Skip to main content

Challenges in the Modeling and Simulation of Green Buildings

  • Living reference work entry
  • First Online:
Handbook of Energy Systems in Green Buildings

Abstract

Green buildings are environmentally bearable and economically viable buildings that are designed, constructed, and operated in order to minimize their environmental impact on the planet and maximize the quality of human life. Achieving a green building is hence a wide, complex, and ambitious challenge that requires close cooperation of all the stakeholders involved in the life cycle of the building, multidisciplinary competencies and field experience, as well as extensive computational skills. In this last regard, building performance simulation, which is a computer-based and multidisciplinary mathematical model of given aspects of building performance, is emerging as a promising support for designers and consultants. Unfortunately, although building performance simulation is renowned to be a powerful, comprehensive, flexible, and scalable tool, its use is not trivial, and, even today, modelers have to face several challenges for employing it to support the design and operation of green buildings. In this chapter, the main features of green buildings will be, first, mentioned. Next, typical mistakes, errors, and uncertainties that can spoil a building model will be presented. Then, a few modeling and simulation challenges – ranging from the model creation, through modeling under aleatory uncertainty, quality assurance, tool integration, simulation-based optimization, visualization and communication issues, to the selection of an appropriate tool – will be presented. Finally, a few final conclusions and future directions are drawn.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Similar content being viewed by others

References

  1. EPA (2009) Green buildings – basic information. Accessed 23 Mar 2017. http://www.epa.gov/greenbuilding/pubs/about.htm

  2. Nordby AS, Carlucci S (2014) Linee guide per l’implementazione di un processo di Progettazione integrata per edifici ad alte prestazioni energetiche ed ambientali. MaTrID Project

    Google Scholar 

  3. Hensen JLM, Lamberts R (2011) Building performance simulation for design and operation. Spon Press, Oxon

    Google Scholar 

  4. Attia S, Hensen JLM, Beltrán L, De Herde A (2012) Selection criteria for building performance simulation tools: contrasting architects’ and engineers’ needs. J Build Perform Simul 5(3):155–169

    Article  Google Scholar 

  5. Hopfe C, Struc C, Ulukavak Harputlugil G, Hensen J, Wilde P (2005) Exploration of using building performance simulation tools for conceptual building design. In: Proceedings IBPSANVL Conference, Delft, The Netherlands, 20 October 2005. pp 1–8. https://pure.tue.nl/ws/files/2222466/889858815005813.pdf

  6. Attia S, Hamdy M, O’Brien W, Carlucci S (2013) Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energ Buildings 60:110–124

    Article  Google Scholar 

  7. Marszal AJ, Heiselberg P, Bourrelle JS, Musall E, Voss K, Sartori I, Napolitano A (2011) Zero energy building – a review of definitions and calculation methodologies. Energ Buildings 43(4):971–979

    Article  Google Scholar 

  8. SHC Task 40, EBC Annex 52 (2015) Modeling, design, and optimization of net-zero energy buildings. Ernst & Sohn, Darmstadt

    Google Scholar 

  9. Lysen E (1996) The Trias Energetica: solar energy strategies for developing countries. In: Goetzberger A, Luther J (eds) EUROSUN conference. DGS Sonnenenergie Verlags-GmbH, Freiburg

    Google Scholar 

  10. Athienitis A, Torcellini P, Hirsch A, O’Brien W, Cellura M, Klein R, Delisle V, Attia S, Bourdoukan P, Carlucci S (2010) Design, optimization, and modelling issues of net-zero energy solar buildings. In: EuroSun 2010 – international conference on solar heating, cooling and buildings, Graz, Austria, pp 1–8

    Google Scholar 

  11. Carlucci S, Pagliano L (2013) An optimization procedure based on thermal discomfort minimization to support the design of comfortable net zero energy buildings. In: 13th IBPSA conference, BS 2013. International Building Performance Simulation Association, Chambery, France, pp 3690–3697

