Center for the Built Environment

Residential demand response (DR) is a valuable resource for grid reliability, but remains challenging because the highly heterogeneous residential building stock leads to widely varying and hard-to-predict load and comfort responses during DR events. Although prior research has estimated the technical potential of DR-capable technologies for achieving energy demand savings, little is known about how they affect thermal comfort. In particular, it remains unclear how indoor thermal conditions due to DR depend on the thermal envelope characteristics of the housing stock. To address this gap, this study provides a systematic, location-specific assessment of indoor thermal performance during DR-events across the US housing stock using both typical DR weather data and detailed building metadata. We evaluate how envelope characteristics influence indoor temperatures during realistic simulated summer and winter DR events across 37 US locations, applying both temperature threshold and rate of temperature change criteria to estimate region-level probabilities of discomfort. Additionally, we show the impact of distinct weather patterns that intensify or abate thermal stress on comfort outcomes. Results show a near-universal overheating risk in summer DR events, where comfort outcomes are strongly influenced by rapid risk of comfort violations. In contrast, overall winter DR discomfort risk is lower, risk escalation is more gradual and shows greater sensitivity to event duration. These findings offer a data-driven quantification of comfort risk across diverse climates and building envelopes, demonstrating the need for region-specific DR scheduling and discomfort mitigation strategies tailored to local weather patterns and the performance of existing residential buildings.

Accurately quantifying operational carbon emissions from buildings is essential to verify that decarbonization targets are met. Yet many current practices rely on overly simplified yearly average emission factors that overlook the temporal variability of the electrical grid generation. This issue becomes increasingly critical with the rising penetration of renewable energy and the adoption of demand-side strategies in buildings, such as load shifting. Existing literature on net-zero carbon buildings rarely examines how the temporal resolution of emission factors affects accounting accuracy. In this study, we evaluated a range of grid emission factors (annual, seasonal, time-of-day, season-hour, and month-hour) and quantified their impact on carbon emissions accounting accuracy for commercial buildings across 18 U.S. grid regions. These emission factors, derived from publicly available datasets, are applied to measured hourly electricity consumption profiles from over 600 real commercial buildings. By considering hourly emission factors as the benchmark reference, we then quantified the resulting errors and uncertainties due to the reduced temporal resolution of the emission factors. Additionally, we assessed how buildings' on-site solar PV generation affects avoided emissions accounting from grid exports when electricity generation exceeds demand. As a result, we found that annual and seasonal averages are unreliable and should not be used for net-zero target assessments. Instead, we recommend incentivizing the adoption of season-hour and month-hour emission factors, which consistently deliver sufficient accuracy across diverse U.S. grid regions, with median errors typically less than 10%. This is also the case when quantifying the avoided emissions when utility export is available, yet the results are more variable and dependent on the grid generation mix. In particular, when coarse emission factors such as annual or seasonal averages are used, increasing onsite solar capacity can raise the median normalized fractional error to approximately 15% for operational emissions and to more than 100% for avoided emissions. These findings highlight the need for future standards and guidelines to consider at least the use of season-hour or month-hour emission factors' resolution to achieve acceptable emissions accounting accuracy.

Architects play a vital role in decarbonization efforts. Architects need the skills to assess the environmental impacts of their designs while understanding how early-stage design decisions shape the environmental impacts of their final designs. However, most academic structures do not provide future architects with the skills and knowledge needed to perform life cycle assessment (LCA) nor understand the linkages between design decisions and environmental impacts. Based on experiences from teaching LCA and life cycle thinking to architecture students in Brazil and the United Kingdom, we outline our vision for providing architecture students with the skills needed by industry to navigate the ever-changing climate crisis. We highlight that not everyone needs to do a full-scale assessment, but everyone should be able to engage in conversations related to sustainability and impact reduction.

