DATA HAS BEEN MIGRATED TO https://data.ess-dive.lbl.gov/view/doi:10.15485/2283980

The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1".

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2".

1. Data, separated by state - minimalist list of each building (rows) for the following fields (columns)
   1. ID - unique building ID
   2. Footprint2D - lat/lon vertices of building footprint
   3. State_Abbrev - Abbreviation for the from which building is located
   4. Area - estimate of total conditioned floor area (ft²)
   5. Area2D - footprint area (ft²)
   6. CZ - ASHRAE Climate Zone designation
   7. Height - building height (ft)
   8. NumFloors - number of floors (above-grade)
   9. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)
   10. CZ - US climate zone designation
   11. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards
   12. Standard - building vintage (determined by building age)

This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA).

About the Project Increasing energy productivity holds some of the greatest possibilities for enhancing the welfare countries get out of their energy systems. It also recasts energy efficiency in terms of boosting competitiveness and wealth, more powerfully conveying its profound benefits to society. KAPSARC and UNESCWA have initiated this project to explore the energy productivity potential of the Arab region, starting with the six GCC countries and later extending to other countries. Aimed at policymakers, this project highlights the social gains from energy productivity investments, where countries are currently at, and pathways to achieving improved performance in this area. Key Points This paper describes our analysis of the cost-effectiveness of designing and retrofitting residential buildings in Bahrain and outlines our analytical approach. The study focuses on residential buildings since households consume more than 48 percent of electricity used in the country. As expected, residential buildings constitute the vast majority of Bahrain’s building stock, with about 76 percent of the total and projected annual growth in energy consumption of around 3 percent in the next few years. The optimization analysis outlined in this paper assesses the potential benefits from retrofitting both individual buildings and the entire national building stock, as well as the benefits of applying proven measures and technologies to improve the energy efficiency of the building sector. Our conclusions are: The development and enforcement of a more stringent energy efficiency code can potentially improve the energy efficiency of the new building stock with a reduction of more than 320 GWh in annual electricity consumption and 87 MW in peak demand. Retrofitting the existing building stock in Bahrain has the potential to cost-effectively reduce energy consumption in the building sector by 62 percent, with a 55 percent reduction in peak electricity demand compared with the business as usual scenario. The avoided costs of building new power plants would be sufficient to offset the implementation costs for a basic level of energy retrofitting of existing residential buildings. We estimate that as much as 31,700 job-years of employment can be created when retrofitting the existing building stock. More than 3,000 jobs would be needed annually in order to retrofit existing buildings over a 10-year period.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. The Residential Existing Homes Program is a market transformation program that uses Building Performance Institute (BPI) Goldstar contractors to install comprehensive energy-efficient improvements. The program is designed to use building science and a whole-house approach to reduce energy use in the State’s existing one-to-four family and low-rise multifamily residential buildings and capture heating fuel and electricity-related savings. The Program provides income-based incentives, including an assisted subsidy for households with income up to 80% of the State or Median County Income, whichever is higher to install eligible energy efficiency improvements including building shell measures, high efficiency heating and cooling measures, ENERGY STAR appliances and lighting. D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Many factors influence the degree to which estimated savings are realized, including proper calibration of the savings model and the savings algorithms used in the modeling software. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. Beginning November 2017, the Program requires the use of HPXML-compliant modeling software tools and data quality protocols have been implemented to more accurately project savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. The New York Residential Existing Homes (One to Four Units) dataset includes the following data points for projects completed during Green Jobs Green-NY, beginning November 15, 2010: Home Performance Project ID, Home Performance Site ID, Project County, Project City, Project Zip, Gas Utility, Electric Utility, Project Completion Date, Customer Type, Low-Rise or Home Performance Indicator, Total Project Cost (USD), Total Incentives (USD), Type of Program Financing, Amount Financed Through Program (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Volume of Home, Number of Units, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Energy Savings $ Estimate (USD), Homeowner Received Green Jobs-Green NY Free/Reduced Cost Audit (Y/N). How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

This project is a three-year, four-laboratory collaboration to collect and curate a handful of high-resolution building systems datasets that have broad applicability to address highest-impact use cases. We will collect and curate high-resolution, well-calibrated time series of building operational and indoor/outdoor environmental data, which are crucial to understanding and optimizing building energy efficiency performance and demand flexibility capabilities as well as benchmarking energy algorithms.

