Single-family homes account for 52% of the building stock in the U.S., but Life Cycle Analyses (LCAs) and energy modeling are rarely incorporated in their design process as this segment of the market typically has smaller budgets, fewer tools, and less building performance expertise than for larger projects. This session focuses on building performance in this underserved small-residential design realm: limitations faced by practitioners, tools targeted to designers, and processes & datasets for this segment of the industry.

This introductory presentation will provide an overview of small residential projects and the small firms primarily responsible for designing those projects, focusing on the limited use of building performance studies in this sector and the issues faced by practitioners in incorporating such studies in the design process. Results from a survey of the AIA Small Firm Roundtable will be shared.

The Decarbonization Analysis for Residential Architecture (DARA) tool was developed as an open-source, accessible design tool for assessing the operational and embodied carbon impacts of design decisions in single-family and low-rise residential buildings. It is intended to fill a gap in the market for small design firms working on single family and other small-scale residential buildings. DARA was created with the input of design professionals that work on these types of projects, resulting in a tool that can fit within their workflow & economic constraints, and analyze building features that are most important to these practitioners. The tool relies on a static data set derived from Tally and energy modeling outputs based on interpolated simulation results. This paper provides a framework for why the tool is useful in the market, describes the tool interface, and outlines the analysis by which the data was created.

The number of data points required for parametric interpolation grows exponentially with the number of parameters, a particular issue for energy modeling due to the computational requirements for energy simulations. This presentation describes the development and validation of an interpolation dataset for small residential buildings that substantially lowers the total number of required simulations while preserving accuracy in estimated heating and cooling energy use intensity. A multi-linear interpolation approach was implemented using a limited set of strategically selected anchor simulations spanning envelope, glazing, infiltration, ventilation, and climate variables. Sensitivity analyses were conducted to assess linearity and identify control points that maintain interpolation error within acceptable bounds. Although the single-family residential dataset presented here was developed for use in the DARA tool, the underlying workflow is broadly applicable to other building typologies and parametric energy modeling efforts.

This paper proposes a new occupancy schedule calculation methodology that considers “at home during weekdays” and “outside of home during weekdays” profiles, using nationwide survey data and the K-means clustering method. In addition, two HVAC system OCC strategies are designed to demonstrate the application of the new occupancy schedules. The result shows that the proposed methodology provides more flexible and realistic occupancy schedules, and OCC strategies can reduce energy consumption in residential buildings by capturing the flexibility of more realistic occupancy schedules.

Since 2013, England’s (England's) Permitted Development Rights (PDR) have enabled the conversion of commercial buildings, such as offices, into housing without formal planning permission. While this policy increases housing supply, many PDR conversions exhibit poor indoor environmental quality— (quality, such as) limited space, inadequate thermal comfort, inefficient energy use, and insufficient daylight— (daylight) potentially compromising occupants’ (occupants') wellbeing. Existing optimization research often relies on theoretical models rather than real projects. This paper investigates how Artificial Intelligence (AI)-driven optimization of indoor spatial configurations can enhance environmental performance in actual PDR homes, focusing on improving thermal comfort, energy efficiency, daylight access, and outdoor views in an office-to-residential case study. The results demonstrated the applicability of the proposed method in enhancing the energy and environmental performance of PDR office to residential conversion.