Urban residential energy consumption modeling in the Integrated Urban Metabolism Analysis Tool (IUMAT)
Abstract The Integrated Urban Metabolism Analysis Tool (IUMAT) is a system-based computational platform for quantifying the environmental impacts of urban development scenarios. IUMAT's EWM module is a bottom-up approach to generate energy, water, and material resources demand profiles based on building and neighborhood characteristics. This paper presents the EWM approach using national and regional datasets to identify the relationships between environmental impacts and resource use determinants within a simulation platform for urban metabolism analysis. We focus on residential energy consumption, which serves as a template for how the EWM module will be used to simulate commercial and industrial demand profiles. Quantile regression methods are applied to Residential Energy Consumption Survey (RECS) 2009 data to describe the impacts of physical and socio-economic parameters on end use residential energy profiles and create a modeling framework for residential energy prediction. Also, a method for quantifying CO 2 emissions and water consumption associated with energy production is outlined. Highlights Quantile regression is used to predict residential energy use categories. A method for calculating GHG emissions for residential energy use is introduced. The use of actual datasets for energy modeling and policy making is evaluated. Prediction power for HVAC energy use is scale-dependent, unlike other categories. Pricing and retrofit strategies do not impact energy savings in some categories.
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