The work presented in this report advances the goals of the U.S. Department of Energy Building America program by improving the energy peformance of affordable and market-rate housing. Southface Energy Institute (Southface), part of the U.S. Department of Energy Building America research team Partnership for Home Innovation, worked with owners and builders with various market constraints and ultimate goals for three projects in three climate zones (CZs): Savannah Gardens in Savannah, Georgia (CZ 2); JMC Patrick Square in Clemson, South Carolina (CZ 3); and LaFayette in LaFayette, Georgia (CZ 4). This report documents the design process, computational energy modeling, construction, envelope performance metrics, long-term monitoring results, and successes and failures of the design and execution of these highperformance homes. The JMC Patrick Square project is a single floor with 1,828 ft2 of conditioned living space, three bedrooms, two bathrooms, and an attached two-car garage. This small-scale production builder wanted to increase its level of energy efficiency beyond its current green building practices, including bringing ducts into conditioned space. The team met this goal through a combination of upgrade measures and achieved many Zero Energy Ready Home program requirements. Monitoring the four ducted HPWHs in LaFayette and one in Savannah revealed that HPWH exhaust air impacts attic air during HPWH runtime only, and attic conditions return to previous levels shortly after the HPWH turns off. The HPWH did not appear to impact the loads on the heating and cooling systems, which were also placed in the attic. HPWHs should not be considered dehumidifiers if one is needed in an attic or basement/crawlspace. Ducting the HPWHs did not negatively impact performance compared to other published data of field performance. Changing duct configurations also did not alter the coefficient of performance. HPWHs in efficiency mode (heat pump only) could satisfy hot water demand for most residents. This mode maximizes energy efficiency. Adding .5 in. of insulated sheathing using the Huber ZIP System R Sheathing reduced peak summer temperatures and increased minimum winter temperatures inside the wall assemblies compared to the neighboring home. The neighboring home experienced significantly more risk of condensation and failed the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Standard 160-2009: Criteria for Moisture-Control Design Analysis in Buildings. Despite the fact that energy modeling predicted only a 2% annual savings from the insulated sheathing, preliminary data indicate that reduced heating, ventilating, and air-conditioning runtimes and energy consumption attributed to this measure provide significantly greater savings. Additional research is necessary.

The ResStock Analysis Tool was developed by NREL with support from the U.S. Department of Energy to provide a new approach to large-scale residential analysis by combining large public and private data sources, statistical sampling, detailed sub hourly building simulations, and high-performance computing. This combination achieves unprecedented granularity and accuracy in modeling the diversity of the housing stock and the distributional impacts of building technologies in different communities.

The annual baseline energy results from a national-scale ResStock run use typical meteorological year 3 (TMY3) files for energy simulations. Results include heating and cooling loads for individual components of each building. Component loads describe the heating/cooling load that can be attributed to specific elements of a home, such as heat transfer through walls or internal gains. Additionally, these results include the standard ResStock outputs for housing characteristics and numerous energy outputs by end-use and fuel.

A snapshot of the ResStock version used to produce this data, including a configuration file for the run can be found using the Source Code resource link.

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

Data is provided for 2,555,152 buildings located within the boundary of Arizona in the United States:

Data (1.48GB *.csv) - Arizona 2,555,152 building information data with simulation results separated by county. (Simulation results are for June 1st-August 31st, 2020)

Building Information Data Fields:

ID

CZ

Centroid

State_Abbr

Footprint2D

Height,Area2D

BuildingType

NumFloors

Area

Standard

NumWalls

WWR_surfaces

Energy Simulation Data Fields:

Electricity_Facility[kBTU]

NaturalGas_Facility[kBTU]

Heating_Electricity[kBTU]

Cooling_Electricity[kBTU]

Heating_NaturalGas[kBTU]

