A new clustering and visualization method to evaluate urban heat energy planning scenarios. (May 2019)

• Record Type: Journal Article
• Authors: Torabi Moghadam, Sara; Coccolo, Silvia; Mutani, Guglielmina; Lombardi, Patrizia; Scartezzini, Jean-Louis; Mauree, Dasaraden
• Is Part Of: Cities. Volume 88(2019)
• Page Start: 19
• Page End: 36
• Publication Date: 2019-05
• Subjects: Built environment -- Geographical information system -- Statistical models -- Deterministic models -- Urban heat energy planning -- Spatial decision support system; City planning -- Periodicals; Urban policy -- Periodicals; 711.4
• DOI: 10.1016/j.cities.2018.12.007
• Languages: English
• ISSNs: 0264-2751

Abstract

Abstract: Spatial 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. Graphical Abstract: Highlights: New methodology to design city archetypes to mimic the energy behavior of the city. Sensitivity study of the simulated and monitored data within a statistical method. Spatial decision support system to visualize multiple refurbishment scenarios.