Evaluating energy performance in non-domestic buildings: A review

Energy and Buildings

Volumn 128, Issue , 2016, Pages 734-755

  Borgstein, E H ^a,c   Lamberts, R ^b   Hensen, J L M ^c  

^a Mitsidi Projetos   (Brazil)

^b FEDERAL UNIVERSITY OF SANTA CATARINA   (Brazil)

^c EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Benchmarking; Energy efficiency; Energy performance

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; BUILDINGS; LEARNING SYSTEMS;

ENERGY PERFORMANCE; ENERGY USE; ENGINEERING CALCULATION; EVALUATION METHODS; MODELLING TECHNIQUES; NON-DOMESTIC BUILDINGS; SERVICE PROVISIONS;

ENERGY EFFICIENCY;

EID: 84979609132     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2016.07.018     Document Type: Article

References (217)

1. [1] International Energy Agency, Total Energy Use in Buildings Analysis and Evaluation Methods. 2013, International Energy Agency.
2. [2] International Energy Agency, Capturing the Multiple Benefits of Energy Efficiency. 2014, International Energy Agency.
3. [3] Cullen, J.M., Allwood, J.M., Borgstein, E.H., Reducing energy demand—what are the practical limits. Environ. Sci. Technol. 45 (2011), 1711–1718.
4. [4] Pérez-Lombard, L., Ortiz, J., Pout, C., A review on buildings energy consumption information. Energy Build. 40:3 (2008), 394–398.
5. [5] Davies, H., Tracing the continuing development of Part L Modern Building Services, 2013. [Online]. 2016 Available: http://www.modbs.co.uk/news/fullstory.php/aid/12062/Tracing_the_continuing_development_of_Part_L.html (accessed 20.03.16).
6. [6] IPEEC Building Energy Efficiency Tasktroup, Building Energy Rating Schemes: Assessing Issues and Impacts. 2014, IPEEC, Paris.
7. [7] Ruparathna, R., Hewage, K., Sadiq, R., Improving the energy efficiency of the existing building stock: a critical review of commercial and institutional buildings. Renew. Sustain. Energy Rev. 53 (2016), 1032–1045.
8. [8] Thomas, R., Environmental design: an introduction for architects and engineers. 3rd ed., 2006, Taylor & Francis.
9. [9] Mumovic, D., Santamouris, M., (eds.) A Handbook of Sustainable Building Design and Engineering, 2009, Earthscan.
10. [10] Olofsson, T., Meier, A., Lamberts, R., Rating the energy performance of buildings. Int. J. Low Energy Sustain. Build., Vol. 3, 2004.
11. [11] R. Cohen, EPLabel: A programme to deliver energy certificates for display in Public buildings across Europe within a harmonising framework, EPLabel Final Publishable Report, Intelligent Energy Europe (IEE), 2007.
12. [12] Cohen, R., Bordass, W., Field, J., Fixed and customised benchmarks for building energy performance certificates based on operational ratings. in EPIC 2006 AIVC Conference, Lyon, France, 2006.
13. [13] ISO, Energy performance of buildings – Assessment of overall energy performance – ISO 16346:2013, vol. Energy per, 2013.
14. ® Performance Ratings Technical Methodology. 2010, US Environmental Protection Agency.
15. [15] Nikolaou, T., Kolokotsa, D., Stavrakakis, G., Review on methodologies for energy benchmarking, rating and classification of buildings. Adv. Build. Energy Res. 5:1 (2011), 53–70.
16. [16] Chung, W., Review of building energy-use performance benchmarking methodologies. Appl. Energy 88:5 (2011), 1470–1479.
17. [17] Pérez-Lombard, L., Ortiz, J., González, R., Maestre, I.R., A review of benchmarking, rating and labelling concepts within the framework of building energy certification schemes. Energy Build. 41:3 (2009), 272–278.
18. [18] U.S. Department of Energy's Commercial Building Initiative, Buildings Performance Metrics Terminology, 2009, [Online]. 2016 Available: http://energy.gov/sites/prod/files/2013/12/f5/metrics_terminology_20090203.pdf (accessed 08.03.16).
19. [19] Bannister, P., Hinge, A., Empirical benchmarking of building performance. Proceedings of theACEEE Summer Study Energy Effic. Build., 2006.
20. [20] Wang, S., Yan, C., Xiao, F., Quantitative energy performance assessment methods for existing buildings. Energy Build. 55 (2012), 873–888.
21. [21] Hong, S.-M., Paterson, G., Burman, E., Steadman, P., Mumovic, D., A comparative study of benchmarking approaches for non-domestic buildings: part 1 – top-down approach. Int. J. Sustain. Built Environ. 2:2 (2013), 119–130.
22. [22] Burman, E., Hong, S.-M., Paterson, G., Kimpian, J., Mumovic, D., A comparative study of benchmarking approaches for non-domestic buildings: part 2 – bottom-up approach. Int. J. Sustain. Built Environ. 2:2 (2013), 119–130.
