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N. Ghiassi, A. Mahdavi:
"Utilization of GIS data for urban-scale energy inquiries: A sampling approach";
Talk: ECPPM 2016 eWork and eBusiness in Architecture, Engineering and Construction, Limassol, Cyprus; 2016-09-07 - 2016-09-09; in: "Proceedings of the 11th European Conference on Product and Process Modelling", S.E. Christodoulou, R.J. Scherer (ed.); Balkema, (2016), ISBN: 9781138032804; 251 - 258.

Over the past years, new energy supply and management paradigms, such as distributed power
and heat generation, have highlighted the significance of urban-scale energy assessments. The present
contribution briefly presents an ongoing research effort towards development of a bottom-up simulation
supported urban energy model for the hourly estimation of heating demand in the city of Vienna, Austria.
The presented research project adopts a sampling approach towards high-resolution urban energy modeling
and employs a well-known data mining method, Multivariate Cluster Analysis, to select representative
buildings based on energy-related building characteristics. The selected sample is subjected to detailed
performance assessments, the results of which are up-scaled to obtain the overall energy profile of the
neighborhood. Focusing on the data-related challenges of urban energy modeling, the paper describes the
informational requirements for the adopted approach, and elaborates on the underlying data structure and
the data processing methods developed to overcome the encountered challenges.

[no german abstract available] Over the past years, new energy supply and management paradigms, such as distributed power
and heat generation, have highlighted the significance of urban-scale energy assessments. The present
contribution briefly presents an ongoing research effort towards development of a bottom-up simulation
supported urban energy model for the hourly estimation of heating demand in the city of Vienna, Austria.
The presented research project adopts a sampling approach towards high-resolution urban energy modeling
and employs a well-known data mining method, Multivariate Cluster Analysis, to select representative
buildings based on energy-related building characteristics. The selected sample is subjected to detailed
performance assessments, the results of which are up-scaled to obtain the overall energy profile of the
neighborhood. Focusing on the data-related challenges of urban energy modeling, the paper describes the
informational requirements for the adopted approach, and elaborates on the underlying data structure and
the data processing methods developed to overcome the encountered challenges.

http://publik.tuwien.ac.at/files/PubDat_251063.pdf