‘Zukunftsquartier’—On the Path to Plus Energy Neighbourhoods in Vienna

Leibold, Jens; Schneider, Šimon; Tabakovic, Momir; Zelger, Thomas; Bell, Daniel A.; Schöfmann, Petra; Bartlmä, Nadja

doi:10.1007/978-981-32-9868-2_17

Cited by 4 publications

(2 citation statements)

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“…The "floor space index" parameter (FSI) can also be an indication of the space that is available for renewable energy generation in relation to the number of people living and consuming energy in that specific area, as described by [35]. The FSI (also called the floor area ratio) is defined by Equation (1) [36]:…”

Section: Input Parameters For District Categorisationmentioning

confidence: 99%

“…The FSI ranges indicate the type of the settlements within a neighbourhood: very rural settlements (<0.25), single-family houses (0.25-1), row housing (1-2) and block housing to very dense urban settlements (>2) [35]. A lower FSI could indicate that a larger area (roofs) is available for renewable energy generation, while the overall energy demand density stays low.…”

Section: Input Parameters For District Categorisationmentioning

confidence: 99%

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Creating Comparability among European Neighbourhoods to Enable the Transition of District Energy Infrastructures towards Positive Energy Districts

et al. 2022

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Planning the required energy infrastructure for the energy transition is a crucial task for various neighbourhood concepts, such as positive energy districts. However, energy planning often comes with the challenges of data shortages and a lack of comparability among solutions for different districts. This work aims to enable this comparability by introducing an approach for categorising districts according to parameters that are relevant for the planning of neighbourhood energy infrastructures. Four parameters (climate, floor space index, heating demand and share of residential buildings) and their respective ranges (bands) were derived from the literature. Additionally, this work visualised the combination of all parameter bands across Europe to conveniently showcase districts that are comparable according to the selected parameters. This approach and its visualisation could be used in urban planning to share knowledge from existing energy district projects with those planned in comparable districts.

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Section: Input Parameters For District Categorisationmentioning

confidence: 99%

Section: Input Parameters For District Categorisationmentioning

confidence: 99%