23. [23] Li, Z., Han, Y., Xu, P., Methods for benchmarking building energy consumption against its past or intended performance: an overview. Appl. Energy 124 (2014), 325–334.
24. [24] ASHRAE, Energy estimating and modeling methods. ASHRAE Handbook – Fundamentals, 2013, ASHRAE.
25. [25] Kinney, S., Piette, M.A., Development of a california commercial building benchmarking database. Proceedings of the ACEEE Summer Study Energy Effic. Build., 2002.
26. [26] Li, X., Wen, J., Review of building energy modeling for control and operation. Renew. Sustain. Energy Rev. 37 (2014), 517–537.
27. [27] Kramer, R., van Schijndel, J., Schellen, H., Simplified thermal and hygric building models: a literature review. Front. Archit. Res. 1:4 (2012), 318–325.
28. [28] Coakley, D., Raftery, P., Keane, M., A review of methods to match building energy simulation models to measured data. Renew. Sustain. Energy Rev. 37 (2014), 123–141.
29. [29] Zhang, Y., O'Neill, Z., Dong, B., Augenbroe, G., Comparisons of inverse modeling approaches for predicting building energy performance. Build. Environ. 86 (2015), 177–190.
30. [30] Al-Homoud, M.S., Computer-aided building energy analysis techniques. Build. Environ. 36:4 (2001), 421–433.
31. [31] Zhao, H., Magoulès, F., A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16:6 (2012), 3586–3592.
32. [32] Raslan, R., Davies, M., Results variability in accredited building energy performance compliance demonstration software in the UK: an inter-model comparative study. J. Build. Perform. Simul. 3:1 (2010), 63–85.
33. [33] ISO, Thermal performance of buildings – Calculation of energy use for space heating and cooling – ISO 13790:2005, 2005.
34. [34] Kim, Y.-J., Yoon, S.-H., Park, C.-S., Stochastic comparison between simplified energy calculation and dynamic simulation. Energy Build. 64 (2013), 332–342.
35. [35] M. Gustafsson, F. Ochs, S. Birchall, G. Dermentzis, C. Bales, C. Dipasquale, T. Calabrese, iNSPiRe: D2.2 Report on Auditing tool for assessment of building needs,Development of Systemic Packages for Deep Energy Renovation of Residential and Tertiary Buildings including Envelope and Systems, research project under EU FP7, Project ReportD2.2 2015.
36. [36] BRE, UK National Calculation Methodology for Non-Domestic Buildings. [Online] Available: http://www.uk-ncm.org.uk/ (accessed 20.03.16).
37. [37] Borgstein, E.H., Lamberts, R., Developing energy consumption benchmarks for buildings: bank branches in Brazil. Energy Build. 82 (2014), 82–91.
38. [38] CIBSE, CIBSE Guide A: Environmental Design. 2015, CIBSE.
39. [39] iSERV, http://www.iservcmb.info/, 2016.
40. [40] Knight, I., Assessing electrical energy use in HVAC systems. REHVA J., 2012.
41. [41] Field, J.W., Soper, J., Jones, P.G., Bordass, W.T., Energy performance of occupied non domestic buildings: assessment by analysing end-use consumptions. Build. Serv. Eng. Res. Technol. 18:1 (1997), 39–46.
42. [42] CIBSE, TM22: Energy Assessment and Reporting Method., 2006, CIBSE.
43. [43] Menezes, A.C., Nkonge, N., Cripps, A., Bouchlaghem, D., Buswell, R., Review of benchmarks for small power consumption in office buildings. CIBSE ASHRAE Tech. Symp., 2012, 1–19.
44. [44] CIBSE, CIBSE Guide D: Transportation Systems in Buildings. 2010, CIBSE.
45. [45] ASHRAE, Procedures for Com;1;mercial Building Energy Energy Audits. 2011, ASHRAE.
46. [46] Cohen, R., Standeven, M., Bordass, B., Leaman, A., Assessing building performance in use 1: the Probe process. Build. Res. Inf. 29:2 (2001), 85–102.
47. [47] CIBSE, TM54: Evaluating Operational Energy Performance of Buildings at the Design Stage. 2013, CIBSE.
48. [48] Bordass, B., Energy Consumption Guide 19: Energy Use in Offices. 2000, Energy Efficiency Best Practices Programme, London.
49. [49] CIBSE, TM46: Energy Benchmarks. 2008, CIBSE.
50. [50] Escrivá-Escrivá, G., Álvarez-Bel, C., Peñalvo-López, E., New indices to assess building energy efficiency at the use stage. Energy Build. 43:2–3 (2011), 476–484.
51. [51] Federspiel, C., Zhang, Q., Arens, E., Model-based benchmarking with application to laboratory buildings. Energy Build. 34:3 (2002), 203–214.
52. [52] Fumo, N., Mago, P., Luck, R., Methodology to estimate building energy consumption using EnergyPlus Benchmark Models. Energy Build. 42:12 (2010), 2331–2337.
53. [53] Pérez-Lombard, L., Ortiz, J., Maestre, I.R., Coronel, J.F., Constructing HVAC energy efficiency indicators. Energy Build. 47 (2012), 619–629.
