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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Retrofit-ability: A supplementary metric to inform energy efficiency policies and programs

Building Energy Use Intensity (EUI) has been commonly used to facilitate policy makers and utilities to design energy efficacy programs and help building managers prioritize investment in building upgrades. Buildings with higher EUIs are usually chosen as better candidates for retrofit. Our study of nearly one hundred buildings in Seattle’s Building Tune Up Accelerator program and thousands of buildings in the Asset Score database reveals that EUI (after weather/location/use type normalization) alone is not the best indicator of a building’s potential to save energy, especially for those buildings in the mid-range (30-70 percentile EUI). In this paper, we introduce the concept of “retrofit-ability” — a building’s real potential to reduce its energy use cost-effectively — as a supplementary metric to inform energy efficiency policy and investment. Currently, such potential analyses are predicted for a building stock using prototypical building models or statistical data. However, typical buildings or general historical data may not sufficiently represent building configurations in a portfolio and predict their saving potentials. We utilized machine learning to investigate how key building characteristics (such as envelope attributes, HVAC type, location, use type, etc.) affect a building’s improvement potentials and developed a low-cost method to quantify such potentials for a portfolio of buildings. The supplementary perspective provided by “retrofit-ability” highlights which building assets correlate most with a building’s energy savings potential across region, building use type and more.

Wang, Na↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Boiler Replacement with Air-Source Heat Pump Boiler and Electric Boiler Backup

Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy's ResStock TM and ComStock TM models over the past three years, the objective of this work is to produce national data sets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to energy efficiency variables that can be applied to buildings during modeling. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each timestep. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub hourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. This documentation focuses on a single end-use savings shape measure—boiler replacement by air-source heat pump boiler. This measure replaces space heating natural gas boilers by air-source heat pump boilers when applicable and helps quantify the decarbonization as well as the energy savings potential from the replacement. The measure resulted higher savings in natural gas consumption compared to the increase in electricity consumption, with a ratio of 2.9. The total natural gas energy consumption was reduced by 20%, whereas the total electricity consumption was increased by 2.5%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Boiler Replacement with Air-Source Heat Pump Boiler and Natural Gas Boiler Backup

Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy's ResStock (™) and ComStock (™) models over the past three years, the objective of this work is to produce national data sets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to energy efficiency variables that can be applied to buildings during modeling. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each timestep. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. This documentation focuses on a single end-use savings shape measure - boiler replacement with air-source heat pump boiler with natural gas boiler backup. This measure replaces natural gas boilers for HVAC application by air-source heat pump boilers when applicable and use natural gas boiler backup when the heat pump boiler could not operate due to outdoor air conditions which are below its cutoff temperature. This measure helps to quantify the decarbonization as well as the energy savings potential from the replacement. The measure resulted higher savings in natural gas consumption compared to the increase in electricity consumption, with a ratio of 3. The total natural gas energy consumption was reduced by 41%, whereas the total electricity consumption was increased by 5.3%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Console Water-to-Air Geothermal Heat Pump

Executive Summary Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models over the past several years, the objective of this work is to produce national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover most of the high-impact, market-ready (or nearly market-ready) measures. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility measure applied. It results in a time series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. This measure models the conversion of an existing heating, ventilating, and air-conditioning (HVAC) system to a series of “console” water-to-air heat pumps served by a ground heat exchanger. Console water-to-air geothermal heat pumps (GHP) are all-in-one packages that have no or minimal ductwork and serve individual spaces. Properly designed ground heat exchanger-coupled systems can offer benefits in energy efficiency relative to “conventional” HVAC systems, as well as facilitating beneficial electrification. Console GHPs can be coupled to a ground loop on the source side and can directly replace electric baseboard heaters or air-source packaged terminal heat pumps. Console GHPs can also bring in outdoor air for ventilation. This measure will be referred to throughout the document as the “Console GHP” upgrade.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2026

Calibration of urban building energy model using smart meter data for district peak load prediction

Urban building energy modeling (UBEM) is a powerful approach to assessing baseline building energy performance and retrofits with new technologies across building stocks in cities. However, the accuracy of UBEM is often constrained by the limited availability of reliable data about building characteristics and operations, such as envelope efficiency levels, HVAC system performance, and end-use load patterns. Existing research has performed UBEM calibration using annual or monthly energy consumption data, which falls short when higher-resolution time series applications are needed, such as peak load prediction for utility operation planning. This study presents a new framework for calibrating building energy models at urban scale using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6 % during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Using the calibrated models, we analyzed the demand flexibility potential of the district building stock as an application of UBEM calibration. The findings affirm the appropriate use of UBEM for peak electric load forecasting and demand side management at the utility distribution system level.

