Hybrid Approach for Large-scale Energy Performance Estimation Based on 3D City Model Data and Typological Classification

  • Federico Prandi
  • Umberto Di StasoEmail author
  • Marco Berti
  • Luca Giovannini
  • Piergiorgio Cipriano
  • Raffaele De Amicis
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


This paper illustrates the results of a research project focused on the development of a Web 2.0 system designed to compute and visualize building energy performance large-scale maps. The workflow and the framework include: emerging platform-independent technologies such as WebGL for data presentation, an extended version of the EU-Founded project TABULA/EPISCOPE for building energy parameters estimation and a data model based on CityGML OGC standard. The proposed platform will allow citizens, public administrations and government agencies to perform city-wide analyses on the energy performance of building stocks. To evaluate the accuracy of the model, the simulation results were compared to real data of energy performance of the energy certificates available and the model uncertainties were analyzed.


CityGML WebGL Energy maps TABULA EPISCOPE Web 2.0 Geovisual analytics 



The project SUNSHINE has received funding from the EC, and it has been co-funded by the CIP-Pilot actions as part of the Competitiveness and innovation Framework Programme. The authors are solely responsible of this work, which does not represent the opinion of the EC. The EC is not responsible for any use that might be made of information contained in this paper.


  1. Ballarini I, Corgnati SP, Corrado V, Talà N et al (2012) Definition of building typologies for energy investigations on residential sector by TABULA IEE-project: application to Italian case studies. Proceedings of the 12th International Conference on Air Distribution in Rooms, Trondheim, Norway, p 19–22Google Scholar
  2. Bowerman B, Braverman J, Taylor J, Todosow H, Von Wimmersperg U (2000) The vision of a smart city. 2nd International Life Extension Technology Workshop, ParisGoogle Scholar
  3. Carrión D, Lorenz A, Kolbe TH (2010) Estimation of the energetic rehabilitation state of buildings for the city of Berlin using a 3D city model represented in CityGML. ISPRS International Conference on 3D Geoinformation, p 4Google Scholar
  4. Fenger J (1999) Urban air quality. Atmos Environ 33(29):4877–4900CrossRefGoogle Scholar
  5. Giffinger R (2007) Smart cities: ranking of European medium-sized cities. Final report, Centre of Regional Science, Vienna UTGoogle Scholar
  6. Giffinger R, Gudrun H (2010) Smart cities ranking: an effective instrument for the positioning of the cities? ACE Archit City Environ 4(12):7–26Google Scholar
  7. Giovannini L, Pezzi S, Di Staso U, Prandi F, (2014) Large-scale assessment and visualization of the energy performance of buildings with ecomaps. Proceedings of DATA 2014, Wien, AustriaGoogle Scholar
  8. Groeger G, Kolbe TH, Nagel C, Häfele KH (2012) OGC city geography markup language (CityGML) en-coding standard. OGC Doc. No. OGC 12-019Google Scholar
  9. Heiple S, Sailor DJ (2008) Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles. Energy Build 40(8):1426–1436CrossRefGoogle Scholar
  10. Kaden R, Kolbe T (2013) City-wide total energy demand estimation of buildings using semantic 3D city models and statistical data. ISPRS Ann Photogrammetry Remote Sens Spat Inf Sci 2:W1Google Scholar
  11. Marrin C (2011) WebGL specification. Khronos WebGL Working GroupGoogle Scholar
  12. Nouvel R, Schulte C, Eicker U, Pietruschka D, Coors V (2013a) CityGML-based 3D city model for energy diagnostics and urban energy policy support. Proceedings of BS2013, 13th Conference of International Building Performance Simulation Association, Chambéry, FranceGoogle Scholar
  13. Nouvel R, Zirak M, Dastageeri H, Coors V, Eicker U (eds) (2013b) Urban energy analaysis based on 3D city model for national scale applications. IBPSA Germany Conference. RWTH Aachen, Sept 2013Google Scholar
  14. Nouvel R et al (2014) Urban energy analysis based on 3D city model for national scale applications. IBPSA Germany Conference. Vol 8.Google Scholar
  15. Nouvel R, Kaden R, Bahu J-M, Kaempf J, Cipriano P, Lauster M, Benner J, Munoz E, Tournaire O, Casper E (2015) Genesis of the citygml energy ADE. CISBAT 2015, Lausanne, Switzerland, 9–11 Sept 2015Google Scholar
  16. Prandi F, Soave M, Devigili F, Andreolli M, De Amicis R (2014) Services oriented smart city platform based on 3d city model visualization. ISPRS Ann Photogrammetry Remote Sens Spat Inf Sci 1:59–64CrossRefGoogle Scholar
  17. Soave M, Devigili F, Prandi F, De Amicis R (2013) Visualization and analysis of CityGML dataset within a client sever infrastructure. In: Proceedings of the 18th International Conference on 3D Web Technology (Web3D ’13). ACM, New York, NY, USA, p 215–215. doi: 10.1145/2466533.2466573,
  18. Special Interest Group 3D (2014)
  19. Strzalka A, Bogdahn J, Coors V, Eicker U (2011) 3D city modelling for urban scale heating energy demand forecasting. HVAC&R Res 17(4):526–539Google Scholar
  20. SUNSHINE Project, Smart UrbaN ServIces for Higher eNergy Efficiency (2013–2016) Website:, Jan 2015
  21. Washburn D, Sindhu U (2009) Helping CIOs understand “smart city” initiatives. Report by Forrester Research, IncGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Federico Prandi
    • 1
  • Umberto Di Staso
    • 1
    Email author
  • Marco Berti
    • 1
  • Luca Giovannini
    • 2
  • Piergiorgio Cipriano
    • 2
  • Raffaele De Amicis
    • 1
  1. 1.Fondazione GraphitechTrentoItaly
  2. 2.Sinergis SrlCasalecchio di RenoItaly

Personalised recommendations