54. [54] Liu, L.R., Gu, J.J., Liu, J., Analysis of operational energy intensity for central air conditioning systems with water-cooled chiller by decomposition method. Energy Build. 93 (2015), 154–159.
55. [55] Yan, C., Wang, S., Xiao, F., Gao, D., A multi-level energy performance diagnosis method for energy information poor buildings. Energy 83 (2015), 189–203.
56. [56] Liddiard, R., Room-scale profiles of space use and electricity consumption in non-domestic buildings. Build. Res. Inf. 42:1 (2014), 72–94.
57. [57] Zhaoxia, W., Yan, D., An occupant-based energy consumption prediction model for office equipment. Energy Build. 109 (2015), 12–22.
58. [58] Hensen, J.L.M., Lamberts, R., (eds.) Building Performance Simulation for Design and Operation, 2011, Routledge.
59. [59] CIBSE, AM11: Building Performance Modelling. 2015, CIBSE.
60. [60] ASHRAE, Guideline 14 – Measurement of Energy, Demand, and Water Savings, vol. 2014, 2014, ASHRAE.
61. [61] Crawley, D.B., Hand, J.W., Kummert, M., Griffith, B., Contrasting the capabilities of building energy performance simulation programs. Build. Environ. 43 (2008), 661–673.
62. [62] Sefaira Ltd, Sefaira, 2016. [Online]. 2016 Available: http://sefaira.com/ (accessed 20.03.16).
63. [63] UFSC, S3E, 2016. [Online]. 2016 Available: http://s3e.ufsc.br/ (accessed 20.03.16).
64. [64] DesignBuilder Software Ltd, DesignBuilder, 2016. [Online]. 2016 Available: http://designbuilder.co.uk/ (accessed 20.03.16).
65. [65] DOE, Building Energy Software Tools Directory, 2016. [Online]. 2016 Available: http://www.buildingenergysoftwaretools.com (accessed 20.03.16).
66. [66] Harish, V.S.K.V., Kumar, A., A review on modeling and simulation of building energy systems. Renew. Sustain. Energy Rev. 56 (2016), 1272–1292.
67. [67] Trčka, M., Hensen, J.L.M., Overview of HVAC system simulation. Autom. Constr. 19:2 (2010), 93–99.
68. [68] Malkawi, A., Augenbroe, G., (eds.) Advanced Building Simulation, 2003, Spon Press.
69. [69] Bordass, B., Cohen, R., Standeven, M., Leaman, A., Assessing building performance in use 3: energy performance of the Probe buildings. Build. Res. Inf. 29:2 (2001), 114–128.
70. [70] De Wilde, P., The gap between predicted and measured energy performance of buildings: a framework for investigation. Autom. Constr. 41 (2014), 40–49.
71. [71] van Dronkelaar, C., Dowson, M., Spataru, C., Mumovic, D., A review of the regulatory energy performance gap and its underlying causes in non-domestic buildings. Front. Mech. Eng. 1:January (2016), 1–14.
72. [72] Daly, D., Cooper, P., Ma, Z., Understanding the risks and uncertainties introduced by common assumptions in energy simulations for Australian commercial buildings. Energy Build. 75 (2014), 382–393.
73. [73] Tuohy, P.G., Murphy, G.B., Closing the gap in building performance: learning from BIM benchmark industries. Archit. Sci. Rev. 58:1 (2015), 47–56.
74. [74] Heidarinejad, M., Dahlhausen, M., Mcmahon, S., Pyke, C., Srebric, J., Green, T.U.S., Council, B., Building classification based on simulated annual results: towards realistic building performance expectations. 13th Conf. Int. Build. Perform. Simul. Assoc., 2013, 1706–1713.
75. [75] Demanuele, C., Tweddell, T., Davies, M., Bridging the gap between predicted and actual energy performance in schools. World Renew. Energy Congr. XI, No. September, 2010, 1–6.
76. [76] Dasgupta, A., Prodromou, A., Mumovic, D., Operational versus designed performance of low carbon schools in England: bridging a credibility gap. HVAC&R Res. 18:1 (2012), 37–41.
77. [77] Fedoruk, L.E., Cole, R.J., Robinson, J.B., Cayuela, A., Learning from failure: understanding the anticipated-achieved building energy performance gap. Build. Res. Inf. 43:No. 6 (2015), 750–763.
78. [78] Ryan, E.M., Sanquist, T.F., Validation of building energy modeling tools under idealized and realistic conditions. Energy Build. 47 (2012), 375–382.
79. [79] Jensen, S.O., Validation of building energy simulation programs a methodology. Energy Build. 22 (1995), 133–144.
80. [80] Neymark, J., Judkoff, R., Knabe, G., Le, H.T., Dürig, M., Glass, A., Zweifel, G., Applying the building energy simulation test (BESTEST) diagnostic method to verification of space conditioning equipment models used in whole-building energy simulation programs. Energy Build. 34:9 (2002), 917–931.
81. [81] Henninger, R.H., Witte, M.J., Crawley, D.B., Analytical and comparative testing of EnergyPlus using IEA HVAC BESTEST E100-E200 test suite. Energy Build. 36 (2004), 855–863.
