue, LA 70803, USA 17. College of Engineering and Computing, Nova Southeastern University, Davie, FL 33314, USA
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.
. [J]. Frontiers in Energy, 2018, 12(2): 314-332.
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. Energy modeling and data structure framework for Sustainable Human-Building Ecosystems (SHBE) — a review. Front. Energy, 2018, 12(2): 314-332.
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
C Batista, R M Ribeiro, V Teixeira. Synthesis and characterization of VO2-based thermochromic thin films for energy-efficient windows. Nanoscale Research Letters, 2011, 6(1): 301 https://doi.org/10.1186/1556-276X-6-301
pmid: 21711813
W S Jeong, J B Kim, M J Clayton, J S Haberl, W Yan. 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 https://doi.org/10.1080/19401493.2014.993709
6
J B Kim, W Jeong, M J Clayton, J S Haberl, W Yan. Developing a physical BIM library for building thermal energy simulation. Automation in Construction, 2015, 50(C): 16–28 https://doi.org/10.1016/j.autcon.2014.10.011
7
W Yan, M R Asl, Z Su, J Altabtabai. 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
M R Asl, A Stoupine, S Zarrinmehr, W Yan. 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
H Kim, M R Asl, W Yan. 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
B K Sovacool, S E Ryan, P C Stern, K Janda, G Rochlin, D Spreng, M J Pasqualetti, H Wilhite, L Lutzenhiser. Integrating social science in energy research. Energy Research & Social Science, 2015, 6: 95–99 https://doi.org/10.1016/j.erss.2014.12.005
11
T Hong, D Yan, S D’Oca, C F Chen. Ten questions concerning occupant behavior in buildings: the big picture. Building and Environment, 2017, 114: 518–530 https://doi.org/10.1016/j.buildenv.2016.12.006
12
C F Chen, X Xu, L Arpan. 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 https://doi.org/10.1016/j.erss.2016.12.011
13
C F Chen, X Xu, J Day. 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 https://doi.org/10.1016/j.erss.2017.01.009
14
C F Chen, X Xu, S Frey. 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 https://doi.org/10.1016/j.erss.2016.02.006
15
X Xu, L Arpan, C F Chen. 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 https://doi.org/10.1016/j.jenvp.2015.09.004
16
X Xu, A Maki, C F Chen, B Dong, J Day. Predicting workplace energy-saving intentions and communication: an application of the attitude-behavior-condition model. Energy Research and Social Science, 2017
17
A Nilsson, K Andersson, C Bergstad. Energy behaviors at the office: an intervention study on the use of equipment. Applied Energy, 2015, 146: 434–441 https://doi.org/10.1016/j.apenergy.2015.02.045
18
P C Stern, K B Janda, M A Brown, L Steg, E L Vine, L Lutzenhiser. Opportunities and insights for reducing fossil fuel consumption by households and organizations. Nature Energy, 2016, 1(5): 16043 https://doi.org/10.1038/nenergy.2016.43
19
S Karatasou, M Laskari, M Santamouris. 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 https://doi.org/10.1080/17512549.2013.809275
20
P Stern. New environmental theories: toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 2000, 56(3): 407–424 https://doi.org/10.1111/0022-4537.00175
21
W Abrahamse, L Steg, C Vlek, T Rothengatter. A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 2005, 25(3): 273–291 https://doi.org/10.1016/j.jenvp.2005.08.002
22
D Yan, W O’Brien, T Hong, X Feng, H Burak Gunay, F Tahmasebi, A Mahdavi. Occupant behavior modelling for building performance simulation: current state and future challenges. Energy and Building, 2015, 107: 264–278 https://doi.org/10.1016/j.enbuild.2015.08.032
23
