A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres

Applied Energy

Volumn 171, Issue , 2016, Pages 592-607

  Capozzoli, Alfonso ^a   Piscitelli, Marco Savino ^a   Neri, Francesco ^b   Grassi, Daniele ^a   Serale, Gianluca ^a  

^a POLITECNICO DI TORINO   (Italy)

^b EXITone Spa   (Italy)

Author keywords

Building energy performance; Energy benchmarking analysis; Healthcare Centres; Linear Mixed Effect Model; Monte Carlo simulation; Supervised learning technique

Indexed keywords

BUILDINGS; ENERGY CONSERVATION; ENERGY EFFICIENCY; ENERGY UTILIZATION; HEALTH CARE; HEATING; INTELLIGENT SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MONTE CARLO METHODS; POPULATION STATISTICS; RANDOM PROCESSES; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; SUPERVISED LEARNING;

ANNUAL ENERGY CONSUMPTION; BUILDING ENERGY PERFORMANCE; ENERGY BENCHMARKING; FREQUENCY DISTRIBUTIONS; IMPROVING PERFORMANCE; LINEAR MIXED-EFFECTS MODEL; MIXED-EFFECT MODELS; STOCHASTIC SIMULATIONS;

BENCHMARKING;

BUILDING; ENERGY EFFICIENCY; ENERGY USE; HEALTH CARE; HEATING; MONTE CARLO ANALYSIS; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; SIMULATION; SUPERVISED LEARNING;

ITALY;

EID: 84961626167     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.03.083     Document Type: Article

References (71)

