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N. Ghiassi, A. Mahdavi:
"A GIS-based Framework for Semi-automated Urban-Scale energy Simulation";
Talk: Central Europe towards Sustainable Building 2016 - Innovations for sustainable Future, Prague, Czech Republic; 2016-06-22 - 2016-06-24; in: "(Printed) Proceedings of CESB 2016: Central Europe towards sustainable building 2016 - innovations for sustrainable future", P. Hàjek, J. Tywoniak, A. Lupisek (ed.); Prague, Czech Republic (2016), ISBN: 9788027102488; Paper ID p.529-536, 8 pages.

New urban energy management paradigms such as distributed generation and flexible energy grids have highlighted the necessity for accurate and high-resolution building-stock energy assessments. Although simplified energy classification schemes have provided an overview of the energy behavior of building agglomerations, detailed assessments portraying realistic and dynamic energy use patterns with high temporal and geographic resolution are lacking. Available large data scale data, including statistical information and GIS data, is not sufficient for such elaborate assessments, yet accumulation and processing of more detailed information for large assemblies of buildings is not trivial.
The present research focuses on bridging the gap between the low resolution of the openly accessible urban data and the informational requirements of dynamic building simulation through application of cluster analysis methods and sampling, based on energy-related building characteristics. This approach can be expected to substantially reduce the computational resources.

(keine deutsche Version) New urban energy management paradigms such as distributed generation and flexible energy grids have highlighted the necessity for accurate and high-resolution building-stock energy assessments. Although simplified energy classification schemes have provided an overview of the energy behavior of building agglomerations, detailed assessments portraying realistic and dynamic energy use patterns with high temporal and geographic resolution are lacking. Available large data scale data, including statistical information and GIS data, is not sufficient for such elaborate assessments, yet accumulation and processing of more detailed information for large assemblies of buildings is not trivial.
The present research focuses on bridging the gap between the low resolution of the openly accessible urban data and the informational requirements of dynamic building simulation through application of cluster analysis methods and sampling, based on energy-related building characteristics. This approach can be expected to substantially reduce the computational resources.

Urban energy model, Cluster analysis, GIS, Building performance simulation