Symbolic neural networks, such as Kolmogorov–Arnold Networks (KAN), offer a promising approach for integrating prior knowledge with data-driven methods, making them valuable for addressing inverse problems in scientific and engineering domains. This study explores the application of KAN in building physics, focusing on predictive modeling, knowledge discovery, and continuous learning. Through four case studies, we demonstrate KAN’s ability to rediscover fundamental equations, approximate complex formulas, and capture time-dependent dynamics in heat transfer. While there are challenges in extrapolation and interpretability, we highlight KAN’s potential to combine advanced modeling methods for knowledge augmentation, which benefits energy efficiency, system optimization, and sustainability assessments beyond the personal knowledge constraints of the modelers. Additionally, we propose a model selection decision tree to guide practitioners in appropriate applications for building physics.

Many cities and regions are making significant investments towards planning for extreme temperature and in particular extreme heat. A heat vulnerability index (HVI) is a metric to track spatial variation in extreme temperature risk to target mitigation interventions. Most HVIs focus on demographic characteristics, which generally relate to vulnerability, and lack information about the building stock, which mediate the occupant’s exposure to extreme temperatures. In this study, we use the Energy Information Administration’s (EIA) Residential Energy Consumption Survey (RECS) to estimate prevalence of temperature-related illness in the United States and develop machine learning models using climate, demographic, and building characteristics to predict them. Temperature-related illness affects approximately 2 million households annually, around 1% of the total population. The models we developed predict temperature-related illness with up to 85% accuracy. The most important feature is energy insecurity, which describes the household’s ability to maintain and operate heating, ventilation, and air conditioning (HVAC) systems. Our results offer guidance for municipalities to improve data collection, enabling them to better identify at-risk households and strategize resources for short-term and long-term interventions.

Our study uses EnergyPlus simulations to examine whole-building demand and energy end-use profiles for different design options and then uses these outputs to evaluate cost and carbon impacts of each scenario in Xendee, a modeling platform designed to “right size” and balance investments in distributed energy resources (DER).

Our results show that efficiency measures are key to meet the ambitious performance metrics for this project; however, most of the technology potential occurs for heating, ventilation and air conditioning (HVAC) or domestic hot water (DHW) loads which are a relatively small portion of a mid-rise multifamily building’s overall energy use. The most meaningful strategies to reduce or shift loads for this building include DHW load shifting, energy recovery ventilation, dynamic ventilation, and ceiling fans. Envelope strategies improve overall annual building performance but become an issue when lower heat loss increases cooling during the critical afternoon peak.

Compared to efficient, packaged air source heat pumps, a hydronic heating and cooling system (also serving DHW loads) with thermal energy storage has the best energy performance, highest load shifting capability, and best thermal resilience during outages. But because heating and cooling demands are small and hydronic systems are expensive, the net benefits of thermal energy storage are not substantial.

Sizing on-site generation and storage systems to cover the “worst case” outage conditions significantly drives up system size and cost. Even small deviations from 100% coverage (95%, or 99%) can dramatically reduce size and cost without a very meaningful change in resilience.

Extreme heat causes more weather-related deaths in the United States than any other natural hazard, and these events are projected to increase in frequency, intensity, and duration. As a result, it is critical to ensure safe thermal conditions in homes while minimizing excessive cooling energy use. In California, where the median age of homes is 45 years and nearly 40% lack mechanical cooling, this deficiency undermines one of the most essential goals of housing: to shelter people from outdoor weather of a warming planet. We aim to quantify the overheating risk in the housing sector to support the development of public policies related to maximum safe indoor thermal limits and building energy use. Using the ResStock modeling framework, we created over 52,000 building models to represent California's residential housing stock and assessed overheating risks by simulating indoor temperatures and analyzing the energy impacts of adding cooling systems.Our findings reveal significant regional disparities. Southern counties face the highest overheating risk, while coastal areas are less vulnerable due to oceanic temperatures. Inland counties, such as the Sierra Nevada region, are also less affected due to higher altitudes. Approximately 1.6 million homes will require retrofitting with cooling systems, which could increase peak electricity demand by 2%. Our sensitivity analysis showed that increasing the threshold temperature reduces the number of homes subject to overheating risk. These results significantly impact the electricity grid, emphasizing the need to consider passive cooling options like shading and cool roofs or energy-efficient cooling options like fans and evaporative coolers.

