Affordable and Clean Energy

Living Edition
| Editors: Walter Leal Filho, Anabela Marisa Azul, Luciana Brandli, Amanda Lange Salvia, Tony Wall

Energy Modelling: Methods and Applications

  • Bianca Gasparetto RebelattoEmail author
  • Marcos Antonio Leite Frandoloso
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-71057-0_62-1
  • 104 Downloads

Definitions

The energy modelling can be described as the process of creating or using a model that focus on energy as an economic resource (Samouilidis 1980). It consists in to capture characteristics of real systems and represents in a computer the behavior that the system would present in the same boundary conditions as in reality (Chwif and Medina 2007). In addition, energy models can have data incorporated which allows to make better decisions in the processes of buildings design and controls. Thus, energy modelling is a way to increase the performance and control an energy system.

Introduction

The Sustainable Development Goals (SDG) are part of the Agenda 2030 that aims to build a more equal, prosperous, and secure world. The seventh goal is “Affordable and clean energy” with the objective to ensure access to affordable, reliable, sustainable, and modern energy for all. In order to achieve this target, it is necessary to reduce, control, and monitor energy consumption and energy...

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References

  1. Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211CrossRefGoogle Scholar
  2. Allegrini J, Orehounig K, Mavromatidis G, Ruesch F, Dorer V, Evins R (2015) A review of modelling approaches and tools for the simulation of district-scale energy systems. Renew Sustain Energ Rev 52:1391–1404.  https://doi.org/10.1016/j.rser.2015.07.123CrossRefGoogle Scholar
  3. Al-Shemmeri T, Naylor L (2017) Energy saving in UK FE colleges: the relative importance of the socio-economic groups and environmental attitudes of employees. Renew Sust Energ Rev 68:1130–1143CrossRefGoogle Scholar
  4. Altan H, Shiram R, Kim Y, Mohammadian K, Zemleduch B (2013) Using energy modelling for calculations of energy savings, payback and return on investment for a typical commercial office building with IBT systems. Paper presented at the 13th conference of International Building Performance Simulation Association, Chambéry, 26–28 Aug 2013Google Scholar
  5. Asdrubali F, Ballaromo I, Corrado V, Evangelisti L, Grazieschi G, Guattari C (2018) Energy and environmental payback times for an NZEB retrofit. Build Environ. Available online.  https://doi.org/10.1016/j.buildenv.2018.10.047CrossRefGoogle Scholar
  6. Attia S et al (2009) “Architect friendly”: a comparison of ten different building performance simulation tools. In: Building simulation. Paper presented at the 11th International IBPSA Conference, Glasgow, 27–30 July 2009. Available at: http://www.ibpsa.org/proceedings/BS2009/BS09_0204_211.pdf. Accessed 23 Aug 2018
  7. Attia S et al (2012) Selection criteria for building performance simulation tools: contrasting architects and engineers needs. J Build Perform Simul 5:155–169.  https://doi.org/10.1080/19401493.2010.549573CrossRefGoogle Scholar
  8. Attia S et al (2013) Achieving informed decision-making for net zero energy buildings design using building performance simulation tools. J Build Perform Simul 6:3–21.  https://doi.org/10.1007/s12273-013-0105-zCrossRefGoogle Scholar
  9. Attiq S, Rasool H, Iqbal S (2017) The impact of supportive work environment, trust, and self-efficacy on organizational learning and its effectiveness: a stimulus-organism response approach. Bus Econ Rev 9:73–100CrossRefGoogle Scholar
  10. Augenbroe G (2002) Trends in building simulation. Build Environ 37(8):891–902.  https://doi.org/10.1016/S0360-1323(02)00041-0CrossRefGoogle Scholar
