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.

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.

In 2024, space and water heating, along with cooling, made up most of the energy consumed by residential buildings in the United States. Overall, U.S. homes used over **** quadrillion British thermal units to heat homes. The type of end-use with the highest level of energy consumption was other uses, a category which includes electric and electronic devices, motors, pool and spa heaters, outdoor grills, and other devices which did not fit in the rest of the categories included.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and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.

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 Buildings

Czech Republic Electricity Consumption: Buildings Service and Landscape Activities data was reported at 46,134.403 MWh in 2022. This records an increase from the previous number of 41,653.891 MWh for 2021. Czech Republic Electricity Consumption: Buildings Service and Landscape Activities data is updated yearly, averaging 58,928.698 MWh from Dec 2007 (Median) to 2022, with 16 observations. The data reached an all-time high of 86,432.000 MWh in 2014 and a record low of 7,137.000 MWh in 2007. Czech Republic Electricity Consumption: Buildings Service and Landscape Activities data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.RB005: Energy Consumption: Electricity: by Industry: Statistical Classification of Economic Activities Rev. 2.

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.

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.

Analysis of ‘Monroe County Single Family Residential Building Assets and Energy Consumption: 2017-2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fd9c640d-6e47-4a51-9b9b-0cc6304517e1 on 12 February 2022.

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

PLEASE DOWNLOAD THE FULL REPORT UNDER THE ATTACHMENT SECTION IN THE 'ABOUT THIS DATASET' SECTION BELOW.

This aggregated and anonymized dataset of single-family residential building asset attributes and observed average annual energy consumption over the 2-year period from August 2017 through July 2019 is available for Monroe County. The dataset includes more than 55,000 properties from the study’s matched residential dataset that had sufficient data for calculation of average annual energy consumption and could not be uniquely identified in the larger dataset of Monroe County residential parcels or Infogroup data. The data were anonymized by removing all property identifying information including address, parcel identifiers, and parcel size. Attributes such as square footage, building age, and assessed value were then grouped such that no groupings contained fewer than three properties in the Monroe County parcel dataset. This dataset with average annual energy consumption for gas, electric, and total consumption can be used by those interested in further analysis and energy modeling.

In response to the New York State Department of Public Service (DPS) Order Adopting Accelerated Energy Efficiency targets, issued December, 18, 2018, the New York State Energy Research and Development Authority (NYSERDA) contracted with Stone Environmental, Inc to conduct an Asset Data Matching Pilot in Monroe County to analyze building asset data, utility usage data, and NYSERDA program data for single family residential buildings. The objective of the study was to analyze publicly available data along with two years of utility usage data provided by Rochester Gas and Electric (RG&E) to provide information and data to the market to help reduce customer acquisition costs for adoption of energy efficiency measures and to better understand the ability to use building asset data to determine energy efficiency.

See the final report from the analysis under the attachments section.

NYSERDA offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.

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

Czech Republic Electricity Consumption: BC: Buildings Construction data was reported at 38,283.010 GJ in 2023. This records a decrease from the previous number of 39,608.355 GJ for 2022. Czech Republic Electricity Consumption: BC: Buildings Construction data is updated yearly, averaging 49,103.115 GJ from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 484,889.000 GJ in 2008 and a record low of 38,283.010 GJ in 2023. Czech Republic Electricity Consumption: BC: Buildings Construction data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.RB005: Energy Consumption: Electricity: by Industry: Statistical Classification of Economic Activities Rev. 2.

The data stock of electricity consumption in Berlin for 2022, aggregated by building blocks, districts and zip code areas. The data does not take into account self-consumption or network losses. For individual building blocks, the consumption is not indicated for data protection reasons

Czech Republic Electricity Consumption: NG: Buildings Construction data was reported at 189,722.078 GJ in 2022. This records a decrease from the previous number of 193,646.047 GJ for 2021. Czech Republic Electricity Consumption: NG: Buildings Construction data is updated yearly, averaging 312,068.000 GJ from Dec 2007 (Median) to 2022, with 16 observations. The data reached an all-time high of 1,416,968.000 GJ in 2009 and a record low of 179,440.047 GJ in 2020. Czech Republic Electricity Consumption: NG: Buildings Construction data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.RB005: Energy Consumption: Electricity: by Industry: Statistical Classification of Economic Activities Rev. 2.

This table shows regional figures on the average consumption of energy (natural gas and electricity) of private dwellings broken down by type of dwelling and ownership for Nederland, group of provinces, provinces and municipalities. Besides, for total dwellings only, the share of heat distribution (district heating) has been added, because this is relevant for the interpretation of the height of the average consumption of natural gas.

Data available from: 2010

Status of the figures: All figures from 2010 - 2021 are definite. Figures of 2022 are provisional.

Changes as of October 2023: Provisional figures of 2022 have been added. Figures of 2021 have been updated. The category “Average consumption of electricity” is replaced by “Average supply of electricity” and a category “Average net supply of electricity” has been added.

