Résumé

Designing energy efficient cities and in particular designing buildings as well-thought components of the urban fabric and active components of the urban energy system requires reliable information on the current demand in energy within buildings, its distribution in time and space and the possibility to impact on this demand. In this article we present a methodology developed to produce this information by aggregating the simulation results of a very large number of buildings. The methodology relies on the use of an existing simulation tool (bSol) and of selected default parameter values corresponding to pre-defined building typologies as well as data from existing weather and GIS databases for calibrating the tool. The core challenge being the adequate choice of default parameters related to the building’s environment (weather conditions and surroundings), its fabric (mass and envelope), its equipment and its occupants’ behaviour, we pay special attention to producing a sensitivity analysis of the tool’s results in relation to these parameters. This leads us to define a database structure for required default values and to start populating the database with robust values. We apply the methodology to recognise typical urban typologies relevant to energy planning and the parameters defining them, such as building typologies, mixity of use, urban density and morphology, existing energy infrastructure, potential for renewable energy production. For each urban typology we attempt to propose typical solutions at different levels of spatial resolution, ranging from decentralised solutions to more centralised solutions at neighbourhood, district or city level. While the methodology presented was developed within a Swiss project it can just as well as be applied to the challenge of planning and implementing energy efficiency in rapidly growing cities of developing countries, in particular to the regeneration of existing and planning of new city districts.

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