Abstract
This paper illustrates the preliminary results of a research project focused on the development of a Web 2.0 system designed to compute and visualize large-scale building energy performance maps, using: emerging platform-independent technologies such as WebGL for data presentation, an extended version of the EU-Founded project TABULA/EPISCOPE for automatic calculation of building energy parameters and CityGML OGC standard as data container. The proposed architecture will allow citizens, public administrations and government agencies to perform city-wide analyses on the energy performance of building stocks.
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References
Apache Community, Apache Tomcat. http://tomcat.apache.org/
Ballarini, I., et al.: Definition of building typologies for energy investigations on residential sector by TABULA IEE-project: application to Italian case studies. In: Proceedings of the 12th International Conference on Air Distribution in Rooms, Trondheim, Norway, pp. 19–22 (2012)
Bowerman, B., et al.: The vision of a smart city. 2nd International Life Extension Technology Workshop, Paris (2000)
Carrión, D., et al.: Estimation of the energetic rehabilitation state of buildings for the city of Berlin using a 3D city model represented in CityGML. In: ISPRS International Conference on 3D Geoinformation, p. 4 (2010)
Dalla Costa, S., et al.: A CityGML 3D geodatabase for buildings’ energy efficiency. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38.4: C21 (2011)
Fenger, J.: Urban air quality. Atmos. Environ. 33(29), 4877–4900 (1999)
Giffinger, R.: Smart cities: Ranking of European medium-sized cities. Final report, Centre of Regional Science, Vienna UT (2007)
Giffinger, R., Gudrun, H.: Smart cities ranking: an effective instrument for the positioning of the cities? ACE: Archit. City Environ. 4(12), 7–26 (2010)
Gröger, G., et al.: OpenGIS city geography markup language (CityGML) encoding standard. Open Geospatial Consortium Inc. (2008)
Hay, G., et al.: HEAT - Home energy assessment technologies: a web 2.0 residential waste heat analysis using geobia and airborne thermal imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVIII-4/C7 (2010)
Kaden, R., Kolbe T.: City-wide total energy demand estimation of buildings using semantic 3D city models and statistical data. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. II-2/W1 (2013)
Krüger, A., Kolbe, T.: Building Analysis for urban energy planning using key indicators on virtual 3D city models - The energy atlas of Berlin. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B2 (2012)
Loga, T.: Use of Building Typologies for Energy Performance Assessment of National Building Stocks: Existent Experiences in European Countries and Common Approach. IWU (2010)
Marrin, C.: WebGL specification. Khronos WebGL Working Group (2011)
Nouvel, R., et al.: CityGML-based 3D city model for energy diagnostics and urban energy policy support. In: Proceedings of BS2013, 13th Conference of International Building Performance Simulation Association, Chambéry, France (2013)
Special Interest Group 3D. http://www.sig3d.org/index.php?&language=en
Stadler, A., et al.: Making interoperability persistent: a 3D geo database based on CityGML. In: Lee, J., Zlatanova, S. (eds.) 3D Geo-Information Sciences, pp. 175–192. Springer, Heidelberg (2009)
Washburn, D., Sindhu, U.: Helping CIOs Understand “Smart City” Initiatives. Report by Forrester Research, Inc. (2009)
Wilson, T.: OGC® KML. OGC Encoding Standard, Version 2.0 (2008)
Acknowledgements
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.
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di Staso, U., Giovannini, L., Berti, M., Prandi, F., Cipriano, P., De Amicis, R. (2015). Large-Scale Residential Energy Maps: Estimation, Validation and Visualization Project SUNSHINE - Smart Urban Services for Higher Energy Efficiency. In: Helfert, M., Holzinger, A., Belo, O., Francalanci, C. (eds) Data Management Technologies and Applications. DATA 2014. Communications in Computer and Information Science, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-319-25936-9_3
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DOI: https://doi.org/10.1007/978-3-319-25936-9_3
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