AMI data↗
DOE OSTI2022

Large scale energy analysis and renovation strategies for social housing in the historic city of Venice

Social houses built after the Second World War to accommodate workers and low-income families represent one of the major energy consumers and greenhouse gas emitters in the residential sector. Plans for their renovation are underway in all European countries, and the process is more complicated for Italian cities due to the lack of space and the large number of historical buildings. This study addresses this challenge by proposing a methodology to renovate a low-income district in the city of Venice using CityBES to model and evaluate energy conservation measures. CityBES is a web-based tool that allows users to employ urban building energy modeling for large-scale energy and retrofit analyses of building stocks. In the case study conducted for Venice's Santa Marta district, due to the particular context, four common energy conservation measures covering both the building envelope and heat generation boilers have been applied. The evaluation of energy-saving performances at the district level showed that the four measures together achieved 67% energy savings, an abatement in energy cost equal to 67%, and annual carbon dioxide emissions reduction of 1.1 MtCO 2 . Furthermore, the case study demonstrates a method and workflow replicable for energy retrofit analysis of building stocks in other historical districts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Commercial and Residential Hourly Load Profiles for All Typical Meteorological Year 3 (TMY3) Locations in the United States

One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High-quality end-use load profiles (EULPs) provide this information and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy funded a 3-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute-resolution load profiles for all major residential and commercial building types and end uses across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics

Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. To address these challenges, this paper introduces a novel algorithm, the Stacked Integration of Geospatial Hierarchical Typologies. This algorithm adapts a meta-learning framework to incorporate geospatial context into the predictive modeling process. We demonstrate the utility of the algorithm through two primary use cases: building use type classification and building height prediction. The algorithm consistently achieved or exceeded a 0.94 macro average F1 score across five geographically distinct countries for building use type classification. For building height prediction, it accurately predicted heights with a root mean square error of 3.01 in a comprehensive study using roughly 3.6 million buildings in Japan. These results underscore the benefits of integrating spatial hierarchies into machine learning models, enhancing both predictive accuracy and reliability in geospatial modeling. This work introduces a new algorithm to address the pervasive data sparsity issue in existing building stock datasets.

Adams, Daniel [ORNL] (ORCID:0000000196950577)↗
DOE OSTI2022

User Friendly Web-Based Tool to Assess the Energy Efficiency and Durability of Residential Wall Retrofits

According to the U.S. Department of Energy Windows and Building Envelope Research and Development Roadmap for Emerging Technologies, building envelope wall energy loss in the United States accounts for about 5.9 x 1010 kWh or 2 quads of energy annually, costing homeowners and occupants billions of dollars. Enclosure retrofits targeting these losses can save significant energy, reduce greenhouse gas emissions, and save occupants millions of dollars over time. Older homes, built before 1992 when DOE’s Building Energy Codes Program was established, represent approximately 68 percent of the residential building stock in the country, often having significant air leakage and inadequate insulation. Homes with little to no air sealing or insulation have heating and cooling losses that can represent a substantial portion of utility bills.High-performance building envelope retrofit systems are rarely selected for retrofit applications. Current solutions are expensive and/or unfamiliar to many designers, builders, contractors, and code officials and therefore are perceived as risky. The dominant perceived risk is durability specifically related to condensation and moisture accumulation in the building envelope component.The Building Science Advisor (BSA) is a rule-based expert system web-based tool that was originally developed to assist building professionals in designing energy efficient and durable wall systems for new construction. With the present focus being placed on upgrading the existing building stock, a retrofit module has been developed that, based on the location, existing construction, and planned retrofit strategy, provides recommendations on how to address the retrofit in a manner that will perform in accordance with IECC 2018 building code and not create a durability problem. This paper will describe the development of this tool and demonstrate its features and capabilities.