82. [82] ASHRAE, Standard 140-2011 – Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. 2011, ASHRAE.
83. [83] Knight, I., Stravoravdis, S., Lasvaux, S., Predicting operational energy consumption profiles – findings from detailed surveys and modelling in a UK educational building compared to measured consumption. Int. J. Vent. 7:1 (2008), 49–57.
84. [84] Ahmad, M., Culp, C.H., Uncalibrated building energy simulation modeling results. HVAC&R Res. 12:4 (2006), 1141–1155.
85. [85] Li, N., Yang, Z., Becerik-Gerber, B., Tang, C., Chen, N., Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures?. Appl. Energy 159 (2015), 196–205.
86. [86] Raftery, P., Keane, M., O'Donnell, J., Calibrating whole building energy models: an evidence-based methodology. Energy Build. 43:9 (2011), 2356–2364.
87. [87] Fabrizio, E., Monetti, V., Methodologies and advancements in the calibration of building energy models. Energies 8:4 (2015), 2548–2574.
88. [88] Reddy, T.A., Maor, I., Panjapornpon, C., Calibrating detailed building energy simulation programs with measured data – part I: general methodology (RP-1051). HVAC&R Res. 13:2 (2007), 221–241.
89. [89] Pedrini, A., Westphal, F.S., Lamberts, R., A methodology for building energy modelling and calibration in warm climates. Build. Environ. 37:8–9 (2002), 903–912.
90. [90] Reddy, T.A., Maor, I., Panjapornpon, C., Calibrating detailed building energy simulation programs with measured data – part II: application to three case study office buildings (RP-1051). HVAC&R Res. 13:2 (2007), 243–265.
91. [91] Neto, A.H., Fiorelli, F.A.S., Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build. 40:12 (2008), 2169–2176.
92. [92] Mustafaraj, G., Marini, D., Costa, A., Keane, M., Model calibration for building energy efficiency simulation. Appl. Energy 130 (2014), 72–85.
93. [93] Royapoor, M., Roskilly, T., Building model calibration using energy and environmental data. Energy Build. 94 (2015), 109–120.
94. [94] Oak Ridge National Laboratory, AUTOTUNE, 2016. [Online]. 2016 Available: http://rsc.ornl.gov/autotune/ (accessed 23.03.16).
95. [95] Efficiency Valuation Organisation, Concepts and options for determining energy and water savings. International Performance Measurement and Verification Protocol (IPMVP), 2012 EVO vol. 1 January.
96. [96] de Wit, S., Uncertainty in building simulation. Malkawi, A., Augenbroe, G., (eds.) Advanced Building Simulation, 2003, Spon Press.
97. [97] MacDonald, I., Strachan, P., Practical application of uncertainty analysis. Energy Build. 33:3 (2001), 219–227.
98. [98] Tian, W., A review of sensitivity analysis methods in building energy analysis. Renew. Sustain. Energy Rev. 20 (2013), 411–419.
99. [99] Hopfe, C.J., Hensen, J.L.M., Uncertainty analysis in building performance simulation for design support. Energy Build. 43:10 (2011), 2798–2805.
100. [100] De Wit, S., Augenbroe, G., Analysis of uncertainty in building design evaluations and its implications. Energy Build. 34:9 (2002), 951–958.
101. [101] Sun, Y., Heo, Y., Tan, M., Xie, H., Jeff Wu, C.F., Augenbroe, G., Uncertainty quantification of microclimate variables in building energy models. J. Build. Perform. Simul. 7:1 (2014), 17–32.
102. [102] Silva, A.S., Ghisi, E., Uncertainty analysis of the computer model in building performance simulation. Energy Build. 76 (2014), 258–269.
103. [103] Hong, T., Taylor-Lange, S.C., D'Oca, S., Yan, D., Corgnati, S.P., Advances in research and applications of energy-related occupant behavior in buildings. Energy Build. 116 (2015), 694–702.
104. [104] Corgnati, S.P., Fabrizio, E., Filippi, M., The impact of indoor thermal conditions, system controls and building types on the building energy demand. Energy Build. 40:4 (2008), 627–636.
105. [105] Heller, J., Heater, M., Frankel, M., Sensitivity Analysis: Comparing the Impact of Design, Operation, and Tenant Behaviour on Building Energy Performance. 2011, White Paper - New Buildings Institute (NBI).
106. [106] Zhang, T., Siebers, P.O., Aickelin, U., Modelling electricity consumption in office buildings: an agent based approach. Energy Build. 43:10 (2011), 2882–2892.
107. [107] Azar, E., Menassa, C.C., A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings. Energy Build. 55 (2012), 841–853.
108. [108] Hoes, P., Hensen, J.L.M., Loomans, M.G.L.C., de Vries, B., Bourgeois, D., User behavior in whole building simulation. Energy Build. 41:3 (2009), 295–302.
109. [109] IEA, IEA EBC Annex 66, Energy in Buildings and Communities Programme. [Online]. Available: http://annex66.org/ (accessed 20.03.16).
