Local Law 84 of 2009 (LL84) requires annual energy and water benchmarking data to be submitted by owners of buildings with more than 50,000 square feet. This data is collected via the Environmental Protection Agency's (EPA) Portfolio Manager website Each property is identified by it's EPA assigned property ID, and can contain one or more tax lots identified by one or more BBLs (Borough, Block, Lot) or one or more buildings identified by one or more building identification numbers (BIN) Please visit DOB's Benchmarking and Energy Efficiency Rating page for additional information.

Seattle’s Building Energy Benchmarking Program (SMC 22.920) requires owners of non-residential and multifamily buildings (Greater than 20,000 square feet) to track energy performance and annually report to the City of Seattle. Annual benchmarking, reporting and disclosing of building performance are foundational elements of creating more market value for energy efficiency. Per Ordinance (125000), starting with 2015 energy use performance reporting, the City of Seattle is making the data for all buildings greater than 20,000 SF available annually. This dataset contains all 2022 buildings required to report. If you have questions or comments on the data, email us at energybenchmarking@seattle.gov and include Open Data in the subject line.

The Energy Benchmarking Law requires the County to make annually reported energy benchmarking information readily available to the public. This dataset reflects data received by the publish date and does not include individually attributable data for buildings in Group 3 and 4, as CY 2022 was the first year that these buildings were required to report to DEP.

In 2022, the Netherlands was the country with the highest energy efficiency score for buildings worldwide. Meanwhile, France had obtained 21 points out of the 25 maximum score. On the other side of the spectrum, Thailand, Egypt, and Russia were on the bottom of the ranking. This score measures several metrics that impact the energy efficiency of buildings.

This data is collected annually via EPA Portfolio Manager. The data collection requires building owners to measure their energy and water consumption and compare it against that of similar buildings in the city and country. The data is useful for policy analysts as it provides transparency into energy and water consumption for the city's largest buildings. Please visit https://www1.nyc.gov/site/buildings/codes/benchmarking.page for additional information.

The operation of residential buildings worldwide consumed more energy than every other segment of the real estate and construction sectors together in 2022. Non-residential buildings were responsible for the consumption of *** percent of all the energy used worldwide that year. Meanwhile, other construction activities, which is the segment that includes the construction of infrastructures, were responsible for over ***** percent of all energy consumption.

"The Building Performance Database (BPD) is the nation's largest dataset of information about the energy-related characteristics of commercial and residential buildings. The BPD combines, cleanses and anonymizes data collected by federal, state and local governments, utilities, energy efficiency programs, building owners and private companies, and makes it available to the public" (Lawrence Berkeley National Laboratory, 2022). Data curated by Carnegie Mellon University Libraries.

Energy benchmarking means tracking a building's energy and water use and using a standard metric to compare the building's performance against past performance and to its peers nationwide. These companions have been shown to drive energy efficiency upgrades and increase occupancy rates and property values. The Clean and Affordable Energy Act of 2008 (CAEA) requires that owners of all large private buildings (over 50,000 gross square feet) annually benchmark their energy and water efficiency and report the results to DOEE for public disclosure. The District government also must annually benchmark and disclose the energy and water efficiency of District government buildings over 10,000 gross square feet. Starting with the calendar year 2021 data (due April 1, 2022) all privately-owned buildings over 25,000 square feet will be required to benchmark, and starting with the calendar year 2024 data (due April 1, 2025) all privately-owned buildings over 10,000 square feet will be required to benchmark, as mandated under the Clean Energy DC Omnibus Act of 2018.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022). These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data. Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period. Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station _location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented. Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region. One major issue with the residential profiles is that for each of the five climate zones, certain _location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold). The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock. Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

Summary energy and building characteristics by building type for non-residential and multifamily buildings greater than 20,000 square feet that benchmark energy data with the City of Seattle. This dataset summarizes information from the full 2022 Building Energy Benchmarking dataset but excludes likely or known errors.

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.

This data set contains the details of 2022 and 2023 to date of energy usage.The Large Offices Significant Energy Users (SEU) comprises the County Hall in Swords and the Civic Offices in Blanchardstown. In 2020 these facilities accounted for 12% of Fingal County Council Energy Consumption. This represent a consumption of 5.6Ghw of primary energy 964 tonnes of Co2 Energy Use for Fingal County Council Large BuildingsCounty HallLibrary BuildingCivic OfficesDriocht

Analysis of ‘Building Energy Performance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a077a124-8357-42e2-b49c-61ada22d88e8 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The City of Hartford benchmarks its most energy-intensive buildings to determine how well the facilities are performing. Using information from energy bills and a tool called WegoWise, City staff are able to evaluate the energy performance per square foot. That data is presented here, in monthly BTU's per Sq. Ft. Please note that this data may not be complete and may be subject to change.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘2016 Building Energy Benchmarking’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9a1cea37-1d5c-49f7-bd10-220a9bd65018 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