    Google Scholar 

  12. Carlucci S, Pagliano L, Zangheri P (2013) Optimization by discomfort minimization for designing a comfortable net zero energy building in the mediterranean climate. In: Chen Z, Guo L, Wu J (eds) Advanced materials research. Trans Tech Publications, Wuhan, pp 44–48

    Google Scholar 

  13. Carlucci S, Cattarin G, Causone F, Pagliano L (2015) Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II). Energ Buildings 104:378–394

    Article  Google Scholar 

  14. Carlucci S, Causone F, Pagliano L, Pietrobon M (2016) Zero-energy living lab. In: Littlewood JR, Spataru C, Howlett R (eds) Smart energy systems and buildings for a sustainable future. Springer-Verlag, Heidelberg

    Google Scholar 

  15. Causone F, Carlucci S, Pagliano L, Pietrobon M (2014) A zero energy concept building for the Mediterranean climate. Energy Procedia 62:280–288

    Article  Google Scholar 

  16. Pagliano L, Carlucci S, Toppi T, Zangheri P (2009) Passivhaus per il sud dell’Europa – Linee guida per la progettazione. Rockwool Italia, Milano

    Google Scholar 

  17. Selkowitz S, Aschehoug O, Lee ES, (2003) Advanced interactive facades – critical elements for future green buildings? GreenBuild 2003. US Green Building Council, Pittsburgh

    Google Scholar 

  18. Navarro L, de Gracia A, Colclough S, Browne M, McCormack SJ, Griffiths P, Cabeza LF (2016) Thermal energy storage in building integrated thermal systems: a review. Part 1. Active storage systems. Renew Energy 88:526–547

    Article  Google Scholar 

  19. Haase M, Andresen I (2007) Thermal mass concepts – state of the art. SINTEF, Trondheim

    Google Scholar 

  20. Rabenseifer R (2015) Teaching building performance simulation using generic simulation model. In: 14th conference of International Building Performance Simulation Association, BS 2015, December 7–9, 2015, International Building Performance Simulation Association, Hyderabad, India, pp 2720–2726

    Google Scholar 

  21. Oberkampf WL, DeLand SM, Rutherford BM, Diegert KV, Alvine KF (2002) Error and uncertainty in modeling and simulation. Reliab Eng Syst Saf 75:333–357

    Article  Google Scholar 

  22. Judkoff R, Wortman D, O'Doherty B, Burch J (2008) Methodology for validating building energy analysis simulations. National Renewable Energy Laboratory (NREL), Golden. p. Medium: ED; Size: 192 pp

    Book  Google Scholar 

  23. Trucano TG, Swiler LP, Igusa T, Oberkampf WL, Pilch M (2006) Calibration, validation, and sensitivity analysis: what’s what. Reliab Eng Syst Saf 91:1331–1357

    Article  Google Scholar 

  24. Schlosser J, Paredis CJJ (2007) Managing multiple sources of epistemic uncertainty in engineering decision making. SAE Technical Paper 2007-01-1481

    Google Scholar 

  25. Macdonald IA (2002) Quantifying the effects of uncertainty in building simulation. Department of Mechanical Engineering, University of Strathclyde. http://www.esru.strath.ac.uk/Documents/PhD/macdonald_thesis.pdf

  26. Hopfe CJ (2009) Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization. Eindhoven University of Technology, Eindhoven, p 215

    Google Scholar 

  27. Rastogi P (2016) On the sensitivity of buildings to climate – the interaction of weather and building envelopes in determining future building energy consumption. EPFL. https://infoscience.epfl.ch/record/220971/files/EPFL_TH6881.pdf

  28. Tian W, De Wilde P (2011) Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: a UK case study. Autom Constr 20:1096

    Article  Google Scholar 

  29. Azhar S, Carlton WA, Olsen D, Ahmad I (2011) Building information modeling for sustainable design and LEED® rating analysis. Autom Constr 20(2):217–224

    Article  Google Scholar 

  30. Foncubierta Blázquez JL, Rodríguez Maestre I, González Gallero FJ, Mena Baladés JD (2016) Reduction of computation time in building energy performance simulation programs by applying tearing techniques. Energ Buildings 130:667–675