1. International Energy Agency. Key World Energy Statistics, 2015.
2. European Parliament Directive 2012/27/EU of the European Parliament and of the Council of 25 on energy efficiency. Off J Eur Union Dir 2012, (October):1-56. 10.3000/19770677.L_2012.315.eng.
3. Zhao L., Zhang J., Liang R. Development of an energy monitoring system for large public buildings. Energy Build 2013, 66:41-48. 10.1016/j.enbuild.2013.07.007.
4. Ma Z., Cooper P., Daly D., Ledo L. Existing building retrofits: methodology and state-of-the-art. Energy Build 2012, 55:889-902. 10.1016/j.enbuild.2012.08.018.
5. Pérez-Lombard L., Ortiz J., Pout C. A review on buildings energy consumption information. Energy Build 2008, 40:394-398. 10.1016/j.enbuild.2007.03.007.
6. Sartor D., Piette M.A., Tschudi W., Fok S. Strategies for energy benchmarking in cleanrooms and laboratory-type facilities 2000, Lawrence Berkeley Natl Lab.
7. Wang S., Yan C., Xiao F. Quantitative energy performance assessment methods for existing buildings. Energy Build 2012, 55:873-888. 10.1016/j.enbuild.2012.08.037.
8. Cohen R., Standeven M., Bordass B., Leaman A. Assessing building performance in use 1: the Probe process. Build Res Inf 2001, 29:85-102. 10.1080/09613210010008018.
9. Lee W.-L., Yik F.W.H., Burnett J. A method to assess the energy performance of existing commercial complexes. Indoor Built Environ 2003, 12:311-327. 10.1177/142032603035424.
10. Fabrizio E., Corrado V., Filippi M. A model to design and optimize multi-energy systems in buildings at the design concept stage. Renew Energy 2010, 35:644-655. 10.1016/j.renene.2009.08.012.
11. Hong T., Piette M.A., Chen Y., Lee S.H., Taylor-Lange S.C., Zhang R., et al. Commercial building energy saver: an energy retrofit analysis toolkit. Appl Energy 2015, 159:298-309. 10.1016/j.apenergy.2015.09.002.
12. Mauro G.M., Hamdy M., Vanoli G.P., Bianco N., Hensen J.L.M. A new methodology for investigating the cost-optimality of energy retrofitting a building category. Energy Build 2015, 107:456-478. 10.1016/j.enbuild.2015.08.044.
13. Lee S.H., Hong T., Piette M.A., Taylor-Lange S.C. Energy retrofit analysis toolkits for commercial buildings: a review. Energy 2015, 10.1016/j.energy.2015.06.112.
14. Tahsildoost M., Zomorodian Z.S. Energy retrofit techniques: an experimental study of two typical school buildings in Tehran. Energy Build 2015, 104:65-72. 10.1016/j.enbuild.2015.06.079.
15. Al-Homoud M.S. Computer-aided building energy analysis techniques. Build Environ 2001, 36:421-433. 10.1016/S0360-1323(00)00026-3.
16. Filogamo L., Peri G., Rizzo G., Giaccone A. On the classification of large residential buildings stocks by sample typologies for energy planning purposes. Appl Energy 2014, 135:825-835. 10.1016/j.apenergy.2014.04.002.
17. Zhang Y., O'Neill Z., Dong B., Augenbroe G. Comparisons of inverse modeling approaches for predicting building energy performance. Build Environ 2015, 86:177-190. 10.1016/j.buildenv.2014.12.023.
18. Petcharat S., Chungpaibulpatana S., Rakkwamsuk P. Assessment of potential energy saving using cluster analysis: a case study of lighting systems in buildings. Energy Build 2012, 52:145-152. 10.1016/j.enbuild.2012.06.006.
19. Gao X. A new methodology for building energy benchmarking: an approach based on clustering concept and statistical models. Energy Build 2013, 84:607-616. 10.1016/j.enbuild.2014.08.030.
20. Hong T., Yang L., Hill D., Feng W. Data and analytics to inform energy retrofit of high performance buildings. Appl Energy 2014, 126:90-106. 10.1016/j.apenergy.2014.03.052.
21. 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 2015, 146:92-103. 10.1016/j.apenergy.2015.02.048.
22. 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 2011, 31:3521-3525. 10.1016/j.applthermaleng.2011.07.005.
23. Yu Z.(Jerry), Haghighat F., Fung B.C.M. Advances and challenges in building engineering and data mining applications for energy-efficient communities. Sustain Cities Soc 2015, 10.1016/j.scs.2015.12.00.
24. Li Q., Meng Q., Cai J., Yoshino H., Mochida A. Predicting hourly cooling load in the building: a comparison of support vector machine and different artificial neural networks. Energy Convers Manage 2009, 50:90-96. 10.1016/j.apenergy.2008.11.035.
25. Aydinalp M., Ismet Ugursal V., Fung A.S. Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks. Appl Energy 2004, 79:159-178. 10.1016/j.apenergy.2003.12.006.
