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A. Mahdavi, N. Ghiassi:
"Advanced urban energy computing: from visions to solutions";
Keynote Lecture: SET2017 16th International Conference on Sustainable Energy Technologies, Bologna, Italy (invited); 2017-07-17 - 2017-07-20; in: "SET2017", S. Riffat, E. Zanchini (ed.); Alma Mater Studiorum University Di Bologna, (2017).

Recently, the interest in urban‐scale modelling of energy transfer phenomena has markedly
increased. This may be explained via multiple contributing factors. The steadily rising prominence of cities as humanity's habitant is one. Another factor pertains to the realisation of cities as highly nested constituents with complex behavioural traits - also with regard to the dynamics of energy generation, distribution, storage, and use. As with other complex problems, computational modelling of the pertinent processes can provide
effective support for both systems analysis and decision making. Specifically, the conception and evaluation of energy performance improvement strategies for the urban environment requires reliable predictions of the spatial and temporal distribution of energy
demand and supply. This implies the need for versatile modelling environments that can facilitate spatiotemporally detailed energy‐related district‐level inquiries, pertaining, for
example, to candidate energy efficiency measures.
In this context, the present keynote presents the structure and elements of a novel multifaceted urban energy computing environment, which is intended to concurrently
accommodate the following three essential requirements: i) Realistic representation of the dynamics and diversity of urban‐level microclimatic boundary conditions requirement; ii)
Realistic representation of internal (occupancy‐related) processes in buildings; iii) Highresolution numeric simulation of underlying transient heat transfer phenomena. To satisfy
these requirements in a computationally feasible manner, a specific hourglass paradigm is deployed. Thereby, a reduced representative sample of buildings in the pertinent urban
analysis domain is generated. This reductive step allows to use, instead of simplified and reduced order algorithms, detailed (high‐resolution, transient) numeric modelling, while
circumventing the problem of massive data requirements and extensive computational loads. The reductive step, however, inadvertently results in some loss of complexity and
diversity. The proposed hourglass paradigm thus involves a second step toward targeted recovery of lost diversity via probabilistically based variation of multiple input data sets for
extensive parametric simulations. As such, the resulting simulation‐assisted urban energy decision support environment facilitates the analysis and comparative evaluation of various
energy‐related intervention scenarios pertaining to macro and microclimate conditions, demographics and behavioural issues, physical and technical aspects of the buildings, and
urban morphology.

(no german version) Recently, the interest in urban‐scale modelling of energy transfer phenomena has markedly
increased. This may be explained via multiple contributing factors. The steadily rising prominence of cities as humanity's habitant is one. Another factor pertains to the realisation of cities as highly nested constituents with complex behavioural traits - also with regard to the dynamics of energy generation, distribution, storage, and use. As with other complex problems, computational modelling of the pertinent processes can provide
effective support for both systems analysis and decision making. Specifically, the conception and evaluation of energy performance improvement strategies for the urban environment requires reliable predictions of the spatial and temporal distribution of energy
demand and supply. This implies the need for versatile modelling environments that can facilitate spatiotemporally detailed energy‐related district‐level inquiries, pertaining, for
example, to candidate energy efficiency measures.
In this context, the present keynote presents the structure and elements of a novel multifaceted urban energy computing environment, which is intended to concurrently
accommodate the following three essential requirements: i) Realistic representation of the dynamics and diversity of urban‐level microclimatic boundary conditions requirement; ii)
Realistic representation of internal (occupancy‐related) processes in buildings; iii) Highresolution numeric simulation of underlying transient heat transfer phenomena. To satisfy
these requirements in a computationally feasible manner, a specific hourglass paradigm is deployed. Thereby, a reduced representative sample of buildings in the pertinent urban
analysis domain is generated. This reductive step allows to use, instead of simplified and reduced order algorithms, detailed (high‐resolution, transient) numeric modelling, while
circumventing the problem of massive data requirements and extensive computational loads. The reductive step, however, inadvertently results in some loss of complexity and
diversity. The proposed hourglass paradigm thus involves a second step toward targeted recovery of lost diversity via probabilistically based variation of multiple input data sets for
extensive parametric simulations. As such, the resulting simulation‐assisted urban energy decision support environment facilitates the analysis and comparative evaluation of various
energy‐related intervention scenarios pertaining to macro and microclimate conditions, demographics and behavioural issues, physical and technical aspects of the buildings, and
urban morphology.

urban systems, energy, modeling