Heating_Total[kBTU]

WaterSystems_Electricity[kBTU]

Lighting_Electricity[kBTU]

Equipment_Electricity[kBTU]

Fans_Electricity[kBTU]

Pumps_Electricity[kBTU]

HeatRejection_Electricity[kBTU]

HeatRecovery_Electricity[kBTU]

Surface_Outside_Face_Heat_Emission[GJ]

Zone_Exfiltration_Heat_Loss[GJ]

Zone_Exhaust_Air_Heat_Loss[GJ]

Heat_Rejection_Energy[GJ]

Anthropogenic_Emissions[GJ]

This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).

This page provides data on the performance of the Register a high-rise residential building service. The data is updated every 3 months.

International Journal of Building Performance Simulation Abstract & Indexing - ResearchHelpDesk - The Journal of Building Performance Simulation (JBPS) aims to make a substantial and lasting contribution to the international building community by supporting our authors and the high-quality, original research they submit. The journal also offers a forum for original review papers and researched case studies. JBPS welcome building performance simulation contributions that explore the following topics related to buildings and communities: Theoretical aspects related to modelling and simulating the physical processes (thermal, airflow, moisture, lighting, acoustics). Theoretical aspects related to modelling and simulating conventional and innovative energy conversion, storage, distribution, and control systems. Theoretical aspects related to occupants, weather data, and other boundary conditions. Methods and algorithms for optimizing the performance of buildings and communities and the systems which service them, including interaction with the electrical grid. Uncertainty, sensitivity analysis, and calibration. Methods and algorithms for validating models and for verifying solution methods and tools. Development and validation of controls-oriented models that are appropriate for model predictive control and/or automated fault detection and diagnostics. Techniques for educating and training tool users. Software development techniques and interoperability issues with direct applicability to building performance simulation. Case studies involving the application of building performance simulation for any stage of the design, construction, commissioning, operation, or management of buildings and the systems which service them are welcomed if they include validation or aspects that make a novel contribution to the BPS knowledge base. The following topics are outside the journal's scope and will not be considered: Case studies involving the routine application of commercially available building performance simulation tools that do not include validation or aspects that make a novel contribution to the knowledge base. The structural performance of buildings and the durability of building components. Studies focused on the performance of buildings and the systems that serve them, rather than on modelling and simulation. The Journal of Building Performance Simulation (JBPS) Journal operates a double-blind peer review and all submissions are to be made online using the JBPS ScholarOne site. For more information on contributing a manuscript visit our Instructions for Authors page. Society information Journal of Building Performance Simulation is the Official Journal of the International Building Performance Simulation Association (IBPSA). Members of IBPSA can receive an individual print subscription at a special society members rate. Please see the pricing or subscribe page for details. (JBPS) Journal information Journal of Building Performance Simulation is abstracted and indexed by: British Library Inside, Cambridge Scientific Abstracts, EBSCO Databases and Scopus. International Building Performance Simulation Association (IBPSA) and our publisher Taylor & Francis make every effort to ensure the accuracy of all the information (the "Content") contained in our publications. However, International Building Performance Simulation Association (IBPSA) and our publisher Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by International Building Performance Simulation Association (IBPSA) and our publisher Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. International Building Performance Simulation Association (IBPSA) and our publisher Taylor & Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to, or arising out of the use of the Content. RG Journal Impact: 2.14 * *This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive. RG Journal impact history 2018 / 2019 2.14 2017 2.10 2016 2.66 2015 2.36 2014 2.55 2013 3.05 2012 1.85 2011 1.69 2010 1.12 2009 0.61 Journal of Building Performance Simulation (JBPS) Additional details Cited half-life 3.50 Immediacy index 0.66 Eigenfactor 0.00 Article influence 0.61 Other titles Journal of building performance simulation ISSN 1940-1493 OCLC 173313893 Material type Periodical, Internet resource Document type Journal / Magazine / Newspaper, Internet Resource Journal of Building Performance Simulation (JBPS) details SJR