  11. Bazan E, Jaber MY, El Saadany AMA (2015) Carbon emissions and energy effects on manufacturing-remanufacturing inventory models. Comput Ind Eng 88:307–316CrossRefGoogle Scholar
  12. Brand S (1994) How buildings learn: what happens after they’re built. Viking Penguin, New YorkGoogle Scholar
  13. Brogger M, Wittchen BK (2018) Estimating the energy-saving potential in national building stocks – a methodology review. Renew Sust Energ Rev 82:1489–1496.  https://doi.org/10.1016/j.rser.2017.05.239CrossRefGoogle Scholar
  14. Buffat R, Froemelt A, Heeren N, Raubal M, Hellweg S (2017) Big data GIS analysis for novel approaches in building stock modelling. Appl Energy 208:277–290.  https://doi.org/10.1016/j.apenergy.2017.10.041CrossRefGoogle Scholar
  15. Carbon Trust (2011) Energy management e a comprehensive guide to controlling energy use. Carbon Trust, LondonGoogle Scholar
  16. Cerezo C, Sokol J, Alkhaled S, Reinhart C, Al-Mumin A, Hajiah A (2017) Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): a residential case study in Kuwait City. Energ Buildings 154:321–334.  https://doi.org/10.1016/j.enbuild.2017.08.029CrossRefGoogle Scholar
  17. Chan TK, Cheung CM, Lee ZW (2017) The state of online impulse-buying research: a literature analysis. Inf Manag 54:204–217CrossRefGoogle Scholar
  18. Chong A, Menberg K (2018) Guidelines for the Bayesian calibration of building energy models. Energ Buildings 174:527–547.  https://doi.org/10.1016/j.enbuild.2018.06.028CrossRefGoogle Scholar
  19. Chwif L, Medina AC (2007) Modelagem e Simulação de Eventos Discretos: Teoria e Aplicações. São Paulo, p 254Google Scholar
  20. Claridge DE (2011) Building simulation for practical operational optimization. In: Hensen JLM, Lamberts R (eds) Building performance simulation for design and operation. Spon Press, London, pp 365–401Google Scholar
  21. Costa A, Keane MM, Torres IJ, Corry E (2011) Building operation and energy performance: monitoring, analysis and optimisation toolkit. Appl Energ 101:310–316.  https://doi.org/10.1016/j.apenergy.2011.10.037CrossRefGoogle Scholar
  22. Dall’ OG, Galante A, Torri M (2012) A methodology for the energy performance classification of residential building stock on an urban scale. Energ Buildings 48:211–219.  https://doi.org/10.1016/j.enbuild.2012.01.034CrossRefGoogle Scholar
  23. DesignBuilder (2018) DesignBuilder software Ltd. http://www.DesignBuilder.co.uk. Accessed 23 Aug 2018
  24. Ding Z, Wang G, Liu Z, Long R (2017) Research on differences in the factors influencing the energy-saving behavior of urban and rural residents in China–a case study of Jiangsu Province. Energy Policy 100:252–259CrossRefGoogle Scholar
  25. Dodds PE, Keppo I, Strachan N (2014) Characterising the evolution of energy system models using model archaeology. Environ Model Assess 20(2):83–102.  https://doi.org/10.1007/s10666-014-9417-3CrossRefGoogle Scholar
  26. DOE Department of Energy USA (2018) Energy plus. Available at: https://energyplus.net. Accessed 23 Aug 2018
  27. Dols WS, Emmerich SJ, Polidoro BJD (2016) Using coupled energy, airflow and indoor air quality software (TRNSYS/CONTAM) to evaluate building ventilation strategies. Build Serv Eng Res Technol 37(2):163–175CrossRefGoogle Scholar
  28. European Parliament (2012) The European Directive/27/EU of the European Parliament and Council of 25 October 2012 on energy efficiencyGoogle Scholar
  29. EUROSTAT (2008) Energy – yearly statistics 2008Google Scholar
  30. Feng W, Reisner A (2011) Factors influencing private and public environmental protection behaviors: results from a survey of residents in Shaanxi, China. J Environ Manag 92:429–436CrossRefGoogle Scholar