    Article  Google Scholar 

  31. Schlesinger S (1979) Terminology for model credibility. Simulation 32(3):103–104

    Article  Google Scholar 

  32. Clarke JA (2001) Building simulation, energy simulation in building design. Elsevier BV, pp 64–98. https://books.google.no/books?hl=no&lr=&id=WH0VCiF8jkoC&oi=fnd&pg=PR3&dq=Clarke+JA+(2001)+Building+simulation,+energy+simulation+in+building+design.+Elsevier+BV,+pp+64%E2%80%9398&ots=c9XCc3untp&sig=tu78alSjw3sEn1SHu2ABI0HHc4I&redir_esc=y#v=onepage&q&f=false

  33. Attia S, Gratia E, De Herde A, Hensen JLM (2012) Simulation-based decision support tool for early stages of zero-energy building design. Energ Buildings 49:2–15

    Article  Google Scholar 

  34. Reeves T, Olbina S, Issa R (2015) Guidelines for using building information modeling for energy analysis of buildings. Buildings 5(4):1361–1388

    Article  Google Scholar 

  35. ASHRAE (2009) In: Owen MS (ed) ASHRAE handbook. fundamentals. American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Atlanta, p 3

    Google Scholar 

  36. Beccali G, Cellura M, Lo Brano M, Orioli A (2005) Is the transfer function method reliable in a EN building context? A theoretical analysis and a case study in the south of Italy. Appl Therm Eng 25:341–357

    Article  Google Scholar 

  37. Seem JE (1987) Modeling of heat in buildings, solar energy laboratory. University of Wisconsin Madison, Madison

    Google Scholar 

  38. Hottel HC, Sarofim AF (1967) Radiative transfer. McGraw Hill Book Company, New York

    Google Scholar 

  39. DOE (2011) EnergyPlus engineering reference: the reference to EnergyPlus calculations. U.S. Department of Energy (DOE). https://energyplus.net/sites/default/files/pdfs_v8.3.0/EngineeringReference.pdf

  40. Gebhart B (1971) Heat transfer. McGraw Hill Book Company, New York

    Google Scholar 

  41. Rickman SL (2015) Form factors, Grey bodies and radiation Conductances (Radks). Silver Spring, Mariland, pp 1–179

    Google Scholar 

  42. Hand JW (2011) The ESP-r cookbook: strategies for deploying virtual representations of the built environment. Energy Systems Research Unit, Department of Mechanical Engineering, University of Strathclyde, Glasgow

    Google Scholar 

  43. Clark JA (2001) Energy simulation in building design. Routledge, London

    Google Scholar 

  44. Underwood CP, Yik FWH (2004) Modelling methods for energy in buildings. Blackwell Publishing Ltd, Oxford

    Book  Google Scholar 

  45. AIAA (1998) Guide for the verification and validation of computational fluid dynamics simulations. American Institute of Aeronautics and Astronautics, Reston

    Google Scholar 

  46. ASHRAE (2002) ASHRAE guideline 14 – measurement of energy and demand savings. American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Atlanta

    Google Scholar 

  47. Hensen JLM, Radošević M (2004) Some quality assurance issues and experiences in teaching building performance simulation. IBPSA News 14(2):22–33

    Google Scholar 

  48. NCDC (1976) Test reference year (TRY), Tape reference manual, TD-9706. National Climatic Data Center, U.S. Department of Commerce, Asheville, North Carolina

    Google Scholar 

  49. Crawley DB, Lawrie LK (2015) Rethinking the TMY: is the ‘typical’ meteorological year best for building performance simulation? 14th Conference of internatonal Building Performance Simulation Association, Hyderabad, India. http://www.ibpsa.org/proceedings/BS2015/p2707.pdf

  50. Crawley DB (1998) Which weather data should you use for energy simulations of commercial buildings? In: ASHRAE (ed) ASHRAE transactions. ASHRAE, Atlanta, pp 498–515