The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with vector layers is highly challenging in practice. Although vector layers provide accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values are represented as raster layers with 30m cell size covering the 48 conterminous states, to better support incorporation of building footprint data into large-extent modelling. This Project is funded by NASA’s Biological Diversity and Ecological Forcasting program; Award # 80NSSC18k0341

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). Two sets of sample data are provided for 2,555,152 buildings located within the boundary of Arizona in the United States: Data (846.3MB .csv) - minimalist list of each building (rows) for the following fields (columns) • ID - unique building ID • Centroid - building center location in latitude/longitude (from Footprint2D) • Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) • State_abbr - state name • Area - estimate of total conditioned floor area (ft2) • Area2D - footprint area (ft2) • Height - building height (ft) • NumFloors - number of floors (above-grade) • WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) • CZ - ASHRAE Climate Zone designation • BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards • Standard - building vintage • Sample Models (114GB.zip by county) - OpenStudio and EnergyPlus building energy models named according to ID This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).

Energy Efficiency for Commercial Buildings Market Outlook

The global market size for Energy Efficiency for Commercial Buildings was valued at approximately USD 80 billion in 2023, and is forecasted to reach around USD 130 billion by 2032, exhibiting a CAGR of about 5.5% during the forecast period. The primary growth drivers for this market include increasing regulatory pressures, rising energy costs, and the growing adoption of sustainable building practices. As governments worldwide continue to introduce stringent energy efficiency standards and incentives, the market for energy-efficient solutions in commercial buildings is expected to experience significant growth.

One of the primary growth factors for the Energy Efficiency for Commercial Buildings market is the increasing awareness and emphasis on environmental sustainability. Governments and organizations globally are adopting stricter environmental regulations and standards, aiming to reduce carbon footprints and promote sustainable practices. This has led to heightened demand for energy-efficient solutions, such as advanced lighting systems, energy management systems, and efficient HVAC systems. Companies are increasingly recognizing the long-term cost benefits and environmental advantages of incorporating energy-efficient technologies in their buildings.

Another significant driver is the escalating energy costs, which have pushed businesses to seek more efficient solutions to reduce operational expenses. The cost of energy is a substantial part of the operating costs for commercial buildings, and energy-efficient technologies can lead to significant savings. The adoption of these technologies not only helps in cost reduction but also improves the overall energy performance of the buildings, making them more attractive to tenants and investors. This economic incentive is prompting a growing number of businesses to invest in energy-efficient upgrades.

The advancement and integration of smart building technologies also play a crucial role in driving the market. Smart building technologies, such as IoT-enabled devices, intelligent energy management systems, and data analytics, are transforming the way energy efficiency is managed in commercial buildings. These technologies enable real-time monitoring, data-driven decision-making, and automation of building systems, leading to enhanced energy efficiency and operational effectiveness. As these technologies continue to evolve and become more accessible, their adoption in commercial buildings is expected to rise significantly.

From a regional perspective, North America and Europe are the leading markets for energy efficiency in commercial buildings, driven by stringent regulatory frameworks, high energy costs, and a strong focus on sustainability. The Asia Pacific region is also witnessing significant growth, fueled by rapid urbanization, increasing economic activities, and supportive government policies. The demand for energy-efficient solutions is expected to rise substantially in developing economies within this region, as they strive to balance their economic growth with environmental sustainability.

Component Analysis

The component segment in the Energy Efficiency for Commercial Buildings market is broadly categorized into Lighting, HVAC, Building Controls, Water Efficiency, and Others. The lighting segment has been a significant contributor to the market, driven by advancements in LED technology, which offers considerable energy savings compared to traditional lighting solutions. The adoption of automated lighting controls and smart lighting systems further enhances energy efficiency, reducing operational costs and improving the indoor environment.

HVAC systems are another critical component, as they account for a substantial portion of energy consumption in commercial buildings. Modern, energy-efficient HVAC systems are designed to provide optimal indoor climate control while minimizing energy use. Innovations such as variable refrigerant flow (VRF) systems, high-efficiency chillers, and advanced heat recovery systems are gaining traction in the market. These systems not only reduce energy consumption but also enhance the overall comfort and productivity of building occupants.