110. [110] US DOE, Commercial Reference Buildings. [Online] Available: http://energy.gov/eere/buildings/commercial-reference-buildings (accessed 20.03.16).
111. [111] Heo, Y., Choudhary, R., Augenbroe, G.A., Calibration of building energy models for retrofit analysis under uncertainty. Energy Build. 47 (2012), 550–560.
112. [112] Nikolaou, T., Skias, I., Kolokotsa, D., Stavrakakis, G., Virtual Building Dataset for energy and indoor thermal comfort benchmarking of office buildings in Greece. Energy Build. 41:12 (2009), 1409–1416.
113. [113] Katafygiotou, M.C., Serghides, D.K., Analysis of structural elements and energy consumption of school building stock in Cyprus: energy simulations and upgrade scenarios of a typical school. Energy Build. 72 (2014), 8–16.
114. [114] Salat, S., Energy loads, CO2 emissions and building stocks: morphologies, typologies, energy systems and behaviour. Build. Res. Inf. 37:5–6 (2009), 598–609.
115. [115] Choudhary, R., Tian, W., Influence of district features on energy consumption in non-domestic buildings. Build. Res. Inf. 42:1 (2014), 32–46.
116. [116] C.C. Davila, C. Reinhart, J. Bemis, Modeling Boston: a workflow for the generation of complete urban building energy demand models from existing urban geospatial datasets,MIT Sustainable Design Lab - Unpublished (under review) 2015.
117. [117] Reinhart, C.F., Davila, C.C., Urban building energy modeling – a review of a nascent field. Build. Environ. 97 (2016), 196–202.
118. [118] Hens, H., Parijs, W., Deurinck, M., Energy consumption for heating and rebound effects. Energy Build. 42:1 (2010), 105–110.
119. [119] ASHRAE, Building Energy Modelling Professional Certification. [Online] Available: https://www.ashrae.org/education-certification/certification/bemp-building-energy-modeling-professional-certification (accessed 20.03.16).
120. [120] IEA, IEA EBC Annex 53 Energy in Buildings and Communities Programme. [Online]. Available: http://www.iea-ebc.org/index.php?id=141 (accessed 20.03.16).
121. [121] Williamson, T.J., Predicting building performance: the ethics of computer simulation. Build. Res. Inf. 38:4 (2010), 401–410.
122. [122] Filippín, C., Benchmarking the energy efficiency and greenhouse gases emissions of school buildings in central Argentina. Build. Environ. 35:5 (2000), 407–414.
123. [123] Martin, C., Generating low-cost national energy benchmarks: a case study in commercial buildings in Cape Town, South Africa. Energy Build. 64 (2013), 26–31.
124. [124] Sharp, T., Energy benchmarking in commercial office buildings. ACEEE Summer Study Energy Effic. Build. 4 (1995), 321–329.
125. [125] Buck, J., Young, D., The potential for energy efficiency gains in the Canadian commercial building sector: a stochastic frontier study. Energy 32:9 (2007), 1769–1780.
126. [126] Lee, W.S., Lin, L.C., Evaluating and ranking the energy performance of office building using technique for order preference by similarity to ideal solution. Appl. Therm. Eng. 31:16 (2011), 3521–3525.
127. [127] Wang, E., Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach. Appl. Energy 146 (2015), 92–103.
128. [128] Lee, W.S., Kung, C.K., Using climate classification to evaluate building energy performance. Energy 36:3 (2011), 1797–1801.
129. [129] Bloomfield, C., Bannister, P., Energy and Water Performance Benchmarking in the Retail Sector. 2010, NABERS Shopping Centres, 1–13.
130. [130] Chung, W., Hui, Y.V., Lam, Y.M., Benchmarking the energy efficiency of commercial buildings. Appl. Energy 83:1 (2006), 1–14.
131. [131] Bohdanowicz, P., Martinac, I., Determinants and benchmarking of resource consumption in hotels—case study of Hilton International and Scandic in Europe. Energy Build. 39:1 (2007), 82–95.
132. [132] Aranda, A., Ferreira, G., Mainar-Toledo, M.D., Scarpellini, S., Llera Sastresa, E., Multiple regression models to predict the annual energy consumption in the Spanish banking sector. Energy Build. 49 (2012), 380–387.
133. [133] Ghiaus, C., Experimental estimation of building energy performance by robust regression. Energy Build. 38:6 (2006), 582–587.
134. [134] Sabapathy, A., Ragavan, S.K.V., Vijendra, M., Nataraja, A.G., Energy efficiency benchmarks and the performance of LEED rated buildings for Information Technology facilities in Bangalore, India. Energy Build. 42:11 (2010), 2206–2212.
135. [135] Guo, Q., Tian, Z., Ding, Y., Zhu, N., An improved office building cooling load prediction model based on multivariable linear regression. Energy Build. 107 (2015), 445–455.
136. [136] So, N., Richman, R., A high level method to disaggregate electricity for cluster-metered buildings. Energy Build. 111 (2016), 351–368.
137. [137] Hsu, D., How much information disclosure of building energy performance is necessary?. Energy Policy 64 (2014), 263–272.