W Turner, T Hong. 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
X Zhou, D Yan, T Hong, X Ren. Data analysis and stochastic modeling of lighting energy use in large office buildings in China. Energy and Building, 2015, 86: 275–287 https://doi.org/10.1016/j.enbuild.2014.09.071
25
E Azar, C C Menassa. Evaluating the impact of extreme energy use behavior on occupancy interventions in commercial buildings. Energy and Building, 2015, 97: 205–218 https://doi.org/10.1016/j.enbuild.2015.03.059
26
S D’Oca, T Hong. A data-mining approach to discover patterns of window opening and closing behavior in offices. Building and Environment, 2014, 82: 726–739 https://doi.org/10.1016/j.buildenv.2014.10.021
27
M Kjaergaard, H. BlunckTool 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
A J Ruiz-Ruiz, H Blunck, T S Prentow, A Stisen, M B Kjaergaard. 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
E Azar, C C Menassa. A comprehensive analysis of the impact of occupant parameters in energy simulation of office buildings. Energy and Building, 2012, 55: 841–853 https://doi.org/10.1016/j.enbuild.2012.10.002
31
R Gulbinas, J E Taylor. Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings. Energy and Building, 2014, 84: 493–500 https://doi.org/10.1016/j.enbuild.2014.08.017
32
H B Gunay, W O’Brien, I Beausoleil-Morrison, R Goldstein, S Breslav, A Khan. 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 https://doi.org/10.1080/19401493.2013.866695
33
R K Jain, K M Smith, P J Culligan, J E 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, 2014, 123: 168–178 https://doi.org/10.1016/j.apenergy.2014.02.057
S D’Oca, V Fabi, S P Corgnati, R K Andersen. Effect of thermostat and window opening occupant behavior models on energy use in homes. Building Simulation, 2014, 7(6): 683–694 https://doi.org/10.1007/s12273-014-0191-6
36
S Wei, R Jones, P de Wilde. Driving factors for occupant-controlled space heating in residential buildings. Energy and Building, 2014, 70: 36–44 https://doi.org/10.1016/j.enbuild.2013.11.001
37
R Gulbinas, R K Jain, J E Taylor, G Peschiera, M Golparvar-Fard. 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 https://doi.org/10.1061/(ASCE)CP.1943-5487.0000319
38
B Dong, 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, 2014, 7(1): 89–106 https://doi.org/10.1007/s12273-013-0142-7
39
H B Gunay, W O’Brien, I Beausoleil-Morrison, B Huchuk. On adaptive occupant-learning window blind and lighting controls. Building Research and Information, 2014, 42(6): 739–756 https://doi.org/10.1080/09613218.2014.895248
40
P de Wilde. The gap between predicted and measured energy performance of buildings: a framework for investigation. Automation in Construction, 2014, 41: 40–49 https://doi.org/10.1016/j.autcon.2014.02.009
41
Wilde P de, R Jones. The building energy performance gap: up close and personal. In: CIBSE ASHRAE Technical Symposium. Dublin, Ireland, 2014
A Roetzel, A Tsangrassoulis, U Dietrich. Impact of building design and occupancy on office comfort and energy performance in different climates. Building and Environment, 2014, 71: 165–175 https://doi.org/10.1016/j.buildenv.2013.10.001
44
A Roetzel. Occupant behavior simulation for cellular offices in early design stages—architectural and modelling considerations. Building Simulation, 2015, 8(2): 211–224 https://doi.org/10.1007/s12273-014-0203-6
45
K Sun, D Yan, T Hong, S Guo. Stochastic modelling of overtime occupancy and its application in building energy simulation and calibration. Building and Environment, 2014, 79: 1–12 https://doi.org/10.1016/j.buildenv.2014.04.030
46