Building controls play a pivotal role in energy efficiency by enabling centralized control and automation of various building systems. Advanced building control systems integrate with HVAC, lighting, and other building systems to optimize energy use, based on real-time data and analytics. This integration leads to significant energy savings, a

Simulation results of high-rise residential buildings of 11,21 and 31 floors in a dense urban tropical climate

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

To reduce computational demand for representing energy-related performance of a specific portion of the building sector, AutoBEM can dynamically generate archetype buildings along with multipliers based on conditioned floor area. This was performed for all residential and commercial buildings in Clark County, Nevada. For the original dataset of all 589,586 buildings, please refer to http://doi.org/10.5281/zenodo.4552901.

Critical note: Building multipliers and models will be updated soon.

Two sets of data are provided for 129 archetype models and 120 multipliers for Clark County (Las Vegas), Nevada in the United States. There are 129 models and only 120 multipliers since 9 building type*vintage categories contain only 1 building:

1. Data (42kB *.csv) - minimalist list of each archetype building (rows) for the following fields (columns)
   1. County - county name
   2. State - state name
   3. CZ - ASHRAE Climate Zone designation
   4. Clim_Zone - text label of climate zone
   5. est_year - estimated year of construction
   6. est_commercial - estimated building type (0=residential, 1=commercial)
   7. Centroid - building center location in latitude/longitude (from Footprint2D)
   8. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...)
   9. Height - building height (meters)
   10. ID - unique building ID
   11. Area2D - footprint area (ft²)
   12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards
   13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, average 40% for commercial buildings varying by building type)
   14. NumFloors - number of floors (above-grade)
   15. Area - estimate of total conditioned floor area (ft²)
   16. Standard - building vintage
   17. Area_Multiplier - amount simulation outputs should be multiplied by to represent all 589,586 buildings in Clark County
2. Models (2.6MB *.zip) - EnergyPlus building energy models named according to ID

Overview and Intended Use Cases

These scenarios establish a range of futures for U.S. buildings sector energy use and CO₂ emissions to 2050 using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO).

Scout benchmark scenario data are suitable for the following example use cases:

• setting high-level policy goals for the U.S. buildings sector to 2050 (e.g., X% building CO₂ emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050);

• exploring the effects of key dynamics driving U.S. buildings sector energy and CO₂ emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market);

• determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO₂ emissions reductions to 2050 under a given set of assumptions; and/or

• identifying the energy and emissions impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio.

Scenario Summary

A total of 8 scenarios explore the effects of changes across both the demand- and supply-side of building energy use on annual U.S. building energy use and CO₂ emissions from 2022–2050. Scenarios are organized into three groups representing low, moderate, and best-case potentials for building decarbonization, respectively. Individual scenarios are distinguished by four input dimensions:

• market-available technology performance range (EE): the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”;

• load electrification (EL): the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment;

• early retrofits (R): the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and

• power grid (P): the annual average CO₂ emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2022–2050), resolved by grid region.

Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Summary_Execution” XLSX.

Results data are reported as an annual time series (2022–2050) at both a national and regional (EMM grid region) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information.

What's New in This Version

This set of benchmark scenarios carries forward elements of past versions of this dataset (previously titled “Scout Core Measures Scenario Analysis” and summarized in this paper) while also streamlining the scenario design and reflecting updated policy ambitions regarding deployment of building efficiency, flexibility, and electrification as well as power grid evolution. Three scenarios in the current dataset map back to past scenarios:

• Scenario 2.1: EE1.P1 -> Scenario 6: HR 1T-2T-3T

• Scenario 2.2: EE1.ELe1a.P1 -> Scenario 7: HR 1T-2T-3T FS0

• Scenario 2.3: EE1.ELe1b.P1 -> Scenario 8: HR 1T-2T-3T FS20

The following scenario features are new in this dataset:

• Measures in the “best available” tier are deployed with load flexibility features that are based on a previous study of the U.S. building-grid resource. Past versions reflected only efficiency and electrification measures.