  31. Frandoloso MAL (2018) La inserción de la eficiencia energética en los edificios universitarios brasileños: las políticas y los procesos de toma de decisiones. Tese (Doutorado em Arquitetura, Energia e Meio Ambiente) – Escola Técnica Superior d’Arquitectura, Universitat Politècnica de Catalunya. Barcelona: UPC. Available at: http://www.tdx.cat/handle/10803/461416
  32. Frederiks ER, Stenner K, Hobman EV (2015) The socio-demographic and psychological predictors of residential energy consumption: a comprehensive review. Energies 8:573–609CrossRefGoogle Scholar
  33. Gao L, Wang S, Li J, Haidong L (2017) Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces. Resour Conserv Recycl 127:107–113.  https://doi.org/10.1016/j.resconrec.2017.08.030CrossRefGoogle Scholar
  34. Global Energy Statistical Yearbook (2018). Enerdata 2009. Available at: https://yearbook.enerdata.net/. Accessed 18 Aug 2018
  35. Groat L, Wang D (2002) Architectural research methods. Wiley, New YorkGoogle Scholar
  36. Hafezalkotob A (2018) Modelling intervention policies of government in price-energy saving competition of green supply chains. Comput Ind Eng 119:247–261.  https://doi.org/10.1016/j.cie.2018.03.031CrossRefGoogle Scholar
  37. Hensen JLM (2012) Evaluation through computational building performance simulation. In: Mallory-Hill S, Preiser WFE, Watson CG (eds) Enhancing building performance. Wiley-Blackwell, Oxford, pp 223–233Google Scholar
  38. Hensen JLM, Lamberts R (2011) Building performance simulation for design and operation. Spon Press, LondonGoogle Scholar
  39. Horschig T, Thrän D (2017) Are decisions well supported for the energy transition? A review on modeling approaches for renewable energy policy evaluation. Energ Sustain Soc 7(5).  https://doi.org/10.1186/s13705-017-0107-2
  40. Howard B, Parshall L, Thompson J, Hammer S, Dickinson J, Modi V (2012) Spatial distribution of urban building energy consumption by end use. Energ Buildings 45:141–151.  https://doi.org/10.1016/j.enbuild.2011.10.061CrossRefGoogle Scholar
  41. Ingle A, Moezzi M, Lutzenhiser L, Diamond R (2014) Better home energy audit modelling: incorporating inhabitant behaviors. Build Res Inf 42(4):409–421.  https://doi.org/10.1080/09613218.2014.890776CrossRefGoogle Scholar
  42. ISO 50001 (2011) International standard, energy management systems – requirements with guidance for use. Int Org Standard:2011Google Scholar
  43. Jovanovic B, Filipovic J (2016) ISO 50001 standard-based energy management maturity model – proposal and validation in industry. J Clean Prod 112:2744–2755.  https://doi.org/10.1016/j.jclepro.2015.10.023CrossRefGoogle Scholar
  44. Kanneganti H, Gopalakrishnan B, Crowe E, Al-Shebeeb O, Yelamanchi T, Nimbarte A, Currie K, Abolhassani A (2017) Specification of energy assessment methodologies to satisfy ISO 50001 energy management standard. Sustain Energ Technol Assess 23:121–135.  https://doi.org/10.1016/j.seta.2017.09.003CrossRefGoogle Scholar
  45. Kavgic M, Mavrogianni A, Mumovic D, Summerfield A, Stevanovic Z, Durovic-Petrovic M (2010) A review of bottom-up building stock models for energy consumption in the residential sector. Build Environ 45:1683–1697.  https://doi.org/10.1016/j.buildenv.2010.01.021CrossRefGoogle Scholar
  46. Kennedy MC, O’Hagan A (2010) Bayesian calibration of computer models. R Stat Soc 63(3):425–464MathSciNetCrossRefGoogle Scholar
  47. Khan S (2018) Preservice teachers, National Association for Research in Science Teaching, Atlanta, GAGoogle Scholar
  48. Kim D, Braun JE (2012) Reduced-order building modeling for application to model-based predictive control. Paper presented at the Fifth National conference of IBPSA-USA, Madison, 1–3 Aug 2012Google Scholar
  49. Kristensen M, Hedegaard R, Petersen S (2018) Hierarchical calibration of archetypes for urban building energy modeling. Energ Buildings 175:219–234.  https://doi.org/10.1016/j.enbuild.2018.07.030CrossRefGoogle Scholar
