Accurate building geometry is fundamental to neighborhood-scale building energy modeling because it directly shapes conditioned volume, envelope area, and resulting energy use patterns at both the building and district scales. Improving geometric fidelity is especially important when modeling small residential buildings in low-income communities, where reducing uncertainty in baseline simulations is necessary to support equitable planning and energy-burden aware decision making. Prototype, archetype-based building stock datasets (e.g., ORNL Model America) enable scalable neighborhood modeling; however, because they are developed for broad coverage, geometric representation can remain uncertain for small residential buildings, motivating community-specific calibration of key geometric features.

This study develops and tests a geometry-correction workflow that updates archetype-based residential prototypes using local spatial data and quantifies the impact on simulated energy performance. Two geometric updates are implemented: (1) replacing default footprints with building outlines extracted through deep-learning segmentation of high-resolution aerial imagery in a GIS environment, and (2) correcting building height using LiDAR-derived elevation differences. The workflow is applied to 22 single-family homes in a low-income Arizona neighborhood. For each home, three EnergyPlus models are simulated; baseline, height-only, and full-geometry, while holding constructions and schedules constant to isolate geometric effects.

Results show that correcting height alone increases annual site energy by an average of 16%, reflecting underestimated conditioned volume in prototype models. Incorporating both footprint and height further increases total site energy at the neighborhood scale, while moderating or reducing EUI at the building scale. These findings demonstrate that geometric inaccuracies in prototype datasets propagate to district-level estimates, highlighting the need for morphology-aligned models in energy analysis for low-income communities.

Contemporary energy-efficient building design often relies on standardized assumptions about household energy use, assumptions that do not reflect the diverse ways people consume energy across social, climatic, and cultural contexts. This study addresses this limitation by analyzing the 2020 Residential Energy Consumption Survey (RECS), a nationally representative dataset containing detailed information on household energy use, building characteristics, and operational behaviors. Drawing on descriptive assessment, end-use analysis, and mixed-data clustering, the study develops intensity-based energy-use typologies that characterize demand heterogeneity at the residential scale.

Results reveal pronounced variation in total consumption and energy use intensity (EUI) across states, climate zones, and housing forms, with space and water heating energy demands emerging as the dominant contributors to annual household consumption. The study also underscores the significant but frequently overlooked contribution of the “Other” end-use category, small appliances and electronics, which is often missing from conventional energy modeling practices. In addition, the results highlight the influence of building form and thermal exposure, suggesting that design strategies such as shared walls and compact massing can meaningfully reduce energy intensity.

Clustering identifies 13 statistically distinct household clusters, which are subsequently interpreted as energy-use typologies based on their distinctions in energy profiles. These typologies provide a structured framework for differentiated architectural decision-making, archetype selection informing energy simulations, retrofit prioritization, and performance target setting without asserting specific causal drivers. By translating empirical energy segmentation into design-relevant categories, the study advances a performance-informed approach to residential energy analysis that supports more context-sensitive and scalable architectural strategies.

Implementation researchers typically refer to the gap between knowing what should be done and current best practice as the "Know-Do Gap." However, in the field of residential energy efficiency, architects have long recognized the importance of reducing energy consumption through passive design strategies before offsetting energy use with renewable sources. In an era of mechanical conditioning and ventilation, however, thermal comfort can easily be provided independent of envelope design. This creates a divide where high-performance design is reserved for custom residential projects, and cost of construction dictates design decisions for the vast majority of “typical” residential new construction. This focus on initial cost seemingly precludes consideration of operations and maintenance costs, and rather than leveraging energy efficiency measures that reduce the total cost of homeownership, minimum performance baselines dictate envelope and system efficiencies. In non-profit affordable housing development, where resources must be allocated judiciously to extend impact to the greatest number of clients, the authors see a strong opportunity for beyond-code construction to be incentivized.

Using initial findings from a study measuring energy savings attributed to energy efficiency improvements in affordable, single-family housing, the paper seeks to identify data points beyond initial construction cost that can inform decisions affecting building performance. The larger study tracks how changes to physical characteristics influence the energy use of a house by measuring circuit-level consumption in eight pairs of houses constructed by non-profit affordable housing developers in a range of climate zones across the US. Initial interviews with the construction teams provide insight into their decision-making processes, and additional feedback will be gathered once energy monitoring study findings are shared with those teams. This paper documents how nonprofit affordable housing developers choose to invest in performance improvements, and results of the study will serve to inform the motivation for incentives offered moving forward.