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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integration of Electric Vehicle Charging Loads in Residential Building Stock Energy Modeling

The rapid adoption of electric vehicles (EVs) has resulted in significant new household electric loads that have the potential to change how energy costs are incurred by homeowners and the landscape of utility operations and energy infrastructure. Whereas adoption patterns and magnitudes of residential building and EV charging loads are influenced by distinct factors, the loads themselves are tightly coupled with the behavior of the individual occupants and EV owners.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Heat Pump Rooftop Units With Exhaust Air Energy Recovery

This technical report documents the Advanced Rooftop Unit Control End Use Savings Shapes (EUSS) measure, including providing a description of the technology, modeling methods, and the energy savings calculated from applying the measure to applicable across the US commercial building stock using ComStock to quantify its potential. This measure replaces gas furnace and electric resistance rooftop units with high-efficiency variable speed heat pump rooftop units (HP-RTU) that include exhaust air heat or energy recovery. The measure uses the same assumptions and technology as the variable speed HP-RTU measure from EUSS 2023 release 1 but adds energy recovery to precondition outdoor ventilation air to reduce HVAC loads. The HP-RTU compressor lockout temperature is modeled as 0 degrees Fahrenheit; below this temperature, the heat pump is set to shut off. The unit is sized based on the design cooling loads, with backup electric resistance heating addressing any remaining loads including heating hours below the compressor lockout temperature when there is no heat pump heating.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Leveraging NREL's ResStock & ComStock Dataset to Evaluate Building Stock Electrification: Preprint

Residential and commercial buildings accounted for 40% of U.S. energy consumption in 2022 and represent a significant opportunity for decarbonization through energy efficiency and electrification, and for grid planning. Building stock energy modeling is a powerful tool that can evaluate what-if scenarios as utilities, municipalities, policymakers, building owners and others work towards equitable building decarbonization and climate goals. This presentation will highlight several high-impact use cases of the National Renewable Energy Laboratory (NREL)'s highly granular, bottom-up building stock energy modeling tools, ResStock and ComStock. These use cases cover a wide range of project scale, from neighborhood electrification analysis and municipality long-term energy planning, to state energy code development and national policy evaluation. This presentation will showcase specific real-world applications for which ResStock and ComStock have been utilized across the country, including California codes and standards cost-effectiveness analysis, New York City affordable housing electrification cost gap analysis, and California targeted electrification and gas decommissioning analysis. For each use case, this presentation will illustrate how ResStock and ComStock played a crucial role in accurately characterizing regional building stocks, providing discrete and aggregated end-use load shapes, and calculating lifecycle consumption, emissions, and costs for a variety of building electrification strategies and scenarios. Finally, this presentation will demonstrate how the data provided by ResStock and ComStock can help unlock significant outcomes for these use cases, including but not limited to, customer bill impact, incentive and program design, and energy equity analyses.

building stock modeling↗
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

ResStock Technical Reference Documentation (V.3.3.0)

ResStock™ is the best-in-class building stock energy model for simulating and publishing energy use, utility bills, and greenhouse gas emissions from the residential sector of the U.S. ResStock answers two primary questions: (1) How is energy used in the U.S. residential building stock? and (2) What is the timeseries aggregate impact of energy technologies? Specifically, ResStock quantifies energy use across geographical locations, demographic groups, building types, fuels, end uses, and time of day. Additionally, it details the impact of efficiency or electrification measures: total changes in the amount of energy used by measure; where or in what use cases efficiency or electrification upgrade measures save energy; when or at what times of day savings occur; and which building stock or demographic segments have the biggest savings potential. This model, and the datasets it produces, are foundational to identifying pathways to affordable and equitable decarbonization of the U.S. residential buildings sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Ten questions on building stock modeling to inform energy efficiency and sustainability

To enhance economic competitiveness and ensure energy efficiency, resilience, and security, cities and governments are adopting technologies and strategies to improve their existing building stocks. This approach aims to reduce energy use, improve energy affordability, and ensure a reliable power supply while safeguarding occupants during extreme weather events that may disrupt energy services. The effectiveness of these solutions will depend on building stock characteristics, use patterns, weather conditions, evolving technologies and their markets, and a city’s socio-economic conditions. This paper presents ten questions and answers that highlight the most important issues regarding the use of building stock modeling as a powerful tool to provide insights for informing stakeholders’ actions and decision-making on energy efficiency, costs reduction, and resilience of buildings in cities. Building stock modeling should build upon the fit-for-purpose framework, balancing the use case accuracy requirements, level of complexity, and needed resources (expertise, compute). The advancements in Artificial Intelligence (AI), the increasingly available open dataset of building stock in cities, and the more affordable powerful computing will accelerate the adoption of building stock modeling across scales by researchers and practitioners to inform decision making on sustainability and efficiency.

