Climate projections indicate that the built environment will be increasingly exposed to extreme temperature events of greater magnitude, frequency, and duration over the coming decades. These events are often accompanied by power outages that persist beyond the initial hazard, resulting in indoor conditions that may threaten occupant health and compromise building operability. This study evaluates the role of energy efficiency and distributed energy systems in improving building reliability, resiliency, and occupant survivability during extended grid disruptions.

The analysis is based on a schematic-phase case study of an approximately 85,000 ft² development in Atlanta, Georgia. The project is designed to pursue LEED Platinum and Living Building Challenge goals, with the additional intent of functioning as a resilience hub during extreme weather events and prolonged outages.

The study evaluates the integration of battery-backed solar photovoltaic systems with islanding capability to support building operability. Photovoltaic capacity is dictated by available roof and parking areas and sized for 105% of annual building energy use in alignment with LBC requirements. Energy storage performance is analyzed over a seven-day period with the highest net electrical consumption—defined as building energy use minus on-site PV generation—under both extreme heat and extreme cold conditions. Counterintuitively, Atlanta being a cooling dominated climate, the critical battery size is dictated by winter conditions due to low PV production. Multiple operating scenarios are evaluated, ranging from critical loads only to maintaining habitable indoor temperatures while supporting essential lighting, equipment, and limited occupant device charging. These scenarios result in on-site battery storage requirements ranging from 200 kWh to 4,500 kWh.

To complement energy resilience, passive survivability is evaluated for extreme heat and extreme cold events using historical weather data and future climate projections. Indoor habitability during simulated power outages is assessed using occupant-centric thermal resilience metrics, including Standard Effective Temperature (SET) and Heat Index (HI). Passive survivability is defined as indoor conditions maintaining SET values between approximately 15 °C and 32 °C, representing lower and upper bounds for short- to moderate-duration occupancy without active HVAC systems. The number of hours meeting these survivability criteria is quantified for three envelope performance configurations: code-minimum construction, an improved high-performance envelope, and a Passive House–equivalent envelope. Effect on inclusion of natural ventilation for summer is included to extend passive survivability hours in summer. Results illustrate the strong influence of envelope thermal performance on indoor conditions during prolonged outages.

The findings demonstrate that efficient buildings, particularly when paired with island-able photovoltaic systems and on-site energy storage, are substantially better equipped to maintain functionality and protect occupant health during extreme events. Energy-efficiency measures are shown to provide value beyond operational cost savings by extending passive survivability, reducing required battery capacity, and enhancing overall building reliability and resiliency.

As net-zero operational energy becomes an expected target rather than an exception, project teams are increasingly confronted with a new challenge: how to address whole-building carbon in a way that is practical, defensible, and repeatable beyond pilot programs. This presentation shares a real-world approach for integrating operational energy modeling and embodied carbon assessment, one that moves from pilot program requirements to everyday design practice.

The approach was developed through a school project participating in a public embodied carbon pilot program, but the workflow is intentionally transferable to any project type. The process begins with an envelope-first strategy to aggressively reduce operational energy demand before turning to systems and renewables. In this case, early energy modeling showed an energy use intensity (EUI) of approximately 70 kBtu/ft²-yr. Code-exceeding envelope measures reduced this to roughly 31 kBtu/ft²-yr, while enhanced thermal performance, high-R walls and roofs, improved glazing, and reduced infiltration, lowered EUI further to about 25.7 kBtu/ft²-yr. Electrified mechanical systems, efficient lighting, and advanced controls reduced operational demand to approximately 15.9 kBtu/ft²-yr, allowing on-site photovoltaic generation to offset the remaining load and achieve net-zero operational energy.

At the same time, the project team conducted phased whole-building embodied carbon calculations using Environmental Product Declarations (EPDs) and whole-building life cycle assessment (WBLCA), reporting Global Warming Potential (GWP) without biogenic carbon inclusion. Embodied carbon intensity results ranged from approximately 38 to 47 kgCO₂e/ft² across defined life-cycle boundaries. Rather than optimizing embodied carbon in isolation, the team evaluated how operational energy decisions influenced material quantities, system choices, and construction feasibility.

The presentation focuses on the moments where modeling met reality: when early assumptions had to be tested against cost, constructability, procurement, and evolving pilot program expectations. By emphasizing process, timing, and coordination over any single outcome, this case study demonstrates how whole-building carbon accounting can evolve from pilot participation into a practical, repeatable approach.

The lessons shared provide actionable insight for designers, modelers, and policymakers seeking to bridge the gap between simulation and measurable carbon performance.

