Urban Building Energy Modeling provides a way to simulate energy use at the scale of a neighborhood or city, rather than the typical simulation of a single building. This can be a powerful tool to reduce current energy usage and to guide future planning efforts. This switch in scales is crucial in reducing energy use and planning more sustainable and resilient cities. This project identifies the impacts of socioeconomic factors and establishes a framework that can be used to gather the data required to run accurate urban building energy modeling studies that consider the urban context. A case study utilizing the Urban Modeling Interface Rhino plugin to simulate the energy use of 110 single-family residential structures in the Grove Park neighborhood of Atlanta, Georgia demonstrates the framework. The results of the study analyze current energy use patterns, compare underserved neighborhood-specific archetype definitions to default residential archetype templates, and investigate the neighborhood's performance under future weather scenarios. 

Urban Building Energy Modeling (UBEM) platforms such as CityBES and UMI primarily utilize EnergyPlus for thermal building-by-building simulations in conjunction with occupancy informed models for thermal comfort. A significant percentage of low-income residents, however, experience energy vulnerability and accordingly register for prepayment plans due to their inability to meet their basic household energy needs. Prepayment plans reduce energy usage due to customers’ decisions but result in thermal discomfort and health concerns. Since current urban modelling platforms do not account for energy vulnerability, the predicted Energy Use Intensity (EUI) for low-income communities can be misleading. This project evaluates the impact of energy vulnerability on the EUI of the Grove Park Neighborhood, an underrepresented community in Atlanta, Georgia, through reduced energy consumption rate scenarios in the occupancy schedules. 

According to the Intergovernmental Panel on Climate Change (IPCC), the global mean temperature is expected to increase from 1.4°C to 5.8°C by 2100. The implications will be particularly significant in urban areas as indoor and outdoor comfort levels will be disrupted, leading to significant health impacts. One of the expected impacts is indoor overheating, as it has been identified as one of the major causes of thermal discomfort and is directly linked to the potential increase in mortality levels in the future. This paper focuses on the potential implications of increased overheating hours on human health in an old low-income residential neighborhood. We study the effect of three main factors: population coping capacity, building thermal performance, and human physiological response to heat exposure. This is achieved by examining an old low-income neighborhood in Cairo, Egypt, whose residents have limited cooling systems access. Results indicate higher overheating risks in older buildings with a projected increase of 18% in indoor temperature and higher health risks, especially for elderly residents. The study's findings can be considered a starting point to examine the relationship between exposure duration, indoor air temperature range, and potential health risks for vulnerable urban communities with limited access to cooling mechanisms such as AC units.

Local energy management programs have the potential to flatten the so-called “duck curve” and enable greater use of renewables by engaging residents to work toward shared sustainability goals. However, there are challenges with managing energy behavior to achieve desired outcomes. This study uses the Energy Cultures framework to examine motivations for participating in a local energy program. It extends the Energy Cultures framework by demonstrating its applicability to the analysis and co-design of local energy management programs, with a particular focus on community-level engagement. The research also identifies the types of information most useful in managing energy behavior. The study site is the Mueller community in Austin, Texas, U.S.A., which has high levels of solar generation and a resulting duck curve that can be addressed by local energy management. A mixed-methods approach was utilized, with qualitative data collection through stakeholder interviews and a community workshop, followed by quantitative data collected in a survey of residents. Results show a strong interest in the proposed energy program, with residents motivated by environmental, societal, and financial benefits. Individual and social norms drove favorable responses while material culture and practices did not. Valuable types of information feedback included energy usage recommendations and renewable energy use at the individual and community level.