AI↗
DOE OSTI2023

BuildStockQuery [SWR-23-58]

BuildStockQuery is a python library designed to simplify and streamline the process of querying massive, terabyte-scale datasets generated by ResStock(TM). ResStock (SWR-19-15) is a U.S. DOE-supported, NREL-built, national residential building energy stock model that enables a new approach to large-scale residential energy analysis across the U.S. by combining large public and private data sources, statistical sampling, detailed sub-hourly building simulations, and high-performance computing. BuildStockQuery offers an intuitive Object-Oriented Programming (OOP) interface to the ResStock output dataset allowing users to easily perform common queries and receive results in familiar pandas DataFrame format, abstracting away the need for complex SQL query. By initializing a query object with the pertinent Athena database and table names, users can easily query for various kinds of insights, for example, timeseries electricity for an end use for a given state grouped by building types.

Adhikari, Rajendra↗
DOE OSTI2024

ResStock 2024.2 Dataset [Slides]

In the ResStock 2024.2 dataset, ResStock runs are used to create "what-if" scenarios including energy efficiency measures such as heat pumps, envelope improvements, and electrification of appliances. This dataset release includes 15 measure packages across two weather years and incorporates ResStock improvements in variable speed heat pump modeling, geothermal heat pump modeling, and housing characteristic data updates from RECS 2020.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Component-Level Analysis of Heating and Cooling Loads in the U.S. Residential Building Stock

The residential building sector accounts for a substantial portion of total energy consumption in the United States and offers a significant opportunity for energy reduction and decarbonization through improvements in energy efficiency. Heating and air conditioning are the primary contributors to residential energy usage and electricity system peak demand. However, due to the diversity of the housing stock and the complexity of factors affecting heating and cooling demand, identifying the relative contributions to heating and cooling loads poses challenges. To address this, we applied the ResStock analysis tool to simulate 550,000 building energy models, providing statistical representation of residential buildings in the contiguous United States. We introduced outputs that quantified the heating and cooling influence of different components of a home, such as air leakage, envelope components (ceilings, walls, windows, foundations), internal heat gains from people, lighting, plug loads, and duct losses and gains. Leveraging the granularity of ResStock, we present a dataset to enable deeper understanding of the contributors to heating and cooling loads as a function of housing characteristics such as location, envelope efficiency, and building type. This work aims to support prioritization of research and development and informed decision-making for residential building decarbonization.

building simulation↗
DOE OSTI2024

ComStock™ 2024 Release 1 [SWR-19-33 and SWR-20-32]

ComStock™ is an NREL model of the U.S. commercial building stock. The model takes some building characteristics from the U.S. Department of Energy's (DOE's) Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics. This repository contains the source code used to build and execute ComStock models, including upgrade scenarios. In addition, the sampling of buildings characteristics used for the initial ComStock (V1.0) release is provided. The ComStock model is under active calibration and development, which is publicly visible on this repository. Execution of the ComStock workflow is managed through the buildstockbatch repository, a shared asset of ResStock™ and ComStock™ , specifically developed to scale to execution of tens of millions of simulations through multiple infrastructure providers. The dataset output from the initial ComStock (V1.0) release can be found at the accompanying ComStock data viewer website and additional information about ComStock found on the NREL Buildings Website. For more details about ongoing model development please consult the End Use Load Profiles website. ComStock is a direct result of the NREL residential stock modeling tool ResStock™ (recipient of a R&D100 award) and was inspired by the high-fidelity solar & storage adoption model dGen™. Additionally, this tool would not be possible without the decades of work undertaken by the OpenStudio® and EnergyPlus® visionaries and contributors, significant funding, feedback and support from the Los Angeles Department of Water and Power, and the Department of Energy's Building Technology Office ongoing support of and investment in building energy modeling software. is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.

Horsey, Henry↗
DOE OSTI2025

ComStock Measure Documentation: Ideal Thermal Air Loads

This study provides hypothetical thermal heating and cooling loads for ComStock models representing the U.S. commercial building stock. This measure scenario removes all HVAC models from the baseline ComStock building model and instead uses "ideal air" to meet loads. "Ideal air" can be thought of as an HVAC unit that mixes air at the zone exhaust condition with the specified amount of outdoor air, and then adds or removes heat and moisture at 100% efficiency to produce a supply air stream at the specified conditions. The resulting ideal thermal loads are represented under the "district" fuel type for both heating and cooling and can be found in both annual and timeseries results in the ComStock public dataset. This measure scenario does not represent any real technology or improvement, but rather, serves as a resource for thermal heating and cooling loads for buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Electric Vehicle Adoption With Level 1 Charging

This report is part of a series describing different ResStock (TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Electric Vehicle Adoption With Level 1 Charging" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Electric Vehicle Adoption With Level 2 Charging

This report is part of a series describing different ResStock (TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Electric Vehicle Adoption With Level 2 Charging" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility

This report is part of a series describing different ResStock measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Efficient Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility

This report is part of a series describing different ResStock measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Efficient Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Efficient Electric Vehicle Adoption With Level 2 Charging

This report is part of a series describing different ResStock (TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Efficient Electric Vehicle Adoption With Level 2 Charging" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