This study presents an automated workflow for evaluating Phase Change Materials (PCM) in building envelopes across diverse climates and building typologies. Two tools were developed: an OpenStudio Measure and a Python library for automated EnergyPlus input data file (IDF) modification. These tools enable efficient PCM definition, envelope integration, and large-scale simulation management. Small Office prototype models were tested with two different PCMs across all 16 climate zones (CZ). Results indicate negligible cooling benefits but heating energy savings of 9–17% and heating demand was reduced by 11–17% in colder climates (CZ-6A to CZ-8). The toolkits facilitate systematic PCM analysis and support climate-specific application strategies.

Accurate calibration of residential building energy models is essential for producing reliable energy audits and retrofit recommendations. Traditional calibration methods often require adjusting numerous input parameters simultaneously, leading to excessive computational time and non-unique solutions that are impractical for field auditors and contractors. This paper presents the Deviation-Based Grouping Calibration (DBGC) framework, a novel approach that enhances calibration efficiency by explicitly linking measured end-use deviations to the parameters most responsible for them. The method integrates three key components: end-use deviation classification, which identifies whether discrepancies are dominated by heating, cooling, or baseload errors; parameter grouping, which limits the optimization to relevant variable sets; and adaptive range narrowing, which dynamically scales parameter bounds in proportion to the deviation magnitude. Implemented using Particle Swarm Optimization (PSO) through the GenOpt–OpenStudio platform, DBGC automates the calibration process while maintaining physical realism.

The framework was validated using two benchmark houses from the NREL BESTEST suite, an all-electric heat pump home and a dual-fuel home with a gas furnace and central air conditioning. Results show that DBGC achieved accurate calibration and reduced the number of optimization iterations by up to 85% compared with full-parameter calibration. The method offers a structured, transparent, and scalable workflow that bridges the gap between advanced research-level optimization and the practical requirements of residential energy audits, providing a reproducible pathway toward faster, data-driven building model calibration.

This study presents a framework that generates annual future weather data (AFWD) files, rather than a Future Typical Methodological Year (FTMY), using high-resolution bias-corrected RCM data for building energy assessment in changing climate conditions. Additionally, the proposed AFWD files were expanded to include morphed weather variables (e.g., direct normal irradiance and diffuse horizontal irradiance), which are underrepresented in existing future weather files such as FTMY. The proposed framework is capable of accurately capturing future extremes and weather fluctuations and predicting their impact on the building’s energy load and energy grid systems. A heating demand comparison analysis was conducted between 16 DOE prototype commercial buildings using AFWD and FTMY, with respect to the HTMY. Building energy simulation results with AFWD exhibit year-to-year weather fluctuations that the FTMY has limitations in capturing. Furthermore, unmet heating hours and Time Not Comfortable (TNC) based on ASHRAE 55 were investigated for the high-rise apartment with HVAC hard-sized based on HTMY. Results indicate that the AFWD file shows more unmet heating hours than FTMY, due to its ability to capture extreme cold events. The TNC hours under AFWD were higher than those under FTMY by approximately 11%, 6%, and 5% for the periods 2025–2039, 2040–2059, and 2060–2075, respectively. This study highlights the importance of evaluating future HVAC performance using AFWD in conjunction with FTMY, particularly when assessing comfort risk, HVAC reliability, and resilience. This can be utilized by building designers and policymakers to inform better design decisions and long-term resilience planning for buildings.

Digital Twins (DTs) offer significant potential for improving building energy modeling, yet the trade-offs among model Level of Detail (LoD), data resolution, and predictive accuracy remain poorly quantified. This study presents a comparative, replicable framework that evaluates multiple geometry-generation pathways, zoning strategies, and operational data inputs using a sensored university building as a testbed. By examining data-light and data-rich configurations, the framework identifies how geometry fidelity, occupancy schedules, and ventilation measurements influence simulation performance. The study provides evidence-based guidance for selecting LoD and sensing strategies to support efficient, scalable DT development that will be integrated into energy predictions.

Enabling local and renewable energy systems and maintaining stable energy services are key to a sustainable energy future. Energy hubs (EHs) that convert and store/release multiple energy types provide flexible resource-sharing opportunities for groups of buildings in urban and rural areas, such as neighborhoods, towns, and campuses. However, state-of-the-art EH design requires nonlinear and dynamic models to accurately account for electrical and thermofluid dynamics in these highly coupled systems. This paper introduces the open-source EnergyHub package in Modelica for designing renewable EHs that serve space heating/cooling, domestic hot water, and electrical demands for buildings. The developed models are first presented and then illustrated in a case study with a grid-tied multi-energy district in France, where we evaluate key performance metrics (i.e., self-sufficiency ratio) considering both electrical and thermofluid energies. Results indicate the importance of using onsite resources and reveal a Pareto optimum for self-sufficiency and life cycle cost. Providing high-level templatized models for emerging EH applications, this work improves accessibility} and design practices for innovative EHs of the future.

