ABSTRACT
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.