Residential space-conditioning-based demand flexibility (DF) has become an increasingly sought-after method for demand-side load management to enhance grid reliability and facilitate integration of renewable energy generation. However, predicting the effectiveness and flexibility of residential DF resources is challenging. Current estimates show that only 50% of projected savings from DF resources are actualized. Currently, there is a very limited understanding of how thermal comfort during space-conditioning-based DF events in real-world settings impacts household energy use behaviors and, consequently, the success of DF programs in achieving targeted savings. This paper proposes a method to comprehensively assess the thermal comfort implications of DF strategies and presents results of their impacts on DF event participation decisions and demand savings. The study's key findings are: 1) DF event setpoint offsets that maintain indoor operative temperatures between 18 to 22°C (65 to 71°F) may be preferred in Cordova, Alaska; 2) Household-level thermal comfort is more sensitive to the duration of the DF event than to the degree of temperature offset from baseline conditions; 3) The delayed impact of changes in indoor operative temperature in response to setpoint offsets, both during and after a DF event, influences occupants’ thermal comfort perceptions and willingness to persistently participate in events. The findings from application of the proposed method can help inform future larger-scale occupant-centric DF programs as it can capture information not readily available. Thus, it can supplement these sources and help develop occupant-centric DF strategies, enabling more accurate predictions of participation rates and savings estimates for space-conditioning-based DF programs.

To decarbonize the buildings and electricity sectors at a pace consistent with state and national climate goals, buildings must become grid resources, capable of morphing load profiles to accommodate variable renewable generation, facilitate cost-effective grid decarbonization, and help ensure reliable power system operation. Simultaneously, the changing climate poses increasing risks of extreme weather events and resulting power outages that developers, designers, and building operators must plan for. This yield s new questions around the best approaches to achieve these outcomes, what it costs, who benefits, and what barriers there are to widespread adoption.We seek to answer these questions by adopting a futuristic set of grid-interactive design requirements for a mid-rise affordable housing, mixed-use development: to sustain critical loads during an outage and to consume no grid electricity for residential end-uses between 4-9pm every day. We present an approach that achieves these requirements, working to decarbonize the grid and maximize resilience for the facility and its residents while also addressing shortfalls in housing supply. Our work includes detailed building energy and economic optimization combined with construction cost analysis and carbon impacts. We find that achieving the requirements comes at a ~5% cost premium, an amount not likely to be acceptable for affordable housing under current policies and industry standards. We then generalize our design into a replicable ‘recipe’ to optimize the integrated use of distributed energy resources and explore the challenges to widespread adoption. We suggest many barriers could be overcome with policy changes that would bring benefits to under-resourced populations and society as a whole.

In high- and middle-income countries, there is a great reliance on heating, ventilation, and air conditioning systems (HVAC) to control the interior thermal environment. However, these systems are expensive to buy, maintain, and operate while being energy and environmentally intensive. Easily-accessible passive and low-energy strategies, such as fans and electrical heated blankets, address these challenges but their comparative effectiveness for providing comfort in sleep environments has not been studied. Using passive strategies in combination with low-energy strategies that elevate air movement like ceiling or pedestal fans enhances the cooling effect by three times compared to using fans alone. We extrapolated our experimental findings to estimate heating and cooling effects in two historical case studies: the 2015 Pakistan heat wave and the 2021 Texas power crisis. Passive and low-energy strategies reduced sleep-time heat or cold exposure by 69-91%. The low-energy strategies we tested require one to two orders of magnitude less energy than HVAC systems, and the passive strategies require no energy input. These strategies can also help reduce peak load surges and total energy demand in extreme temperature events. This reduces the need for utility load shedding, which can put individuals at risk of hazardous heat or cold exposure. Our results may serve as a starting point for evidence-based public health guidelines on how individuals can sleep better during heat waves and cold snaps without relying on HVAC.