26. Karatasou S., Santamouris M., Geros V. Modeling and predicting building's energy use with artificial neural networks: methods and results. Energy Build 2006, 38:949-958. 10.1016/j.enbuild.2005.11.005.
27. Mihalakakou G., Santamouris M., Tsangrassoulis A. On the energy consumption in residential buildings. Energy Build 2002, 34:727-736. 10.1016/S0378-7788(01)00137-2.
28. Li Q., Meng Q., Cai J., Yoshino H., Mochida A. Predicting hourly cooling load in the building: a comparison of support vector machine and different artificial neural networks. Appl Energy 2009, 86:2249-2256. 10.1016/j.apenergy.2008.11.035.
29. Dong B., Cao C., Lee S.E. Applying support vector machines to predict building energy consumption in tropical region. Energy Build 2005, 37:545-553. 10.1016/j.enbuild.2004.09.009.
30. Heo Y., Zavala V.M. Gaussian process modeling for measurement and verification of building energy savings. Energy Build 2012, 53:7-18. 10.1016/j.enbuild.2012.06.024.
31. Manfren M., Aste N., Moshksar R. Calibration and uncertainty analysis for computer models - a meta-model based approach for integrated building energy simulation. Appl Energy 2013, 103:627-641. 10.1016/j.apenergy.2012.10.031.
32. Capozzoli A., Grassi D., Causone F. Estimation models of heating energy consumption in schools for local authorities planning. Energy Build 2015, 105:302-313. 10.1016/j.enbuild.2015.07.024.
33. Vu D.H., Muttaqi K.M., Agalgaonkar A.P. A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Appl Energy 2015, 140:385-394. 10.1016/j.apenergy.2014.12.011.
34. Braun M.R., Altan H., Beck S.B.M. Using regression analysis to predict the future energy consumption of a supermarket in the UK. Appl Energy 2014, 130:305-313. 10.1016/j.apenergy.2014.05.062.
35. Chung W., Hui Y.V., Lam Y.M. Benchmarking the energy efficiency of commercial buildings. Appl Energy 2006, 83:1-14. 10.1016/j.apenergy.2004.11.003.
36. Chung W. Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings. Appl Energy 2012, 95:45-49. 10.1016/j.apenergy.2012.01.061.
37. Li Z., Han Y., Xu P. Methods for benchmarking building energy consumption against its past or intended performance: an overview. Appl Energy 2014, 124:325-334. 10.1016/j.apenergy.2014.03.020.
38. Chung W. Review of building energy-use performance benchmarking methodologies. Appl Energy 2011, 88:1470-1479. 10.1016/j.apenergy.2010.11.022.
39. Energy Star Energy star performance ratings - technical methodology 2011, Environmental Protection Agency, Washington (DC), [n.d.].
40. Huber S., Klein E., Moeller K., Willmes K. Comparing a single case to a control group - applying linear mixed effects models to repeated measures data. Cortex 2015, 71:148-159. 10.1016/j.cortex.2015.06.020.
41. Tu Y.-K. Using generalized linear mixed models to evaluate inconsistency within a network meta-analysis. Value Heal 2015, 18:1120-1125. 10.1016/j.jval.2015.10.002.
42. Kohli N., Sullivan A.L., Sadeh S., Zopluoglu C. Longitudinal mathematics development of students with learning disabilities and students without disabilities: a comparison of linear, quadratic, and piecewise linear mixed effects models. J Sch Psychol 2015, 53:105-120. 10.1016/j.jsp.2014.12.002.
43. Bjork K.E., Kopral C.A., Wagner B.A., Dargatz D.A. Comparison of mixed effects models of antimicrobial resistance metrics of livestock and poultry Salmonella isolates from a national monitoring system. Prev Vet Med 2015, 122:265.
44. Khodakarami J., Nasrollahi N. Thermal comfort in hospitals - a literature review. Renew Sustain Energy Rev 2012, 16:4071-4077. 10.1016/j.rser.2012.03.054.
45. ASHRAE Technical Committee (TC) 9.6 HFC. HVAC design manual for hospitals and clinics, 2nd ed. Atlanta: 2013.
46. Energy U.S. Department Select results from the energy assessor experiment in the 2012 Commercial buildings energy consumption survey 2015, 1-27. US Energy Inf Adm.
47. García Sanz-Calcedo J., Cuadros Blázquez F., López Rodríguez F., Ruiz-Celma A. Influence of the number of users on the energy efficiency of health centres. Energy Build 2011, 43:1544-1548. 10.1016/j.enbuild.2011.02.012.
48. Salem Szklo A., Soares J.B., Tolmasquim M.T. Energy consumption indicators and CHP technical potential in the Brazilian hospital sector. Energy Convers Manage 2004, 45:2075-2091. 10.1016/j.enconman.2003.10.019.
49. Energy U.S. Department Commercial buildings energy consumption survey 2006, 1-269. US Energy Inf Adm.
