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N. Ghiassi, A. Mahdavi:
"A Novel Approach to High-Resolution Urban-Level Building Energy Modeling";
Talk: Vienna Young Scientists Symposium 2016, Vienna; 2016-06-09 - 2016-06-10; in: "Proceedings of Vienna Young Scientists Symposium 2016", B. Ullmann, .. TU Wien et al. (ed.); Eigenverlag mit wissenschaftlichem Lektorat / TU Wien, (2016), ISBN: 978-3-9504017-2-1; Paper ID ARP15, 2 pages.

New energy supply and management paradigms such as distributed power and heat generation, and grid-independent urban clusters have highlighted the necessity for accurate and high-resolution building stock energy assessments. Such energy paradigms perceive the urban stock as an interconnected network of energy demand and supply nodes, allowing for more efficient allocation of resources and shared use of infrastructure, suited to the energy use patterns and sustainable energy harvest potentials of an assembly of buildings. For such planning, an overall understanding of the temporal and spatial distribution of energy demand and supply potential is essential. Realization of smart metering and large scale monitoring activities in near future are hindered by ectensive investement costs and associated data privacy issues. In the meantime, computation methods which estimate the energy performance of the urban building stock based on available information seem promising to bridge the information gap. Bottom-up engineering methods, EM, are considered to be highly effective due to their independence from historical data and their capability to capture unprecedented changes. These models rely on building level performance assessment methods for the estimation of the energy demand of individual buildings. The precision and resolution of such models depend on the underlying computational engines. Presently, dynamic performance simulation is the most elaborate building energy assessment method available, which enables detailed investigation of occupant behavior, building's physical properties and boundary conditions with sub-hourly temperoral resoultion. However, they depend upon substantial amounts of data on buildings and extensive computational resources, hampering their implementation in large-scale enquiries. The present contribution reports on an ongoing research effort, which facilitates the employment of performance simulation for urban energy computing, by reducing the computational domain through automated GIS-based building sampling. Representation of the urban building stock through sample buildings or archetypes is not a new venture. However, a review of some of the contemporary energy assessment methods and their adopted samling criteria revealed a frequent lack of explicitely stated arguments, evidence, or reasoning in support of the selected criteria. In most efforts, building are treated as isolated entities and their urban context is ignored.

(no german version available). New energy supply and management paradigms such as distributed power and heat generation, and grid-independent urban clusters have highlighted the necessity for accurate and high-resolution building stock energy assessments. Such energy paradigms perceive the urban stock as an interconnected network of energy demand and supply nodes, allowing for more efficient allocation of resources and shared use of infrastructure, suited to the energy use patterns and sustainable energy harvest potentials of an assembly of buildings. For such planning, an overall understanding of the temporal and spatial distribution of energy demand and supply potential is essential. Realization of smart metering and large scale monitoring activities in near future are hindered by ectensive investement costs and associated data privacy issues. In the meantime, computation methods which estimate the energy performance of the urban building stock based on available information seem promising to bridge the information gap. Bottom-up engineering methods, EM, are considered to be highly effective due to their independence from historical data and their capability to capture unprecedented changes. These models rely on building level performance assessment methods for the estimation of the energy demand of individual buildings. The precision and resolution of such models depend on the underlying computational engines. Presently, dynamic performance simulation is the most elaborate building energy assessment method available, which enables detailed investigation of occupant behavior, building's physical properties and boundary conditions with sub-hourly temperoral resoultion. However, they depend upon substantial amounts of data on buildings and extensive computational resources, hampering their implementation in large-scale enquiries. The present contribution reports on an ongoing research effort, which facilitates the employment of performance simulation for urban energy computing, by reducing the computational domain through automated GIS-based building sampling. Representation of the urban building stock through sample buildings or archetypes is not a new venture. However, a review of some of the contemporary energy assessment methods and their adopted samling criteria revealed a frequent lack of explicitely stated arguments, evidence, or reasoning in support of the selected criteria. In most efforts, building are treated as isolated entities and their urban context is ignored.