000263236 001__ 263236
000263236 005__ 20190809113530.0
000263236 0247_ $$2doi$$a10.1016/j.cities.2018.12.007
000263236 02470 $$2DOI$$a10.1016/j.cities.2018.12.007
000263236 037__ $$aARTICLE
000263236 245__ $$aA new clustering and visualization method to evaluate urban heat energy planning scenarios
000263236 260__ $$c2019-05-19
000263236 269__ $$a2019-05-19
000263236 336__ $$aJournal Articles
000263236 520__ $$aSpatial visualization is a very useful tool to help decision-makers in the urban planning process, i) to define future energy transition pathways, ii) to implement energy efficiency strategies and iii) to integrate renewable energy technologies in the context of sustainable cities. There is thus a need to develop new tools to understand the energy consumption patterns across cities. Statistical methods are often used to understand the driving parameters of energy consumption but rarely used to evaluate future urban refurbishment scenarios. Simulating whole cities using energy demand softwares can be very extensive in terms of computer resources and data collection. A new methodology, using city archetypes, is hence proposed to simulate the energy consumption of urban areas and to evaluate urban energy planning scenarios. The objective of this paper is to present a solid framework and innovative solution for the computation and visualization of energy saving at the city scale. The energy demand of cities, as well as the microclimatic conditions, are calculated by using a 3D model designed as function of the real city urban geometrical and physical characteristics. Data are extracted from a GIS database. We demonstrate how the number of buildings to be simulated can be drastically reduced thereby significantly decreasing the computational time and without compromising the accuracy of the results. This model is then used to evaluate the influence of two sets of refurbishment solutions. The energy consumption are then integrated back in the GIS to identify the areas in the city where refurbishment works are needed more rapidly. The city of Settimo Torinese (Italy) is used as a demonstrator for the proposed methodology, which can be applied to medium-sized cities worldwide with limited amount of information.
000263236 6531_ $$aBuilt environment
000263236 6531_ $$aGeographical information system
000263236 6531_ $$aStatistical models
000263236 6531_ $$aDeterministic models
000263236 6531_ $$aUrban heat energy planning
000263236 6531_ $$aSpatial decision support system
000263236 700__ $$aTorabi Moghadam, Sara
000263236 700__ $$g193973$$0247286$$aCoccolo, Silvia
000263236 700__ $$aMutani, Guglielmina
000263236 700__ $$aLombardi, Patrizia
000263236 700__ $$aScartezzini, Jean-Louis
000263236 700__ $$aMauree, Dasaraden
000263236 773__ $$tCities$$j88$$q19-36
000263236 8560_ $$fbarbara.smith@epfl.ch
000263236 909C0 $$zPasquier, Simon$$xU10262$$pLESO-PB$$mmarlene.muff@epfl.ch$$mbarbara.smith@epfl.ch$$0252072
000263236 909CO $$ooai:infoscience.epfl.ch:263236$$pENAC$$particle
000263236 960__ $$abarbara.smith@epfl.ch
000263236 961__ $$afantin.reichler@epfl.ch
000263236 973__ $$aEPFL$$sPUBLISHED$$rREVIEWED
000263236 980__ $$aARTICLE
000263236 981__ $$aoverwrite