    Google Scholar 

  51. US-DoE (2016) Weather Data. https://energyplus.net/weather. Accessed 5 July 2016

  52. Erba S, Causone F, Armani R (2017) The effect of weather datasets on building energy simulation outputs. Energy Procedia 134(Supplement C):545–554

    Article  Google Scholar 

  53. Luterbacher J, Dietrich D, Xoplaki E, Grosjean M, Wanner H (2004) European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303:1499–1503

    Article  Google Scholar 

  54. Wilby RL (2007) A review of climate change impacts on the built environment. Built Environ (1978–) 33(1):31–45

    Article  Google Scholar 

  55. de Wilde P, Coley D (2012) The implications of a changing climate for buildings. Build Environ 55:1–7

    Article  Google Scholar 

  56. Herrera M, Natarajan S, Coley DA, Kershaw T, Ramallo-González AP, Eames M, Fosas D, Wood M (2017) A review of current and future weather data for building simulation. Build Serv Eng Res Technol 38(5):602–627

    Article  Google Scholar 

  57. Belcher SE, Hacker JN, Powell DS (2005) Constructing design weather data for future climates. Build Serv Eng Res Technol 26(1):49–61

    Article  Google Scholar 

  58. Pagliano L, Carlucci S, Causone F, Moazami A, Cattarin G (2016) Energy retrofit for a climate resilient child care centre. Energ Buildings 127:1117–1132

    Article  Google Scholar 

  59. De Dear R (2006) Adapting buildings to a changing climate: but what about the occupants? Build Res Inf 34(1):78–81

    Article  Google Scholar 

  60. Arup (2013/14) Ove Arup Official Web Page http://www.arup.com/Projects/SPeAR.aspx. May 2015

  61. Arup, Argos Analytics, SPD (2017) WeatherShift. http://www.weather-shift.com/. Accessed 17 Dec 2017

  62. Moazami A, Carlucci S, Geving S (2017) Critical analysis of software tools aimed at generating future weather Files with a view to their use in building performance simulation, Nordic symposium on building physics, Trondheim, Norway

    Google Scholar 

  63. Coley D, Herrera M, Fosas D, Liu C, Vellei M (2017) Probabilistic adaptive thermal comfort for resilient design. Build Environ 123(Supplement C):109–118

    Article  Google Scholar 

  64. Nik VM (2016) Making energy simulation easier for future climate – synthesizing typical and extreme weather data sets out of regional climate models (RCMs). Appl Energy 177:204–226

    Article  Google Scholar 

  65. O’Brien W, Gaetani I, Gilani S, Carlucci S, Hoes PJ, Hensen JLM (2016) International survey on current occupant modelling approaches in building performance simulation. J Build Perfor Simul 10:653

    Article  Google Scholar 

  66. O’Brien W (2013) Occupant-proof buildings: Can we design buildings that are robust against occupant behaviour? In: 13th conference of the International Building Performance Simulation Association, BS 2013, August 26– 28, 2013. International Building Performance Simulation Association, Chambery, France, pp 1746–1754

    Google Scholar 

  67. Clevenger CM, Haymaker J (2006) The impact of the building occupant on energy modeling simulations. Citeseer, Joint International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada. https://www.annex66.org/?q=node/100

  68. Yan D, O’Brien W, Hong T, Feng X, Burak Gunay H, Tahmasebi F, Mahdavi A (2015) Occupant behavior modeling for building performance simulation: current state and future challenges. Energ Buildings 107:264–278

    Article  Google Scholar 

  69. IEA EBC Annex 66 (2018) Exploring occupant behavior in buildings. Springer International Publishing AG, Cham

    Google Scholar 

  70. Attia S, Hamdy M, O’Brien W, Carlucci S (2013) Computational optimisation for zero energy buildings design: interviews results with twenty eight international experts. In: Proceedings of the 13th international conference of the IBPSA, pp 978–984

    Google Scholar 

  71. Hamdy M (2012) Combining simulation and optimisation for dimensioning optimal building envelopes and HVAC systems. Aalto University, Espoo