Space heating decarbonization is a central priority of the Chicago Climate Action Plan, motivating the launch of an ambitious residential retrofit program to minimize natural gas heating consumption. New evidence suggests that solar heating resources are substantial in the region, potentially expanding these retrofit options, but design parameters influencing solar heating performance in the region have not yet been investigated. Here, we examine the ability of direct solar heat collection to reduce representative contemporary space heating needs through EnergyPlus simulations. In a single-family dwelling of recent construction, the addition of solar heat-collecting skylights approximately doubled the window solar heat gain, while night insulation reduced window and skylight heat losses by over two-thirds. Indoor air temperatures rose in response, as expected, but this warmer indoor air caused heat losses from air leakage to increase, partly counteracting the solar heat collection and retention. Envelope air-tightness measures and thermal storage mass largely reversed these infiltration losses, however. Together, these measures reduced heating loads to 44% of the baseline, lowering annual household CO2 emissions by 1.4 MT and indicating that direct solar space heating has extensive untapped potential in the Windy City.

Machine learning shows great potential in building applications but faces challenges due to system complexity and extensive data requirements. Meta-learning, an approach that enables learning from prior experiences, offers a promising solution to these challenges. Currently, studies on its applications in the building domain remain fragmented, and no comprehensive review has been conducted. Therefore, this study presents the first systematic review that synthesizes existing studies, identifies current research limitations, and outlines future opportunities. This work aims to introduce meta-learning to the building research community, providing a foundation for researchers to understand its core principles and evaluate its applicability to specific research problems.

.The procedure used to determine the acceptance criteria ranges for time series results for ASHRAE Standard 140 is outlined, and the resulting ranges are detailed.

Traditional energy planning approaches often treat building performance and grid reliability as separate challenges, resulting in fragmented analyses and suboptimal planning outcomes. This study presents an Data-driven framework that links urban building data with electrical system design through a unified Building+Grid Reliability Metric (BGRM). The approach uses machine learning to infer missing building attributes, predict neighborhood peak power, and propagate retrofit and energy efficiency scenarios to assess grid impacts. By comparing predicted peaks against grid capacity limitations, the framework identifies when upgrades enhance performance or risk overload. This integrated methodology provides a scalable and practical pathway to guide tailored investments in both building efficiency and grid infrastructure.

Traditional design approaches for community energy systems often overlook the strong coupling between component sizing and control strategies. Most bi-level optimization frameworks treat sizing in an upper layer and controls in a lower layer, which can lead to suboptimal designs. At the same time, communities face increasing power outages and rising electricity costs, highlighting the need for resilient and cost-effective solutions. This paper presents a co-design optimization framework that simultaneously determines system sizing and control strategies to balance cost and resilience. The framework uses a Modelica model whose parameters are updated for each design option, allowing for the efficient evaluation of multiple configurations. The framework is demonstrated using a representative community energy system consisting of photovoltaic (PV) generation, a battery energy storage system (BESS), aggregated building electrical loads, and a grid connection. Resilience was assessed using three representative outage scenarios derived from historical outage data in Tampa, Florida. Each scenario reflects different levels of grid disruption. A cost–resilience index was used as the optimization objective, normalizing life-cycle cost (LCC) and critical unserved load (CUL) relative to a no-battery baseline. To benchmark performance, the co-design results were compared against a Pareto frontier generated by exhaustive search. The framework successfully identified Pareto-optimal solutions across cost-focused, resilience-focused, and balanced designs. A balanced configuration increased LCC by less than 11% relative to the baseline while reducing CUL by up to 92%, demonstrating the framework’s ability to provide high-quality design options that support informed stakeholder decisions on cost–resilience trade-offs.

