@misc{31750,
  keywords = {Buildings, Energy, GHG emissions, Urbanization, Models, PV, Renewables, Visualization, District-scale, Smart Cities, Electric Mobility, Land use planning, Developer, Forecast},
  author = {Reshma Singh and Baptiste Ravache and Mary Ann Piette},
  title = {Energy Modeling in Urban Districts: Forecast of multi-sector Energy Use and GHG Emissions},
  abstract = {<p>This document reports the findings of the energy use and greenhouse gases (GHG) emissions model loosely based on three districts in the Bay Area, District A, District B, and District C.<br />Modeling platforms exist for city energy benchmarking, inventorying, and GHG emissions forecasting and planning. However, the wide variety and features of today's tools, their focus on a sub-set or snapshot data from various energy generation and consumption sectors, and the fact that many of them are not open data models, create sub-optimal environments for the energy analysis districts are seeking to conduct. Hence, we have developed a new software tool with customized data and dynamic visualization; DEPICT (Decision-support and Emissions Prediction Interactive Cities Tool) to obtain energy and emissions forecasts at different stages of the districts' build-out by varying selected design parameters. This report presents the methodology and framework we have developed to estimate whole district emissions and details the results by district, with the objective of finding insights into the main sources of emissions, and the available levers to reduce them efficiently. This document details the impact of each emissions mitigation measure investigated, along with the assumption and models that were used to reach these values. The key findings for each district are presented in Table 1 and Table 2.</p>},
  year = {2019},
  month = {06/2019},
  language = {eng},
}