50. Chung M., Park H.-C. Comparison of building energy demand for hotels, hospitals, and offices in Korea. Energy 2015, 92:383-393. 10.1016/j.energy.2015.04.016.
51. García-Sanz-Calcedo J., López-Rodríguez F., Cuadros F. Quantitative analysis on energy efficiency of health centers according to their size. Energy Build 2014, 73:7-12. 10.1016/j.enbuild.2014.01.021.
52. Murray J., Pahl O., Burek S. Evaluating the scope for energy-efficiency improvements in the public sector: benchmarking NHSScotland's smaller health buildings. Energy Policy 2008, 36:1236-1242. 10.1016/j.enpol.2007.11.021.
53. Fabrizio E. Feasibility of polygeneration in energy supply systems for health-care facilities under the Italian climate and boundary conditions. Energy Sustain Dev 2011, 15:92-103. 10.1016/j.esd.2011.01.003.
54. Buonomano A., Calise F., Ferruzzi G., Palombo A. Dynamic energy performance analysis: case study for energy efficiency retrofits of hospital buildings. Energy 2014, 78:555-572. 10.1016/j.energy.2014.10.042.
55. Bonnema E, Leach M, Pless S, Liu B, Wang W, Thornton B. 50% Advanced Energy Design Guides Preprint, 2012.
56. Bonnema E, Doebber I, Pless S, Torcellini P, Bonnema E, Doebber I, et al. Technical Support Document : Development of the Advanced Energy Design Guide for Small Hospitals and Healthcare Facilities - 30% Guide. 2010.
57. Bonnema E, Studer D, Parker A, Pless S, Torcellini P, Bonnema E, et al. Large Hospital 50% Energy Savings : Technical Support Document. 2010.
58. Hammad F., Abu-Hijleh B. The energy savings potential of using dynamic external louvers in an office building. Energy Build 2010, 42:1888-1895. 10.1016/j.enbuild.2010.05.024.
59. Lima T.P., Dutra J.C.C., Primo A.R.M., Rohatgi J., Ochoa A.A.V. Solar water heating for a hospital laundry: a case study. Sol Energy 2015, 122:737-748. 10.1016/j.solener.2015.10.006.
60. Costa A., Fichera A. A mixed-integer linear programming (MILP) model for the evaluation of CHP system in the context of hospital structures. Appl Therm Eng 2014, 71:921-929. 10.1016/j.applthermaleng.2014.02.051.
61. Anvari S., Taghavifar H., Saray R.K., Khalilarya S., Jafarmadar S. Implementation of ANN on CCHP system to predict trigeneration performance with consideration of various operative factors. Energy Convers Manage 2015, 101:503-514. 10.1016/j.enconman.2015.05.045.
62. Gimelli A., Muccillo M. Optimization criteria for cogeneration systems: multi-objective approach and application in an hospital facility. Appl Energy 2013, 104:910-923. 10.1016/j.apenergy.2012.11.076.
63. Pagliarini G., Corradi C., Rainieri S. Hospital CHCP system optimization assisted by TRNSYS building energy simulation tool. Appl Therm Eng 2012, 44:150-158. 10.1016/j.applthermaleng.2012.04.001.
64. Ortiga J., Bruno J.C., Coronas A. Selection of typical days for the characterisation of energy demand in cogeneration and trigeneration optimisation models for buildings. Energy Convers Manage 2011, 52:1934-1942. 10.1016/j.enconman.2010.11.02.
65. Mavrotas G., Florios K., Vlachou D. Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters. Energy Convers Manage 2010, 51:722-731. 10.1016/j.enconman.2009.10.029.
66. Čongradac V., Prebiračević B., Petrovački N. Methods for assessing energy savings in hospitals using various control techniques. Energy Build 2014, 69:85-92. 10.1016/j.enbuild.2013.10.023.
67. Dedesko S., Stephens B., Gilbert J.A., Siegel J.A. Methods to assess human occupancy and occupant activity in hospital patient rooms. Build Environ 2015, 90:136-145. 10.1016/j.buildenv.2015.03.029.
68. Capozzoli A., Lauro F., Khan I. Fault detection analysis using data mining techniques for a cluster of smart office buildings. Expert Syst Appl 2015, 42:4324-4338. 10.1016/j.eswa.2015.01.010.
69. Ministro dello sviluppo economico. Decreto Ministeriale 26/06/2015. Applicazione delle metodologie di calcolo delle prestazioni energetiche e definizione delle prescrizioni e dei requisiti minimi degli edifici. Roma: 2015.
70. EN ISO 13790. Energy performance of buildings - calculation of energy use for space heating and cooling. 2008.
71. Capozzoli A., Grassi D., Piscitelli M.S., Serale G. Discovering knowledge from a residential building stock through data mining analysis for engineering sustainability. Energy Proc 2015, 83:370-379. 10.1016/j.egypro.2015.12.212.