    Google Scholar 

  72. Hayter S, Torcellini P, Hayter RB, Judkoff R (2000) The energy design process for designing and constructing high-performance buildings. Citeseer, Clima 2000/Napoli 2001 World Congress – Napoli (I):15–18 September 2001. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.201.9355&rep=rep1&type=pdf

  73. O’Brien W (2010) Design, optimization, and modeling issues of net-zero energy solar buildings. In: Proceedings of the EuroSun 2010 conference, International Solar Energy Society (ISES)

    Google Scholar 

  74. Rysanek AM, Choudhary R (2012) A decoupled whole-building simulation engine for rapid exhaustive search of low-carbon and low-energy building refurbishment options. Build Environ 50:21–33

    Article  Google Scholar 

  75. Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP (2015) A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance. Energ Buildings 88:78–90

    Article  Google Scholar 

  76. Attia S, Hamdy M, Carlucci S, Pagliano L, Bucking S, Hasan A (2015) Building performance optimization of net zero-energy buildings. In: Athienitis A, O’Brien W (eds) Modeling, design, and optimization of net-zero energy buildings. Wilhelm Ernst & Sohn, Hoboken, pp 175–206

    Google Scholar 

  77. Hamdy M, Nguyen A-T, Hensen JLM (2016) A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems. Energ Buildings 121:57–71

    Article  Google Scholar 

  78. Machairas V, Tsangrassoulis A, Axarli K (2014) Algorithms for optimization of building design: a review. Renew Sust Energ Rev 31:101–112

    Article  Google Scholar 

  79. Park S, Norrefeldt V, Stratbucker S, Jang YS, Grun G (2013) Methodological approach for calibration of building energy performance simulation models applied to a common “measurement and verification” process. Bauphysik 35(4):235–241

    Article  Google Scholar 

  80. Tenne Y (2012) A computational intelligence algorithm for expensive engineering optimization problems. Eng Appl Artif Intell 25(5):1009–1021

    Article  Google Scholar 

  81. Hamdy M, Sirén K (2015) A multi-aid optimization scheme for large-scale investigation of cost-optimality and energy performance of buildings. J Build Perform Simul 9(4):411–430

    Article  Google Scholar 

  82. Christensen C, Horowitz S, Givler T, Courtney A (2005) BEopt: software for identifying optimal building designs on the path to zero net energy, ISES 2005 Solar World Congress, Orlando, Florida

    Google Scholar 

  83. York D, Cappiello C (1981) In: Lawrence Berkeley National Laboratory (ed) DOE-2 reference manual, (version 2.1A), Berkeley

    Google Scholar 

  84. Klein SA, Beckman WA, Mitchell JW, Duffie JA, Duffie NA, Freeman TL, Mitchell JC, et al. (1996) TRNSYS: a transient system simulation program – reference manual. Solar Energy Laboratory, University of Wisconsin, Madison

    Google Scholar 

  85. Christensen C, Barker G, Horowitz S (2004) A sequential search technique for identifying optimal building designs on the path to zero net energy. Solar 2004 conference, Boulder, CO, pp 877–882

    Google Scholar 

  86. O’Brien W, Athienitis A, Kesik T (2011) Parametric analysis to support the integrated design and performance modelling of net-zero energy houses. ASHRAE Trans 117(1)

    Google Scholar 

  87. Eastman CM (2011) BIM handbook: a guide to building information modeling for owners,managers, designers, engineers and contractors, 2nd edn. Wiley, Hoboken

    Google Scholar 

  88. GSA (2015) BIM Guide 05 – energy performance. In: The National 3D-4D BIM Program U.S. General Services Administration, Washington, DC. https://www.gsa.gov/cdnstatic/GSA_BIM_Guide_05_Version_2.1.pdf

  89. Kim H, Anderson K (2013) Energy modeling system using building information modeling open standards. J Comput Civ Eng 27(3):203–211

    Article  Google Scholar 

  90. Remmen P, Cao J, Ebertshäuser S, Frisch J, Lauster M, Maile T, O’Donnell J, Pinheiro S, Rädler J, Streblow R (2015) An open framework for integrated BIM-based building performance simulation using Modelica. In: Proceedings of the 14th IBPSA conference