• The effects of progressively raising the market-available technology performance “floor” are explored by including reference case technologies in the measure competition and assuming codes/standards remove these technologies from the market-available mix beginning in a certain year. Past versions only explored the effects of a higher technology “ceiling”.

• Increasing ambitions for the top “Prospective” tier of measure performance are reflected. Past versions mapped much of this measure tier to the 2016 BTO MYPP.

• Electrification is explored via both endogenous and exogenous model settings, where the former is based on Scout’s economic measure competition models and the latter is based on fuel switching scenarios developed by Guidehouse for the BTO E3 Initiative. Past versions only explored endogenous electrification.

• Inefficient electrification is explored (past versions did not explore inefficient electrification). In such cases, consumers switch fossil-based heating and water heating equipment to a mix of electric resistance and heat pump technologies, with the mix determined by AEO 2021 Reference Case sales share data for these technologies.

• The effects of early retrofitting behavior are isolated by running all but one scenario without early retrofits. Past versions assumed a 1% early retrofit rate.

• More aggressive grid scenarios are explored using Brattle’s GridSIM model. Two scenarios are included—an 80% decarbonized grid by 2050 and 100% decarbonized grid by 2035. Past versions used the AEO 2018 “$25 carbon allowance fee” side case, which reached ~73% carbon-free electricity generation (including nuclear) by 2050.

These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including Scout, ResStock, and the Commercial Building Prototype Models, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020. Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.

The four ZIP files that make up this data record are interpreted as follows:

Measure_Data.zip: Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available here.

Results_Data.zip: Includes the main and side case results data. Baseline-case outcomes, which are consistent with the EIA 2019 Annual Energy Outlook, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in each of these first three folders. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) regions (p.6) of focus are stored in "Sector_Level_8760s".

Source_Code.zip: Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout Quick Start Guide; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described here. See Scout's Local Execution Tutorials for more details on how to develop Scout inputs and outputs.

Supporting_Data.zip: Includes supplemental data files provided by EIA that describe key inputs and outputs to the Electricity Market Module in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json".

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Zero-Energy Buildings Market Size 2025-2029

The zero-energy buildings market size is forecast to increase by USD 188.26 billion at a CAGR of 21.4% between 2024 and 2029.

The Zero-Energy Buildings (ZEB) market is experiencing significant growth due to the increasing global focus on sustainability and the shift towards net-zero emissions. Zero-Energy Buildings are structures that produce as much energy as they consume, primarily through renewable sources such as solar power. This trend is being driven by various factors, including stringent energy efficiency regulations, rising energy costs, and growing awareness of the environmental impact of traditional energy sources. However, the intermittent nature of solar power poses a challenge to the widespread adoption of ZEBs. To mitigate this issue, advancements in energy storage technologies and smart grid systems are gaining traction. These solutions enable the efficient management and distribution of energy, ensuring a consistent power supply and maximizing the benefits of renewable energy. Companies seeking to capitalize on this market opportunity should focus on developing innovative energy storage and management solutions, while also collaborating with stakeholders across the value chain to create a sustainable and interconnected energy ecosystem.

What will be the Size of the Zero-Energy Buildings Market during the forecast period?

Request Free SampleThe market represents a significant growth opportunity in the global construction sector, driven by increasing environmental awareness and the need to reduce carbon emissions. Zero-energy buildings, also known as net-zero energy structures, consume only as much energy as they produce through renewable sources, such as solar panels, wind power, and geothermal energy systems. This market is poised for expansion as the building sector seeks to contribute to carbon neutrality and mitigate the impact of global average temperature increases and climatic changes. Key trends in the market include the integration of energy-efficient appliances, green construction technology, and the use of natural ventilation, air sealing, and insulation in walls and roofs. Renewable energy systems, such as solar panels and wind power, are becoming increasingly cost-effective and accessible, making zero-energy buildings an attractive option for both new construction and retrofits. Additionally, the market is being fueled by the growing demand for educational facilities and other institutions to lead by example in environmental protection. Overall, the market is expected to continue growing as businesses and governments prioritize energy consumption reduction and carbon emissions mitigation.