Seattle’s Building Energy Benchmarking and Reporting Program (SMC 22.920) requires owners of non-residential and multifamily buildings (20,000 square feet or larger) to track energy performance and annually report to the City of Seattle. Buildings account for 33% of Seattle's core emissions. The benchmarking policy supports Seattle's goals to reduce energy use and greenhouse gas emissions from existing buildings. In 2013, the City of Seattle adopted a Climate Action Plan to achieve zero net greenhouse gas (GHG) emissions by 2050. Annual benchmarking, reporting and disclosing of building performance are foundational elements of creating more market value for energy efficiency. Per Ordinance (125000), starting with 2015 energy use performance reporting, the City of Seattle will make the data for all building 20,000 SF and larger available annually. This update to the benchmarking mandate was passed by Seattle City Council on February 29, 2016.

If you have questions or comments on the data, email us at energybenchmarking@seattle.gov and include Open Data in the subject line.

--- Original source retains full ownership of the source dataset ---

In 2024, ***** trillion British thermal units were used for the space heating of commercial buildings in the United States. The type of end use with the highest level of energy consumption were other uses, a category which includes elevators, equipment, and transformers, among many other devices.

Amended in 2021, the Building Emissions Reduction and Disclosure Ordinance (BERDO) aims to reduce air pollution and greenhouse gas emissions generated by large buildings in Boston. BERDO gives the City of Boston authority to set emissions standards for large existing buildings, including residential buildings with 15 or more units and non-residential buildings that are 20,000 square feet or larger.

The emissions standards set by BERDO decrease over time, with all buildings expected to reach net-zero emissions by 2050. BERDO encourages retrofits, energy efficiency improvements, fuel switching, and renewable energy generation in local buildings.

BERDO applies to the following buildings:

• Residential buildings that have 15 or more units.
• Non-residential buildings that are 20,000 square feet or larger.
• Any tax parcel with multiple buildings that sum to at least 20,000 square feet or 15 units.

Every year by May 15, covered building owners need to report the total energy and water use of their buildings from the previous calendar year. The City of Boston is required to annually disclose BERDO reported data from the previous year.

Please note:

• Reported data from 2021 and earlier was reported under the old Building Energy Reporting and Disclosure Ordinance, which applied to buildings 35,000 square feet and above or 25 units or above. The prior iteration of BERDO required energy and water reporting, but did not include emissions reduction requirements.
• Usage figures reported in these datasets are provided by building owners and have not been independently verified by the City of Boston.
• This dataset will be updated periodically with usage figures which are submitted or revised after the official reporting deadline.
• The compliance map reflects buildings' status as of the reporting deadline in the most recent year, and late submissions will remain marked as out of compliance even if usage figures have subsequently been added to the dataset.
• Reports for buildings which show "Not Available" values in one or more fields are pending revision and will be updated when additional data becomes available.

Over half of existing buildings with a certificate in Spain in 2022 had a rating of E. That was the case for both categories, energy consumption and CO2 emissions. Overall, there were roughly ****** buildings with an A rating for CO**emissions and ****** buildings with an A rating for energy consumption.

Analysis of ‘DCAS Managed Building Energy Usage’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/223b0b2f-ae62-4b70-989d-5a5ce2b93ab2 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

City Building Energy Usage Data.

--- Original source retains full ownership of the source dataset ---

The building energy management solutions market is valued at US$ 4.7 billion as of 2022. The market is expected to grow at a CAGR of 12.5% during the period 2022 to 2032 and is expected to reach a valuation of US$ 15.2 billion by 2032.

Attributes	Details
Building Energy Management Solutions Market Value in 2022	US$ 4.7 Billion
Building Energy Management Solutions Market Value in 2032	US$ 15.2 Billion
Building Energy Management Solutions Market CAGR (2022 to 2032)	12.5%

Report Scope

Attributes	Details
Growth Rate	CAGR of 12.5% from 2022 to 2032.
The base year for Estimation	2022
Historical Data Available for	2016 to 2021
Forecast Period	2022 to 2032
Quantitative Units	Revenue in US$ Million and CAGR from 2022 to 2032
Report Coverage	Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis.
Segments Covered	Component End Use Regions
Regions Covered	North America Latin America Asia Pacific Japan Western Europe Eastern Europe Middle East & Africa
Key Countries Profiled	The United States of America Canada Brazil Argentina Germany The United Kingdom France Spain Italy Nordics BENELUX Australia & New Zealand China India GCC Countries South Africa
Key Companies Profiled	Siemens AG Schneider Electric Honeywell International Inc. Cisco Eaton. General Electric Company Johnson Controls. CA Technologies Ecova Inc. Cylon Controls Others
Customization	Available upon Request