138. [138] Olofsson, T., Andersson, S., Sjögren, J.U., Building energy parameter investigations based on multivariate analysis. Energy Build. 41:1 (2009), 71–80.
139. [139] Önüt, S., Soner, S., Energy efficiency assessment for the Antalya Region hotels in Turkey. Energy Build. 38:8 (2006), 964–971.
140. [140] Lee, W.-S., Benchmarking the energy efficiency of government buildings with data envelopment analysis. Energy Build. 40:5 (2008), 891–895.
141. [141] Lee, W.-S., Lee, K.-P., Benchmarking the performance of building energy management using data envelopment analysis. Appl. Therm. Eng. 29:16 (2009), 3269–3273.
142. [142] Lee, W.-S., Benchmarking the energy performance for cooling purposes in buildings using a novel index-total performance of energy for cooling purposes. Energy 35:1 (2010), 50–54.
143. [143] Ruiz, J.L., Segura, J.V., Sirvent, I., Benchmarking and target setting with expert preferences: an application to the evaluation of educational performance of Spanish universities. Eur. J. Oper. Res. 242:2 (2015), 594–605.
144. [144] Lam, J.C., Wan, K.K.W., Wong, S.L., Lam, T.N.T., Principal component analysis and long-term building energy simulation correlation. Energy Convers. Manage. 51:1 (2010), 135–139.
145. [145] Hong, S.-M., Paterson, G., Mumovic, D., Steadman, P., Improved benchmarking comparability for energy consumption in schools. Build. Res. Inf. 42:1 (2014), 47–61.
146. [146] Bordass, B., Cohen, R., Spevack, R., Burman, E., Hong, S.-M., Ruyssevelt, P., Field, J., Tailored energy benchmarks for offices and schools, and their wider potential. CIBSE ASHRAE Tech. Symp. Dublin, Ireland, 3–4 April 2014, No. April, 2014, 1–15.
147. [147] Hsu, D., Identifying key variables and interactions in statistical models of building energy consumption using regularization. Energy 83 (2015), 144–155.
148. [148] Kalogirou, S.a., Artificial neural networks in energy applications in buildings. Int. J. Low-Carbon 1:3 (2006), 694–702.
149. [149] Ekici, B.B., Aksoy, U.T., Prediction of building energy consumption by using artificial neural networks. Adv. Eng. Softw. 40:5 (2009), 356–362.
150. [150] Yalcintas, M., An energy benchmarking model based on artificial neural network method with a case example for tropical climates. Int. J. Energy Res. 30:14 (2006), 1158–1174.
151. [151] Hou, Z., Lian, Z., Yao, Y., Yuan, X., Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique. Appl. Energy 83:9 (2006), 1033–1046.
152. [152] Li, K., Hu, C., Liu, G., Xue, W., Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy Build. 108 (2015), 106–113.
153. [153] Melo, A.P., Cóstola, D., Lamberts, R., Hensen, J.L.M., Development of surrogate models using arti fi cial neural network for building shell energy labelling. Energy Policy 69 (2014), 457–466.
154. [154] Gao, X., Malkawi, A., A new methodology for building energy performance benchmarking: an approach based on intelligent clustering algorithm. Energy Build. 84 (2014), 607–616.
155. [155] Gaitani, N., Lehmann, C., Santamouris, M., Mihalakakou, G., Patargias, P., Using principal component and cluster analysis in the heating evaluation of the school building sector. Appl. Energy 87:6 (2010), 2079–2086.
156. [156] Hsu, D., Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data. Appl. Energy 160 (2015), 153–163.
157. [157] Santamouris, M., Mihalakakou, G., Patargias, P., Gaitani, N., Sfakianaki, K., Papaglastra, M., Pavlou, C., Doukas, P., Primikiri, E., Geros, V., Assimakopoulos, M.N., Mitoula, R., Zerefos, S., Using intelligent clustering techniques to classify the energy performance of school buildings. Energy Build. 39:1 (2007), 45–51.
158. [158] Lam, J.C., Wan, K.K.W., Cheung, K.L., An analysis of climatic influences on chiller plant electricity consumption. Appl. Energy 86:6 (2009), 933–940.
159. [159] Yu, F.W., Chan, K.T., Using cluster and multivariate analyses to appraise the operating performance of a chiller system serving an institutional building. Energy Build. 44:1 (2012), 104–113.
160. [160] Petcharat, S., Chungpaibulpatana, S., Rakkwamsuk, P., Assessment of potential energy saving using cluster analysis: a case study of lighting systems in buildings. Energy Build. 52 (2012), 145–152.
161. [161] Arambula Lara, R., Pernigotto, G., Cappelletti, F., Romagnoni, P., Gasparella, A., Energy audit of schools by means of cluster analysis. Energy Build. 95 (2015), 160–171.
162. [162] Pieri, S.P., Tzouvadakis, I., Santamouris, M., Identifying energy consumption patterns in the Attica hotel sector using cluster analysis techniques with the aim of reducing hotels’ CO2 footprint. Energy Build. 94 (2015), 252–262.