How is this Zero-Energy Buildings Industry segmented?

The zero-energy buildings industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. SourceSolar energyBiogasOthersProductHVAC and controlsInsulation and glazingLighting and controlsWater heatingComponentSolutions and servicesEquipmentApplicationPublic and commercial buildingsResidential buildingsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaMiddle East and AfricaSouth America

By Source Insights

The solar energy segment is estimated to witness significant growth during the forecast period.The Zero-Energy Buildings (ZEB) market is experiencing notable growth in the solar energy segment between 2025 and 2029. This expansion is driven by technological advancements and heightened environmental consciousness. Solar power is a preferred renewable energy source for ZEBs due to its efficiency in on-site energy generation. In January 2024, Canadian Solar introduced a new line of high-performance solar panels tailored for residential applications, improving energy generation and lowering installation expenses. This innovation underscores the trend of incorporating renewable technologies into architectural designs. In March 2024, First Solar increased its US manufacturing capacity to cater to the escalating demand for solar modules in net-zero energy projects. The integration of greenhouse gas reduction technologies, such as solar and wind power, into building structures is crucial in mitigating carbon emissions and contributing to sustainable development. Additionally, energy efficiency improvements in HVAC systems, greenhouse gases, and energy-efficient appliances contribute to the carbon emissions reduction in the construction sector. Nearly Zero-Energy Buildings (nZEBs) and Green buildings employ green construction technology, natural ventilation, air sealing, and energy management systems to ensure indoor air quality and reduce energy consumption. This environmental conservation approach is essential in combating global a

The Developer Services Review Program is designed to meet the special needs of owners, developers, architects and contractors working on moderately- to highly-complex construction or renovation projects. Eligible projects include high-rise buildings, mercantile buildings with more than 150,000 square feet, other occupancies with more than 80,000 square feet, buildings with foundations deeper than 12 feet, and residential projects that contain more than 25 units. This metric tracks the average number of days DOB takes to process individual Developer Services Permits, grouped by the week the permit was processed. The target average process time is within 89 days. Click here for more information about DOB’s Developer Services Program.

Abstract This study analyses the possibility of achieving negative energy demands in single-family housing (Plus-Energy House). Through the integration of energy efficiency measures jointly with active solar systems, it is possible to obtain high performance energy models. In order to demonstrate this, we performed energy simulations integrating active solar systems, and also possible self-shading and separations between dwellings. We analysed the deployment of BIPV, BISTw, BIPVTa and BIPVTw technologies individually and in different combinations between them with the purpose of maximizing production capability for self-consumption, comparing them with residential demands in order to identify energy deficits and surpluses in different seasons. The measurements were taken hourly on typical days in summer, winter and intermediary seasons. Finally, an annual balance was obtained, showing energy surpluses of approximately 174 % when deploying only BIPV collectors and 251 % when combining BIPVTw and BIPVTa.

Working with builder partners on test homes allows for vetting of whole-house building strategies to eliminate any potential unintended consequences prior to implementing these solution packages on a production scale. To support this research, the Consortium for Advanced Residential Buildings partnered with Preferred Builders Inc. on a high performance test home in Old Greenwich, Connecticut. The philosophy and science behind the 2,700 ft2 Performance House were based on the premise that homes should be safe, healthy, comfortable, durable, efficient, and adapt with the homeowners. The technologies and strategies used in the Performance House were not cutting-edge, but simply best practices practiced. The focus was on simplicity in construction, maintenance, and operation. When seeking a 30% source energy savings targets over a comparable 2009 International Energy Conservation Code-built home in the cold climate zone, nearly all components of a home must be optimized. Careful planning and design are critical. The Performance House demonstrates how a home can be designed and constructed in the cold climate zone to be energy efficient, low maintenance, sustainable, and comfortable. Lower price premiums are still needed for solutions such as ccSPF and light-emitting diodes, but this is anticipated as their market demand increases. For a solution package of this level to become commercially viable, there is still a need to improve the home appraisal process to better value the multiple benefits of a solution package of this type over standard builder practices.