Energy planning tools and CityGML-based 3D virtual city models: experiences from Trento (Italy)

被引:35
作者
Agugiaro G. [1 ]
机构
[1] Energy Department, Sustainable Buildings and Cities Unit, Austrian Institute of Technology, Giefinggasse 2, Vienna
关键词
3D virtual city models; CityGML; Data integration; Energy performance of buildings;
D O I
10.1007/s12518-015-0163-2
中图分类号
学科分类号
摘要
This article presents the first results concerning the development and implementation of a tool for the estimation of the energy performance for residential buildings at city scale. Space heating and domestic hot water production are taken into account. Project “EnerCity” focuses on two main topics: (a) the creation of a CityGML-compliant 3D city model from sub-optimal datasets and (b) its adoption as information hub to develop energy-related assessment tools. A part of the city of Trento, in northern Italy, was chosen as the case study area for testing purposes; however, the methodology was developed to be extended to the whole city. Only publicly available data were used. The energy demand calculation method is based on the Italian Technical Specifications UNI/TS 11300:2008. For each building, the primary energy demand for space heating and domestic hot water production, as well as the resulting energy performance index, are estimated. In order to characterise the buildings, heterogeneous datasets (cadastral data, statistical data, etc.) were harmonised and integrated. All residential buildings were successively classified into distinct building types according to the criteria defined for Italy in the European project “Tabula”. The developed tool allows for data visualisation, editing as well as interactive refurbishment of the buildings. The article describes all relevant steps of the project and discusses possible enhancements and the future improvements. © 2015, Società Italiana di Fotogrammetria e Topografia (SIFET).
引用
收藏
页码:41 / 56
页数:15
相关论文
共 41 条
[31]  
Prandi F., De Amicis R., Piffer S., Soave M., Cadzow S., Gonzalez Boix E., D'Hont E., Using CityGML to deploy smart-city services for urban ecosystems. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-4, W1, pp. 87-92, (2013)
[32]  
Prandi F., Soave M., Devigili F., Andreolli M., De Amicis R., Services oriented smart city platform based on 3D city model visualization, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-4, pp. 59-64, (2014)
[33]  
Ratti C., Baker N., Steemers K., Energy consumption and urban texture, Energy and Buildings, 37, 7, pp. 762-776, (2005)
[34]  
Remondino F., Poli D., Back to the future: Il ritorno della fotogrammetria, GEOmedia, 2014, 2, pp. 6-8, (2014)
[35]  
Resch B., Sagl G., Tornros T., Bachmaier A., Eggers J.-B., Herkel S., Narmsara S., Gundra H., GIS-based planning and modeling for renewable energy: challenges and future research avenues, ISPRS International Journal of Geo-Information, 3, 2, pp. 662-692, (2014)
[36]  
Robinson D., Campbell N., Gaiser W., Kabel K., Le-Mouel A., Morel N., Page J., Stankovic S., Stone A., Suntool—a new modelling paradigm for simulating and optimising urban sustainability, Solar Energy, 81, 9, pp. 1196-1211, (2007)
[37]  
Salat S., Energy loads, CO<sub>2</sub> emissions and building stocks: morphologies, typologies, energy systems and behaviour, Building Research & Information, 37, 56, pp. 598-609, (2009)
[38]  
Schrenk M., Wasserburger W.W., Music B., Dorrzapf L., SUNSHINE: Smart UrbaN ServIces for Higher eNergy Efficiency, pp. 18-24, (2013)
[39]  
Stadler A., Kolbe T.H., Spatio-semantic coherence in the integration of 3D city models, (2007)
[40]  
Strzalka A., Bogdahn J., Coors V., Eicker U., 3D city modeling for urban scale heating energy demand forecasting, HVAC&R Research, 17, 4, pp. 526-539, (2011)