163. [163] Famuyibo, A.A., Duffy, A., Strachan, P., Developing archetypes for domestic dwellings – an Irish case study. Energy Build. 50 (2012), 150–157.
164. [164] Yu, Z., Fung, B.C.M., Haghighat, F., Yoshino, H., Morofsky, E., A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy Build. 43:6 (2011), 1409–1417.
165. [165] Dong, B., Cao, C., Lee, S.E., Applying support vector machines to predict building energy consumption in tropical region. Energy Build. 37:5 (2005), 545–553.
166. [166] Li, Q., Meng, Q., Cai, J., Yoshino, H., Mochida, A., Applying support vector machine to predict hourly cooling load in the building. Appl. Energy 86:10 (2009), 2249–2256.
167. [167] Zhao, H., Magoulès, F., Parallel support vector machines applied to the prediction of multiple buildings energy consumption. J. Algorithm Comput. Technol. 4:2 (2010), 231–249.
168. [168] Yu, Z., Haghighat, F., Fung, B.C.M., Yoshino, H., A decision tree method for building energy demand modeling. Energy Build. 42:10 (2010), 1637–1646.
169. [169] Li, K., Su, H., Chu, J., Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: a comparative study. Energy Build. 43:10 (2011), 2893–2899.
170. [170] Li, Z., Huang, G., Re-evaluation of building cooling load prediction models for use in humid subtropical area. Energy Build. 62 (2013), 442–449.
171. [171] Pang, X., Wetter, M., Bhattacharya, P., Haves, P., A framework for simulation-based real-time whole building performance assessment. Build. Environ. 54 (2012), 100–108.
172. [172] Yun, K., Luck, R., Mago, P.J., Cho, H., Building hourly thermal load prediction using an indexed ARX model. Energy Build. 54 (2012), 225–233.
173. [173] Karatasou, S., Santamouris, M., Geros, V., Modeling and predicting building's energy use with artificial neural networks: methods and results. Energy Build. 38:8 (2006), 949–958.
174. [174] Kusiak, A., Xu, G., Modeling and optimization of HVAC systems using a dynamic neural network. Energy 42:1 (2012), 241–250.
175. [175] De Wilde, P., Martinez-Ortiz, C., Pearson, D., Beynon, I., Beck, M., Barlow, N., Building simulation approaches for the training of automated data analysis tools in building energy management. Adv. Eng. Inf. 27:4 (2013), 457–465.
176. [176] Yan, C., Wang, S., Xiao, F., A simplified energy performance assessment method for existing buildings based on energy bill disaggregation. Energy Build. 55 (2012), 563–574.
177. [177] Iyer, S.R., Sankar, M., Venkata Ramakrishna, P., Sarangan, V., Vasan, A., Sivasubramaniam, A., Energy disaggregation analysis of a supermarket chain using a facility-model. Energy Build. 97 (2015), 65–76.
178. [178] Rasanen, T., Voukantsis, D., Niska, H., Karatzas, K., Kolehmainen, M., Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data. Appl. Energy 87:11 (2010), 3538–3545.
179. [179] Panapakidis, I.P., Papadopoulos, T.a., Christoforidis, G.C., Papagiannis, G.K., Pattern recognition algorithms for electricity load curve analysis of buildings. Energy Build. 73 (2014), 137–145.
180. [180] Gul, M.S., Patidar, S., Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build. 87 (2015), 155–165.
181. [181] Jota, P.R.S., Silva, V.R.B., Jota, F.G., Building load management using cluster and statistical analyses. Int. J. Electr. Power Energy Syst. 33:8 (2011), 1498–1505.
182. [182] CIBSE, CIBSE Guide F: Energy Efficiency in Buildings. 2012, Chartered Institute of Building Services Engineers.
183. [183] Leaman, A., Stevenson, F., Bordass, B., Building evaluation: practice and principles. Build. Res. Inf. 38:5 (2010), 564–577.
184. [184] P. Steskens, M. Loomans, PERFECTION: T1.3 – Performance Indicators for Health and Comfort,PERFECTION – Performance Indicators for Health, Comfort and Safety of the Indoor Environment, research project under EU FP7, Report T1.3 2010.
185. [185] Goçer, O., Hua, Y., Goçer, K., Completing the missing link in building design process: enhancing post-occupancy evaluation method for effective feedback for building performance. Build. Environ. 89 (2015), 14–27.
186. [186] Yang, L., Yan, H., Lam, J.C., Thermal comfort and building energy consumption implications – a review. Appl. Energy 115 (2014), 164–173.
187. [187] Menezes, A.C., Cripps, A., Bouchlaghem, D., Buswell, R., Predicted vs. actual energy performance of non-domestic buildings: using post-occupancy evaluation data to reduce the performance gap. Appl. Energy 97 (2012), 355–364.
188. [188] US Department of Energy, Benchmarking & Transparency Policy and Program Impact Evaluation Handbook. 2015, US Department of Energy.
189. [189] US Green Building Council, LEED – Leadership in Energy and Environmental Design. [Online]. Available: http://www.usgbc.org/LEED/ (accessed 20.03.16).