  50. Lagios K, Niemasz J, Reinhart CF (2010) Animated building performance simulation (ABPS) – linking Rhinoceros/Grasshopper with Radiance/Daysim. In: SimBuild 2010. Fourth National Conference of IBPSA-USA. New York City, New York, pp 321–327. Available at: http://www.ibpsa.us/pub/simbuild2010/papers/SB10-DOC-TS06A-03-Lagios.pdf
  51. Laouadi A (2004) Development of a radiant heating and cooling model for building energy simulation software. Build Environ 39:421–431.  https://doi.org/10.1016/j.buildenv.2003.09.016CrossRefGoogle Scholar
  52. Leal F, Costa R, Motevechi J, Almeida D, Marins F (2011) A practical guide for operational validation of discrete simulation models. Brazil Operat Res Soc 31(1):57–77.  https://doi.org/10.1590/S0101-74382011000100005CrossRefGoogle Scholar
  53. Madden TJ, Ellen PS, Ajzen I (1992) A comparison of the theory of planned behaviour and the theory of reasoned action. Personal Soc Psychol Bull 18:3–9CrossRefGoogle Scholar
  54. Mahdavi A, Ghiassi N, Vuckovic M, Taheri M, Tahmasebi F (2017) High-resolution representations of internal and external boundary conditions in urban energy modelling. Building simulation. Available at: http://www.ibpsa.org/proceedings/BS2017/BS2017_019.pdf. Accessed 5 Aug 2018
  55. Mallory-Hill SM (2004) Supporting strategic design of workplace environments with case-based reasoning. PhD thesis, University of Technology EidhovenGoogle Scholar
  56. Martinsson J, Lundqvist LJ, Sundström A (2011) Energy saving in Swedish households. The (relative) importance of environmental attitudes. Energy Policy 39:5182–5191CrossRefGoogle Scholar
  57. Mehrabian A, Russell JA (1974) An approach to environmental psychology. the MIT Press, Cambridge, MAGoogle Scholar
  58. Nagurney A, Yu M (2012) Sustainable fashion supply chain management under oligopolistic competition and brand differentiation. Int J Prod Econ 135:532–540CrossRefGoogle Scholar
  59. NRC (1997) Model national energy code for buildings. Institute for Research in Construction, National Research Council of Canada, OttawaGoogle Scholar
  60. Onwezen M, Antonides G, Bartels J (2013) The norm activation model: an exploration of the functions of anticipated pride and guilt in pro-environmental behaviour. J Econ Psychol 39:141–153.  https://doi.org/10.1016/j.joep.2013.07.005CrossRefGoogle Scholar
  61. Openmod Initiative (2016) Energypedia. https://wiki.openmod-initiative.org/wiki/Main_Page. Accessed 10 Aug 2018
  62. Perman R (2011) Natural resource and environmental economics, 4th edn. Pearson Addison Wesley, HarlowGoogle Scholar
  63. Pfenninger S, Hirth L, Schlecht I et al (2017) Opening the black box of energy modelling: strategies and lessons learned. Energ Strat Rev 19:63–71.  https://doi.org/10.1016/j.esr.2017.12.002CrossRefGoogle Scholar
  64. Poortinga W, Steg L, Vlek C, Wiersma G (2003) Household preferences for energy-saving measures: a conjoint analysis. J Econ Psychol 24:49–64CrossRefGoogle Scholar
  65. Reinhart CF, Cerezo Davila C (2016) Urban building energy modeling – a review of a nascent field. Build Environ 97:196–202.  https://doi.org/10.1016/j.buildenv.2015.12.001CrossRefGoogle Scholar
  66. Samouilidis J (1980) Energy modelling: a new challenge for management science. Omega 8(6):609–621.  https://doi.org/10.1016/0305-0483(80)90002-XCrossRefGoogle Scholar
  67. Schwartz SH (1977) Normative influence on altruism. In: Berkowitz L (ed) Advances in experimental social psychology, vol 10. Academic, New York, pp 221–279Google Scholar
  68. Shi Z, Fonseca JA, Schlueter A (2017) A review of simulation-based urban form generation and optimization for energy-driven urban design. Build Environ 121:119–129.  https://doi.org/10.1016/j.buildenv.2017.05.006CrossRefGoogle Scholar