When will new figures be published? A revision to the method of this statistic is currently underway, causing the table to be delayed. New figures will come in the 3rd quarter of the folowing year.

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 ---

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.

[v2 update] weather data correction

The data describes an electrical energy community, containing photovoltaic (PV) production profiles and end-user consumption profiles, desegregated by individual appliances used.

A dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. The data concerns a full year.

The overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles.

This work has been published in Elsevier's Data in Brief journal: Calvin Goncalves, Ruben Barreto, Pedro Faria, Luis Gomes, Zita Vale, Dataset of an energy community's generation and consumption with appliance allocation, Data in Brief, Volume 45, 2022, 108590, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2022.108590 (https://www.sciencedirect.com/science/article/pii/S2352340922007971)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

Reference data used to create this dataset:

Renewable energy production profiles: https://site.ieee.org/pes-iss/data-sets/

End-user profiles:

https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households

https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption

https://site.ieee.org/pes-iss/data-sets/

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 district of Treptow-Köpenick of Berlin operates a variety of different buildings that create a significant energy consumption. As part of energy management, this energy consumption is regularly monitored and published annually in accordance with the Berlin Energy Transition Act. The consumption data are available for download here. It is noted that not every object has its own supply and thus a separate heat or power meter. This can lead to no or only a pro rata consumption shown in the overview for individual properties. Other properties, on the other hand, may also contain the (partial) heat or power consumption of adjacent objects. In addition, an area-based breakdown of the total consumption was made in individual cases. The specific boundary conditions of individual objects are not taken into account. The present presentation also excludes other temporary special effects (such as the limited use of a property in the course of a refurbishment). The consumption data shown correspond to the current state of knowledge and may change in individual cases (e.g. by subsequent billing corrections of the suppliers).

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Green Data Center (GDC) Market Size 2024-2028

The green data center (GDC) market size is forecast to increase by USD 202.4 billion, at a CAGR of 27.17% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing electricity consumption and cost associated with traditional data centers. This trend is compelling organizations to adopt energy-efficient solutions, such as GDCs, to reduce their carbon footprint and minimize operational expenses. Another key driver is the growing adoption of Data Center Infrastructure Management (DCIM) and automation technologies, which enable more efficient use of resources and improved energy management in GDCs. However, the market faces challenges as well. One major obstacle is the high cost of building and maintaining GDCs, which can be a significant barrier for entry for some organizations.
Additionally, the complexity of designing and implementing GDCs requires specialized expertise and significant investment in research and development. These challenges necessitate strategic planning and partnerships for companies seeking to capitalize on the opportunities presented by the growing demand for energy-efficient data center solutions.

What will be the Size of the Green Data Center (GDC) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by the increasing demand for sustainable and energy-efficient IT infrastructure. GDCs integrate various technologies to minimize environmental impact, including smart grids, energy storage solutions, renewable energy sources, water conservation, and data center design. These components work in tandem to optimize energy consumption, enhance reliability, and promote cooling system efficiency. Emerging trends in GDCs include the implementation of data center automation, demand response, and energy consumption monitoring. Data center design focuses on maximizing server density and improving building automation to reduce carbon footprint and enhance overall efficiency. Renewable energy sources, such as solar, wind, and hydro power, are increasingly being adopted to power data centers, while energy storage solutions ensure consistent power supply.

Water conservation is another critical aspect of GDCs, with many data centers implementing recycling systems to minimize water usage. Cooling systems are being optimized through the use of free cooling and liquid cooling to reduce energy consumption. Data center services providers offer managed services, optimization, and decommissioning solutions to help organizations navigate the complexities of GDC implementation. The ongoing dynamism of the GDC market is reflected in the evolving patterns of data center infrastructure, as organizations continue to seek ways to reduce their environmental impact while maintaining data center availability and reliability. The integration of various technologies, from HVAC systems to network infrastructure, is essential to achieving optimal energy efficiency and sustainability.

How is this Green Data Center (GDC) Industry segmented?

The green data center (GDC) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  IT infrastructure
  Power solutions
  General construction
  Cooling solutions
  Monitoring and management


End-user

  BFSI
  Energy
  IT and telecom
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Component Insights

The it infrastructure segment is estimated to witness significant growth during the forecast period.

The digital transformation era has led an unprecedented number of businesses, particularly small and medium enterprises (SMEs), to embrace cloud computing. By 2025, it is projected that approximately 90% of SMEs will conduct their operations via cloud storage, either by housing their infrastructure in colocation facilities or by availing cloud services from leading Cloud Service Providers (CSPs). Cloud computing's flexibility, scalability, and efficiency make it an indispensable tool for businesses, despite the increased demand for computational power, network traffic, and data storage. This surge in data and the need for more efficient data processing have resulted in a global expansion of data center facilities.

Many medium-sized enterprises are also expected to join the trend of constructing their data centers. The intricacy of data storage systems and the accompanying network infrastructure continue to grow, necessitating advanced cooling systems, energy consumpti