B Kingma, W van Marken Lichtenbelt. Energy consumption in buildings and female thermal demand. Nature Climate Change, 2015, 5(12): 1054–1056 https://doi.org/10.1038/nclimate2741
47
J Zhao, B Lasternas, K P Lam, R Yun, V Loftness. Occupant behavior and schedule modelling for building energy simulation through office appliance power consumption data mining. Energy and Building, 2014, 82: 341–355 https://doi.org/10.1016/j.enbuild.2014.07.033
S H Jeong, R Gulbinas, R K Jain, J E Taylor. The impact of combined water and energy consumption eco-feedback on conservation. Energy and Building, 2014, 80: 114–119 https://doi.org/10.1016/j.enbuild.2014.05.013
50
W O’Brien, H B Gunay. The contextual factors contributing to occupants’ adaptive comfort behaviors in offices–a review and proposed modeling framework. Building and Environment, 2014, 77: 77–87 https://doi.org/10.1016/j.buildenv.2014.03.024
51
X Xu, J E Taylor, A L Pisello. Network synergy effect: establishing a synergy between building network and peer network energy conservation effects. Energy and Buildings, 2014, 68(PartA): 312–320 https://doi.org/10.1016/j.enbuild.2013.09.017
52
X Xu, A Maki, C F Chen, B Dong, J K Day. 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 https://doi.org/10.1016/j.erss.2017.02.011
53
X Ren, D Yan, C Wang. Air-conditioning usage conditional probability model for residential buildings. Building and Environment, 2014, 81: 172–182 https://doi.org/10.1016/j.buildenv.2014.06.022
E Rogers. New product adoption and diffusion. Journal of Consumer Research, 1976, 2(4): 290–301 https://doi.org/10.1086/208642
56
M Olson. The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge: Harvard University Press, 1971
57
R C Feiock, C Coutts. Guest editor’s introduction: governing the sustainable city. Cityscape, 2013, 15(1): 1–7
58
J N Terman, A Kassekert, R C Feiock, K Yang. 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 https://doi.org/10.1111/ropr.12166
59
J Terman, R C Feiock. Improving outcomes in fiscal federalism: local political leadership and administrative capacity. Journal of Public Administration: Research and Theory, 2015, 25(4): 1059–1080 https://doi.org/10.1093/jopart/muu027
60
J Terman, R Feiock. Third-party federalism: using local governments (and their contractors) to implement national policy. Publius, 2015, 45(2): 322–349 https://doi.org/10.1093/publius/pju041
61
R M Krause, H Yi, R C Feiock. Applying policy termination theory to the abandonment of climate protection initiatives by U.S. local governments. Policies Studies Journal, 2016, 44(2): 176–195 https://doi.org/10.1111/psj.12117
62
R C Feiock, A F Tavares, M Lubell. 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 https://doi.org/10.1111/j.1541-0072.2008.00277.x
63
E R Gerber, A Henry, M Lubell. The political logic of local collaboration in regional planning in California. In: 3rd Annual Political Networks Conference. Duke University, 2010, 1–27
64
R M Krause. Policy innovation, intergovernmental relations, and the adoption of climate protection initiatives by U.S. cities. Journal of Urban Affairs, 2011, 33(1): 45–60 https://doi.org/10.1111/j.1467-9906.2010.00510.x
65
A Deslatte, W L Swann. Is the price right? Gauging the marketplace for local sustainable policy tools. Journal of Urban Affairs, 2016, 38(4): 581–596 https://doi.org/10.1111/juaf.12245
O Ortiz, F Castells, G Sonnemann. Sustainability in the construction industry: a review of recent developments based on LCA. Construction & Building Materials, 2009, 23(1): 28–39 https://doi.org/10.1016/j.conbuildmat.2007.11.012
68
H Yi, R C Feiock. Policy tool interactions and the adoption of state renewable portfolio standards. Review of Policy Research, 2012, 29(2): 193–206 https://doi.org/10.1111/j.1541-1338.2012.00548.x