Prototype models provide a basis for a variety of analysis, ranging from initial design evaluation to code development and measurement analysis. Office prototypes, in particular, have been widely used for developing codes and standards over the past decades, as offices buildings are numerous and consume a significant portion of energy in the commercial sector. Existing national and California-specific office prototypes have simplified core-perimeter zoning. This simplified zoning has been criticized for potentially misrepresenting internal loads, occupancy and consumption patterns, and HVAC interactions, such as improper turndown of flow rates and ventilation rates, which can reduce simulation accuracy. This study aims to address this gap by proposing a new set of office prototypes based on a comprehensive building stock assessment and collaboration with regulatory agencies in the state of California

This study develops residential building archetypes that represent houses with similar thermal features. These archetypes were developed to be utilized in an online application that predicts indoor temperature and alerts residents in vulnerable neighborhoods. The archetype development was a result of clustering the houses based on features such as long-side orientation, tree coverage, and temperature profile. Producing archetypes simplified the process of selecting suitable house models for each participant in the overall study. The selected archetypes had similar physical features such as building orientation, window-to-wall ratio, and number of stories above the ground. Finally, twelve archetypes were selected to be available for selection in the application to represent participants’ homes.

Indoor environmental quality has a profound impact on human comfort, well-being, and cognitive performance—particularly in learning environments where students spend much of their time. As educators and designers explore ways to create healthier and more engaging classrooms, indoor plant systems have gained attention for their ability to improve air quality, moderate humidity and temperature, and enhance occupants’ psychological well-being. Among these systems, hydroponics, a soil-less plant cultivation method using nutrient-rich water, has emerged as a scalable solution. Beyond its agricultural value, indoor hydroponics enhance indoor environments by improving air quality, moderating humidity and temperature, and promoting occupants’ well-being.

This study examines how indoor hydroponic systems influence student perceptions of environmental quality and well-being in high school classrooms. Surveys from classes with and without hydroponics (n = 143, n = 111) showed significant improvements in student perceptions of environmental quality, such as in air quality and visual comfort. Results suggest that hydroponics can promote biophilic, engaging learning environments. Limitations include reliance on self-reports and a single-site design. Future research should incorporate objective environmental measures and multi-classroom comparisons.

The 2025 edition of California’s Energy Code included air-to-water heat pumps (AWHP) as a prescriptive requirement, triggering the need to incorporate AWHPs into the performance path compliance software. This paper describes how the AWHP modelling was integrated and validated within California's Building Energy Code Compliance Software (CBECC).

EnergyPlus’s improved HeatPump:PlantLoop:EIR:Heating (AWHP-EIR) object was used to replace the prior method using the WaterHeater:HeatPump:PumpedCondenser object. The new approach enables autosizing, dynamic performance curves, and part-load operation with defined cut-off temperatures. Prototypes including Medium Office and Large School were tested across 16 California climate zones using EnergyPlus v24.1. Results showed that the new EnergyPlus object provided comparable compliance margins when using source energy and long-term system cost (LSC) metrics, allowing AWHPs to be modelled using CBECC in a reliable, validated manner for Title 24 2025 performance compliance.

The built environment contributes significantly to global greenhouse gas (GHG) emissions, accounting for nearly forty percent of the total annual emissions worldwide (Architecture 2030). As urbanization accelerates, the global building stock is projected to double by 2060, posing immense challenges for achieving sustainable development goals. This paper presents a framework for optimizing urban design parameters of new districts using the Computational City Design (CCD) tool. The tool integrates parametric modeling of urban form and environmental simulation to assess environmental metrics such as operational and embodied carbon, daylight and Universal Thermal Comfort Index (UTCI). A 500m×500m site of similar size and mid density character were selected from three cities Chicago, IL, Atlanta, GA, and Austin, TX to serve as a demonstration to explore relationships between street width, building footprint depth, building height and carbon efficiency, daylight availability and outdoor comfort of open spaces given context-specific constraints such as zoning regulations and climate conditions. There was correlation found between carbon efficiency, daylight availability with the tested urban design parameters that influence the floorplate depth and building height of different degrees. This conclusion helped inform a clear methodology for designing sustainable districts and could offer practical qualitative suggestions for future planning approaches.

Amid rapid urbanization and climate change, urban building energy modelling (UBEM) has become increasingly important for supporting decarbonization planning and policy decision-making. However, most existing UBEM frameworks rely heavily on building geometry, age, and use type, while overlooking façade material properties that directly influence heat transfer, solar absorption, and electricity demand. This study develops a street-view-based material recognition workflow to extract building façade materials from Google Street View imagery and integrate them into two common UBEM frameworks: physics-based simulation and data-driven modelling. Results show that incorporating façade material information improves model performance and strengthens the physical interpretability of UBEM by linking material properties to building energy behavior. Beyond prediction accuracy, the material-informed framework also provides an opportunity to examine spatial disparities in the built environment, including how uneven distributions of façade materials may contribute to urban energy inequality. This work demonstrates the value of material-aware UBEM for more interpretable, scalable, and equity-oriented urban decarbonization.