  69. Swan LG, Ugursal VI (2009) Modeling of end-use energy consumption in the residential sector: a review or modeling techniques. Renew Sust Energ Rev 16:1819–1835.  https://doi.org/10.1016/j.rser.2008.09.033CrossRefGoogle Scholar
  70. Tang Z, Warkentin M, Wu L (2019) Understanding employees’ energy saving behaviour from the perspective of stimulus-organism-responses. Resour Conserv Recycl 140:216–223.  https://doi.org/10.1016/j.resconrec.2018.09.030CrossRefGoogle Scholar
  71. Tian Z, Love J, Tian W (2009) Applying quality control in building energy modelling: comparative simulation of a high performance building. J Build Perform Simul 2(3):163–178.  https://doi.org/10.1080/19401490902893757CrossRefGoogle Scholar
  72. TRNSYS (2018) Transient system simulation tool. Available at: http://www.trnsys.com/. Accessed 23 Aug 2018
  73. Villa-Arrieta M, Sumper A (2018) A model for an economic evaluation of energy systems using TRNSYS. Appl Energy 215:765–777.  https://doi.org/10.1016/j.apenergy.2018.02.045CrossRefGoogle Scholar
  74. Wang C, Nie P (2018) How rebound effects of efficiency improvement and price jump of energy influence energy consumption? J Clean Prod 202:497–503CrossRefGoogle Scholar
  75. Wesselink B, Harmsen R, Wolfgang E (2010) Energy savings 2020: how to triple the impact of energy saving policies in Europe – a contributing study to Roadmap 2050. ECF –European Climate Foundation, BerlinGoogle Scholar
  76. Wetter M (2011) A view on future building system modeling and simulation. In: Hensen, Lamberts (eds) Building performance simulation for design and operation. Routledge, MiltonGoogle Scholar
  77. Wiese F, Hilpert S, Kaldemeyer C, Plebmann G (2018) A qualitative evaluation approach for energy system modelling frameworks. Energ Sustain Soc 8:13.  https://doi.org/10.1186/s13705-018-0154-3CrossRefGoogle Scholar
  78. Yuan J, Nian V, Su B (2017) A meta model based Bayesian approach for building energy models calibration. Energy Procedia 143:161–166.  https://doi.org/10.1016/j.egypro.2017.12.665CrossRefGoogle Scholar
  79. Yue T, Long R, Chen H (2013) Factors influencing energy-saving behavior of urban households in Jiangsu Province. Energy Policy 62:665–667CrossRefGoogle Scholar
  80. Zekar A, El Khatib S (2018) Evelopment and assessment of simplified building representations under the context of an urban energy model: application to arid climate environment. Energ Buildings 173:461–469.  https://doi.org/10.1016/j.enbuild.2018.04.030CrossRefGoogle Scholar
  81. Zhang C, Yu B, Wang J, Wei Y (2018) Impact factors of household energy-saving behavior: an empirical study of Shandong Province in China. J Clean Prod 185:285–298.  https://doi.org/10.1016/j.jclepro.2018.02.303CrossRefGoogle Scholar
  82. Zhao HX, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energ Rev 16:3586–3592.  https://doi.org/10.1016/j.rser.2012.02.049CrossRefGoogle Scholar
  83. Zhang S, Worrell E, Crijns-Graus W, Wagner F, Cofala J (2014) Co-benefits of Energy Efficiency Improvement and Air Pollution Abatement in the Chinese Iron and Steel Industry. Available at: http://s3.amazonaws.com/academia.edu.documents/35462764/1-s2.0-S0360544214011670-main.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1498216923&Signature=EhrQSfF67wztnE0VpI3Og7fHA1Y%3D&response-contentdisposition=inline%3B%20filename%3DCo-benefits_of_energy_efficiency_improve.pdf

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Bianca Gasparetto Rebelatto
    • 1
    Email author
  • Marcos Antonio Leite Frandoloso
    • 1
  1. 1.University of Passo FundoPasso FundoBrazil

Section editors and affiliations

  • Matti Sommarberg
    • 1
  1. 1.Faculty of Management and BusinessTampere UniversityTampereFinland