69
J Noailly. Improving the energy efficiency of buildings: the impact of environmental policy on technical innovation. Energy Economics, 2012, 34(3): 795–806 https://doi.org/10.1016/j.eneco.2011.07.015
70
S Datta, S Gulati. Utility rebates for ENERGY STAR appliances: are they effective? Journal of Environmental Economics and Management, 2014, 68(3): 480–506 https://doi.org/10.1016/j.jeem.2014.09.003
71
S Pevnitskaya, D Ryvkin. Environmental context and termination uncertainty in games with a dynamic public bad. Environment and Development Economics, 2013, 18(01): 27–49 https://doi.org/10.1017/S1355770X12000423
72
S Pevnitskaya, D Ryvkin. The effect of access to clean technology on pollution reduction: an experiment. Florida State University Working Paper, 2017
73
G Rebitzer, T Ekvall, R Frischknecht, D Hunkeler, G Norris, T Rydberg, W P Schmidt, S Suh, B P Weidema, D W Pennington. Life cycle assessment part 1: framework, goal and scope definition, inventory analysis, and applications. Environment International, 2004, 30(5): 701–720 https://doi.org/10.1016/j.envint.2003.11.005
pmid: 15051246
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
G A Norris. Integrating life cycle cost analysis and LCA. International Journal of Life Cycle Assessment, 2001, 6(2): 118–120
77
J Parent, C Cucuzzella, J P Revéret. 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 https://doi.org/10.1007/s11367-012-0485-9
78
M Asif, A Davidson, T Muneer. Life cycle of window materials—a comparative assessment. 2017–11
S Junnila, A Horvath, A A Guggemos. Life-cycle assessment of office buildings in Europe and the United States. Journal of Infrastructure Systems, 2006, 12(1): 10–17 https://doi.org/10.1061/(ASCE)1076-0342(2006)12:1(10)
81
K van Ooteghem, L Xu. The life-cycle assessment of a single-storey retail building in Canada. Building and Environment, 2012, 49(1): 212–226 https://doi.org/10.1016/j.buildenv.2011.09.028
82
T Hong, S D’Oca, W J N Turner, S C Taylor-Lange. An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 2015, 92: 764–777 https://doi.org/10.1016/j.buildenv.2015.02.019
83
S Hellweg, L Milà i Canals. Emerging approaches, challenges and opportunities in life cycle assessment. Science, 2014, 344(6188): 1109–1113 https://doi.org/10.1126/science.1248361
pmid: 24904154
84
H U de Haes, G Finnveden, M Goedkoop, M Hauschild, E Hertwich, P Hofstetter, O Jolliet, W Klopffer, W Krewitt, E Lindeijer, R Mueller-Wenk, S Olsen, D Pennington, J Potting, B Steen.Life-Cycle Impact Assessment: Striving towards Best Practice. Pensacola : SETAC Press, 2002
85
F Field, R Kirchain, J Clark. Life-cycle assessment and temporal distributions of emissions. Journal of Industrial Ecology, 2000, 4(2): 71–91 https://doi.org/10.1162/108819800569816
86
J Reap, F Roman, S Duncan, B Bras. A survey of unresolved problems in life cycle assessment. International Journal of Life Cycle Assessment, 2008, 13(4): 290–300 https://doi.org/10.1007/s11367-008-0008-x
87
I Milà, L Canals, C Bauer, J Depestele, A Dubreuil, R Freiermuth Knuchel, G Gaillard, O Michelsen, R Müller-Wenk, B Rydgren. Key elements in a framework for land use impact assessment within LCA. International Journal of Life Cycle Assessment, 2007, 12(1): 5–15 https://doi.org/10.1065/lca2006.05.250
88
M Levasseur, L Richard, L Gauvin, E Raymond. 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 https://doi.org/10.1016/j.socscimed.2010.09.041
pmid: 21044812
89
P Collet, A Hélias, L Lardon, J P Steyer. Time and life cycle assessment: how to take time into account in the inventory step? Towards Life Cycle Sustainability Management, 2011, 119–130 https://doi.org/10.1007/978-94-007-1899-9_12
