Urban Building Energy Modelling In Mendoza (AR). Statistical Models For Space Heating & Domestic Hot Water

Cities are home to half of the world's population. By 2050, 70% of people would live in different cities, based on UN reports. Obviously, the population growth has made serious environmental consequences such as climate change and GHG emissions. More specifically, one of the main actors regarding energy consumption is construction sector or more specifically residential buildings where energy is needed for various activities, which, the main contributors of consumption are space heating & domestic hot water. Therefore, understanding of buildings characteristics & thermal energy profile in existing situation, and predicting it for future has a considerable importance. Therefore, applying energy consumption analysis should be at city scale instead of a single building The selected case study is the metropolitan area of gran Mendoza located in Argentina. Total primary energy of Argentina is dominated by natural gas (55%) & oil (33%), both as fossil fuels. Relatively, the energy source in Mendoza is mostly supported by natural gas, in addition to, its expansion during past 30 years, it has provided harmful environmental impacts . This research aims to describe how to model thermal energy consumption of whole Mendoza with aid of existing census database, annual NG consumption data and a GIS-based methodology. Consequently, we will be able to acquire most energy related variables, correlating to both space heating & domestic hot water consumption and a statistical model of natural gas consumption. Regarding this, after leveling all data in same scale, a sensitivity analysis carried on identifying most correlated variables to energy consumption. After this a multiple linear regression analysis is adopted to predict NG consumption & compare it with real data. At last, two types of modelling presented for central & peripheral areas with their most energy related variables for reference year of 2016 & three scenarios of kWh, kWh/m2, kWh/family once for normal NG consumption & once for Normalized NG consumption (total six scenarios). Lastly, having energy consumption data & its influential variables will assist us to spot critical areas of a city & discovering the variables that made great impact on them. Besides, applying the correct decisions or relevant interventions towards more sustainable city & energy systems.