Desjarlais, Andre Omer↗
DOE OSTI2025

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Nweye, Kingsley↗
DOE OSTI2023

BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]

The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings. BuildingsBench is a platform for: - Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database. - Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. We provide an index-based PyTorch Dataset for large-scale pretraining, easy data loading for multiple real building energy consumption datasets as PyTorch Tensors or Pandas DataFrames, simple (persistence) to advanced (transformer) baselines, metrics management, and more.

Emami, Patrick↗
DOE OSTI2022

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault datasets for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling unit and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for the air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, two detailed AHU models, which included the single duct AHU and dual duct AHU developed in the Modelica language and HVACSIM+ were employed to carry out annual simulations of numerous common sensor faults, mechanical faults, and control sequence faults. The fault inclusive data were then validated by comparing fault effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. We report some lessons learnt during the efforts of validating the high volumes of the FDD data sets. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗
DOE OSTI2022

Development of a Annual Air Handling Unit Fault Dataset for FDD Tools: Lessons Learned and Considerations for FDD Developers

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault data for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling units and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for a single duct air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, a detailed AHU model was employed to carry out annual simulations of numerous common sensor and mechanical faults, which were then validated by comparing their effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗
DOE OSTI2025

Highly Resolved Reference Projections of Building Energy Use for the Contiguous United States: Building Sector Energy Baselines, Projection Methods, and Results

This report describes one methodology of projecting energy consumption of the US residential and commercial building sectors using NREL's ResStock™ and ComStock™ as well as growth rates derived from EIA's Annual Energy Outlook (AEO). The impetus for this work is to provide an intermediate method for compiling demand-side sectoral energy projections that is suitable for grid-scale analysis, such as NREL's Standard Scenarios. ResStock and ComStock are physics-based and statistically representative building stock models of the US residential and commercial sector, respectively. Using the 2012 actual meteorological year (AMY) weather data, the sectoral energy baselines are simulated and then segmented along key dimensions (e.g., geography, dwelling/building type). The segmented results are then scaled using the corresponding annual growth rates derived from the 2021 AEO reference case to produce energy projections out to 2050. The compiled result is a demand-side grid model (dsgrid) data set suitable for use in NREL's large-scale grid models, such as the Regional Energy Deployment System (ReEDS). This simple projection method does not endogenously represent how the building stock could evolve through time. Most notably, it does not reflect large-scale electrification, for example, the conversion of space heating, water heating, clothes drying, and cooking from primary fossil fuels to electricity, as this is not part of AEO's reference case assumptions. Nonetheless this approach is more resolved and potentially extensible compared to the current method used by Standard Scenarios's reference case, which augments a sector's total load based on a single growth rate from AEO.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A global comparison of building decarbonization scenarios by 2050 towards 1.5–2 °C targets

Buildings play a key role in the transition to a low-carbon-energy system and in achieving Paris Agreement climate targets. Analyzing potential scenarios for building decarbonization in different socioeconomic contexts is a crucial step to develop national and transnational roadmaps to achieve global emission reduction targets. This study integrates building stock energy models for 32 countries across four continents to create carbon emission mitigation reference scenarios and decarbonization scenarios by 2050, covering 60% of today’s global building emissions. These decarbonization pathways are compared to those from global models. Results demonstrate that reference scenarios are in all countries insufficient to achieve substantial decarbonization and lead, in some regions, to significant increases, i.e., China and South America. Decarbonization scenarios lead to substantial carbon reductions within the range projected in the 2 °C scenario but are still insufficient to achieve the decarbonization goals under the 1.5 °C scenario.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Heat Pump Rooftop Units

The heat pump rooftop units (RTUs) measure replaces gas furnace and electric resistance RTUs with high-efficiency heat pump rooftop units (HP-RTUs). The HP-RTUs are intended to be top-of-the line, including high-efficiency fans and heat pump systems. The fans are variable speed, allowing the HP-RTUs to operate as single-zone variable air volume systems. The heat pumps are also variable speed, allowing for high part load performance. All schedules in the existing RTUs are transferred to the new HP-RTUs for consistency. Furthermore, any energy efficiency features in the existing baseline RTUs such as energy recovery or economizers are also transferred to the new HP-RTUs for consistency. This measure is applicable to approximately 45% of the ComStock floor area. The HP-RTU measure demonstrates 10.3% total site energy savings (449 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