J Reap, B Bras, P J Newcomb, C Carmichael. 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
J Struijs, A van Dijk, H Slaper, H J van Wijnen, G J Velders, G Chaplin, M A Huijbregts. Spatial- and time-explicit human damage modeling of ozone depleting substances in life cycle impact assessment. Environmental Science & Technology, 2010, 44(1): 204–209 https://doi.org/10.1021/es9017865
pmid: 19958022
W Collinge, A E Landis, A K Jones, L A Schaefer, M M Bilec. 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 https://doi.org/10.1016/j.buildenv.2013.01.015
95
W O Collinge, A E Landis, A K Jones, L A Schaefer, M M Bilec. 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 https://doi.org/10.1016/j.buildenv.2014.08.032
96
P Stasinopoulos, P Compston, B Newell, H M Jones. 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 https://doi.org/10.1007/s11367-011-0344-0
97
W O Collinge, A E Landis, A K Jones, L A Schaefer, M M Bilec. Dynamic life cycle assessment: framework and application to an institutional building. International Journal of Life Cycle Assessment, 2013, 18(3): 538–552 https://doi.org/10.1007/s11367-012-0528-2
98
C L Mutel, S Hellweg. Regionalized life cycle assessment: computational methodology and application to inventory databases. Environmental Science & Technology, 2009, 43(15): 5797–5803 https://doi.org/10.1021/es803002j
pmid: 19731679
99
K P Lam, J Zhao, B E Ydstie, J Wirick, M Qi. 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
F Oldewurtel, A Parisio, C N Jones, D Gyalistras, M Gwerder, V Stauch, B Lehmann, M Morari. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Building, 2012, 45(1): 15–27 https://doi.org/10.1016/j.enbuild.2011.09.022
101
S Ramesh, K P Lam, N Baird, H Johnstone. 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
J Zhao, R Yun, B Lasternas, H Want, K Lam, A Aziz, V Loftness. Occupant behavior and schedule prediction based on office appliance energy consumption data mining. In: CISBAT 2013. Lausanne, Switzerland, 2013, 1: 549–554
103
S Kota, J S Haberl, M J Clayton, W Yan. Building Information Modeling (BIM)-based daylighting simulation and analysis. Energy and Building, 2014, 81: 391–403 https://doi.org/10.1016/j.enbuild.2014.06.043
104
B Lasternas, J Zhao, R Yun, C Zhang, H Wang, A Aziz, K P Lam, V Loftness. Behavior oriented metrics for plug load energy savings in office environment. ACEEE Summer Study on Energy Efficiency in Buildings, 2014, 7: 160–172
105
T S Prentow, H Blunch, K Gr¢nbæk, M B Kjærgaard. 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
T Spiegelhalter, S Vassigh. Achieving best practice net-zero-energy building design instruction methods. In: 30th International PLEA 2014 Conference. Ahmedabad, Gujarat, India, 2014, 1: 25–33
107
T Spiegelhalter. 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 https://doi.org/10.1016/j.egypro.2014.10.056
108
K Sun, T Hong. A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures. Energy and Building, 2017, 146: 383–396 https://doi.org/10.1016/j.enbuild.2017.04.065
109
K Sun, T Hong. A simulation approach to estimate energy savings potential of occupant behavior measures. Energy and Building, 2017, 136: 43–62 https://doi.org/10.1016/j.enbuild.2016.12.010
110
Y Chen, T Hong, X Luo. An agent-based stochastic occupancy simulator. Building Simulation, 2018, 11(11): 37–49
111
Y Chen, X Liang, T Hong, X Luo. Simulation and visualization of energy-related occupant behavior in office buildings. Building Simulation, 2017, 10(6): 785–798 https://doi.org/10.1007/s12273-017-0355-2
112
W J Tolone, M Hadzikadic, S Shannon. SOPHI observatory, UNC Charlotte. 2015