    Google Scholar 

  91. Wimmer R, Cao J, Remmen P, Maile T, O’Donnel J, Frisch J, Streblow R, Müller D, van Treeck C (2015) Implementation of advanced BIM-based mapping rules for automated conversion to Modelica. In: Proceedings of the 14th IBPSA conference

    Google Scholar 

  92. Weytjens L, Attia S, Verbeeck G, De Herde A (2011) The ‘architect-friendliness’ of six building performance simulation tools: a comparative study. Int J Sustain Build Technol Urban Dev 2(3):237–244

    Article  Google Scholar 

  93. Baba A, Mahdjoubi L, Olomolaiye P, Booth C (2013) State-of-the-art on buildings performance energy simulations tools for architects to deliver low impact building (LIB) in the UK. Int J Dev Sustain 2(3):1867–1884

    Google Scholar 

  94. Bambardekar S, Poerschke U (2009) The architect as performer of energy simulation in the early design stage. Eleventh international IBPSA conference, Glasgow

    Google Scholar 

  95. Attia S, Beltran L, Herde Ad, Hensen J (2009) “Architect friendly”: a comparison of ten different building performance simulation tools. Eleventh international IBPSA conference, Glasgow, pp 204–211

    Google Scholar 

  96. Lapinskiene V, Martinaitis V (2013) The framework of an optimization model for building envelope. Procedia Eng 57:670–677

    Article  Google Scholar 

  97. Wong NH (2015) Grand challenges in sustainable design and construction. Front Built Environ 1(22):1–3

    Google Scholar 

  98. Clarke JA, Hensen JLM (2015) Integrated building performance simulation: progress, prospects and requirements. Build Environ 91:294–306

    Article  Google Scholar 

  99. Wetter M, van Treeck C, Hensen J (2013) New generation computational tools for building and community energy systems. Energy in Buildings and Communities Programme. IEA EBC Annex 60

    Google Scholar 

  100. Caldas L (2006) GENE_ARCH: An evolution-based generative design system for sustainable architecture. Intelligent Computing in Engineering and Architecture Lecture Notes in Computer Science 4200:109–118

    Google Scholar 

  101. jEPlus + EA (2011) jEPlus + EA user’s guide (v1.4). http://www.jeplus.org/wiki/doku.php?id%20=%20docs:jeplus_ea:start

  102. Zhang Y, Tindale A, Garcia AO, Korolija I, Tresidder E, Passarelli M, Gale P (2013) How to integrate optimization into building design practice: lessons learnt from a design optimization competition. 13th conference of international building performance simulation association, Chambery, France, August 26–28. http://www.ibpsa.org/proceedings/BS2013/p_1462.pdf

  103. Socolow RH (1978) The twin rivers program on energy conservation in housing: highlights and conclusions. Energy and Buildings, Vol. 1, 207–242

    Google Scholar 

  104. Seligman C, Darley JM, Becker LJ (1978) Behavioral approaches to residential energy conservation, Energy and Buildings 1(3):325–337

    Google Scholar 

  105. Baker N, Steemers K (2000) Energy and environment in architecture: a technical design guide, E & FN Spon, London

    Google Scholar 

  106. Emery AF, Kippenhan CJ (2006) A long term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards. Energy and Buildings 31(5):677–693

    Google Scholar 

  107. Andersen RK (2012) The influence of occupants’ behaviour on energy consumption investigated in 290 identical dwellings and 35 apartments. In: Proceedings of Healthy Buildings 2012, Brisbane, Australia

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Salvatore Carlucci .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer-Verlag GmbH Germany

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Carlucci, S., Hamdy, M., Moazami, A. (2018). Challenges in the Modeling and Simulation of Green Buildings. In: Wang, R., Zhai, X. (eds) Handbook of Energy Systems in Green Buildings. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49088-4_50-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-49088-4_50-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49088-4

  • Online ISBN: 978-3-662-49088-4

  • eBook Packages: Springer Reference EnergyReference Module Computer Science and Engineering

Publish with us

Policies and ethics