190. [190] US EPA, Energy Star Portfolio Manager. [Online] Available: https://www.energystar.gov/buildings/facility-owners-and-managers/existing-buildings/use-portfolio-manager.
191. [191] US Energy Information Administration, Commercial Building Energy Consumption Survey. [Online]. Available: http://www.eia.gov/consumption/commercial/ (accessed 20.03.16).
192. [192] California Energy Commission, California Commercial End-Use Survey (CEUS), Analysis, no. March, 2006.
193. [193] Natural Resources Canada, Survey of Commercial and Institutional Energy Use: Buildings 2009, 2012.
194. [194] Nicholls, C., Energy use in non-domestic buildings: the UK government's new evidence base. Build. Res. Inf. 42:1 (2014), 109–117.
195. [195] N.E. Matson, M.A. Piette, Review of California and National Methods for Energy-Performance Benchmarking of Commercial Buildings, Ernest Orlando Lawrence Berkeley National Laboratory, LBNL no. 57364, 2005,.
196. [196] ANSI/ASHRAE/IES, Standard 90.1 – Energy Standard for Buildings Except Low-Rise Residential Buildings. 2013, ASHRAE.
197. [197] Corgnati, S.P., Corrado, V., Energy monitoring and labelling. Mumovic, D., Santamouris, M., (eds.) A Handbook of Sustainable Building Design and Engineering, 2009, Earthscan.
198. [198] Cohen, R., Bordass, B., Mandating transparency about building energy performance in use. Build. Res. Inf. 43:4 (2015), 534–552.
199. [199] Bannister, P., NABERS: lessons from 12 years of performance-based ratings in Australia. ICEBO - International Conference for Enhanced Building Operations, 2012.
200. [200] Rajagopalan, P., Leung Tony, C.Y., Progress on building energy labelling techniques. Adv. Build. Energy Res. 6:1 (2012), 61–80.
201. [201] Leipziger, D., Comparing Building Energy Performance Measurement. A framework for international energy efficiency assessment systems. 2013, Institute for Market Transformation, Washington.
202. [202] Mo, K., Burt, L., Hao, B., Cheng, J., Burr, A., Kemkar, S., Comparative analysis of U.S. and China building energy rating and labeling systems China's MOHURD rating and labeling program. Proc. ACEEE Summer Study Energy Effic. Build., 2010, 256–269.
203. [203] Kerr, L., Beber, H., Hope, D., New York City Local Law 84 Benchmarking Report., 2012, PlaNYC, The City of New York, New York.
204. [204] Institute for Market Transformation, BuildingRating.org. [Online] Available: http://www.buildingrating.org/ (accessed 20.03.16).
205. [205] Building Research Establishment, BREEAM – BRE Environmental Assessment Methodology. [Online]. Available: http://www.breeam.com/ (accessed 20.03.16).
206. [206] Schwartz, Y., Raslan, R., Variations in results of building energy simulation tools, and their impact on BREEAM and LEED ratings: a case study. Energy Build. 62 (2013), 350–359.
207. [207] Newsham, G.R., Mancini, S., Birt, B.J., Do LEED-certified buildings save energy? Yes, but…. Energy Build. 41:8 (2009), 897–905.
208. [208] Scofield, J.H., Do LEED-certified buildings save energy? Not really. Energy Build. 41:12 (2009), 1386–1390.
209. [209] Henry Gifford, et al., v. United States Green Building Council, et al., U.S. District Court for the Southern District of New York, vol. 10 CIV 774.
210. [210] International Code Council, International Green Construction Code, 2012.
211. 211] Bruhns, H., Jones, P., Cohen, R., Bordass, B., Hywel Davies, CIBSE review of energy benchmarks for display energy certificates - analysis of DEC results to date. Proceedings of the CIBSE Technical Symposium, DeMontfort University, Leicester, 2011.
212. [212] Armitage, P., Godoy-Shimizu, D., Steemers, K., Chenvidyakarn, T., Using Display Energy Certificates to quantify public sector office energy consumption. Build. Res. Inf. 43:6 (2014), 691–709.
213. [213] Scofield, J.H., Efficacy of LEED-certification in reducing energy consumption and greenhouse gas emission for large New York City office buildings. Energy Build. 67 (2013), 517–524.
214. [214] Mills, E., Friedman, H., Powell, T., Bourassa, N., Claridge, Da., Haasl, T., Piette, M.A., The cost-effectiveness of commercial-buildings commissioning. Lawrence Berkeley Natl. Lab., 2004.
215. [215] de Wilde, P., Tian, W., Augenbroe, G., Longitudinal prediction of the operational energy use of buildings. Build. Environ. 46:8 (2011), 1670–1680.
216. [216] Bordass, B., Leaman, A., Ruyssevelt, P., Assessing building performance in use 5: conclusions and implications. Build. Res. Inf. 29:2 (2001), 37–41.
217. [217] Pérez-Lombard, L., Ortiz, J., Velázquez, D., Revisiting energy efficiency fundamentals. Energy Effic. 6:2 (2013), 239–254.