The Department of Energy and Climate Change (DECC) has collected and published energy consumption data within the Digest of UK Energy Statistics since 1948. Over recent years there has been a greater focus on more detailed information to help support policy development. To support this, DECC has created the National Energy Efficiency Data-Framework (NEED). This a data framework made up of data from difference sources. Annual gas and electricity consumption data are matched, at an individual property level, with information about energy efficiency measures installed at the property and property attributes. The Framework was first announced in the Heat and Energy Saving Strategy in 2009 and was developed by DECC in order to assist DECC in its business plan priority to 'save energy with the Green Deal and support vulnerable consumers'. It forms a key element of DECC's evidence base supporting DECC to: develop, monitor and evaluate key policies; identify energy efficiency potential which sits outside the current policy framework; develop a greater understanding of the drivers of energy consumption; and gain a deeper understanding of the impacts of energy efficiency measures for households and businesses. Regular outputs from NEED are published by DECC and available on the gov.uk National Energy Efficiency Data-Framework (NEED) webpage.

The Department for Business, Energy & Industrial Strategy has also made an anonymised version of the dataset available for analysis by a wider audience to help support these aims. The anonymised dataset includes meter point gas and electricity consumption data; information on energy efficiency of households including measures installed; and property attributes. This is available on the gov.uk National Energy Efficiency Data-Framework (NEED): anonymised data webpage.

The National Energy Efficiency Data-Framework (NEED) was set up to provide a better understanding of energy use and energy efficiency in domestic and non-domestic buildings in Great Britain. The data framework matches gas and electricity consumption data, collected for BEIS sub-national energy consumption statistics, with information on energy efficiency measures installed in homes, from the Homes Energy Efficiency Database (HEED), Green Deal, the Energy Company Obligation (ECO) and the Feed-in Tariff scheme. It also includes data about property attributes and household characteristics, obtained from a range of sources.

Annual gas and electricity consumption data for individual meters matched at property level to other information on the energy efficiency measures in the properties and characteristics of the property and occupiers

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR® Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases D I S C L A I M E R: Normalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter. Home Performance with ENERGY STAR® Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings 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 Home Performance with ENERGY STAR 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. 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. This dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric. 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 provides a national unified database of residential building retrofit measures and associated retail prices and end-user might experience. These data are accessible to software programs that evaluate most cost-effective retrofit measures to improve the energy efficiency of residential buildings and are used in the consumer-facing website https://remdb.nrel.gov/ This publicly accessible, centralized database of retrofit measures offers the following benefits: Provides information in a standardized format Improves the technical consistency and accuracy of the results of software programs Enables experts and stakeholders to view the retrofit information and provide comments to improve data quality Supports building science R&D Enhances transparency This database provides full price estimates for many different retrofit measures. For each measure, the database provides a range of prices, as the data for a measure can vary widely across regions, houses, and contractors. Climate, construction, home features, local economy, maturity of a market, and geographic _location are some of the factors that may affect the actual price of these measures. This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.

The Domestic National Energy Efficiency Data-Framework (NEED) brings together information on domestic gas and electricity consumption with other information about domestic properties and the households that live in them. The full NEED dataset is not published. However, DESNZ publishes two samples of the full NEED dataset (one containing 4,000,000 properties and one containing 50,000 properties) designed to be representative of domestic properties in England and Wales, while ensuring that information about individual households remains protected.

Over the last two decades, the Royal Government of Cambodia has successfully implemented a rectangular strategy for growth, employment, equity, and efficiency. This strategy becomes a policy tool that will transform Cambodia from a least developed country to one of the fastest-growing economies in the world, helping millions of Cambodians lift themselves out of poverty and improve several other social development indicators.

Data on the energy efficiency of dwellings, environmental impact score and estimated CO2 emissions in England and Wales at the country and region level. These are broken down by property type, tenure, age of property and whether a dwelling is new or existing.

Data were made available by the National Energy Efficiency Dataset (NEED). Access to the dataset can be found here. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/857035/anon_set_50k_2019.csv Data was complemented with information from Eurostat (Heating degree days and cooling degree days are not available anymore by region in Eurostat. Original dataset from Eurostat can be provided upon request) and the Department of Business, Energy and Industrial Strategy through https://www.gov.uk/government/statistical-data-sets/annual-domestic-energy-price-statistics. The version used in this paper was published on 26-Mar-2020. All the data gathered for this research belongs to a third parties and are available open access in their respective repositories. The raw data is included in the document Dataset.

Data from the Opinions and Lifestyle Survey (OPN), on domestic energy efficiency in Great Britain, collected between 22 September and 3 October 2021. Questions cover energy in the home, and attitudes to improving energy efficiency in the home.

Energiatõhususe riiklik andmeraamistik (NEED) loodi selleks, et paremini mõista energiatarbimist ja energiatõhusust Suurbritannia kodu- ja välismaistes hoonetes. Andmeraamistik vastab gaasi- ja elektritarbimise andmetele, mis on kogutud BEISi piirkondliku energiatarbimise statistika jaoks, ning kodumajapidamiste energiatõhususe andmebaasist saadud teabele kodumajapidamiste energiatõhususe meetmete kohta (soojendatud).See hõlmab ka mitmesugustest allikatest saadud andmeid kinnisvara omaduste ja leibkonna omaduste kohta. NB! Vigadeade avaldati 4. augustil 2021. Lähteasutus: Äri-, energia- ja tööstusstrateegia

Nimetus: Riiklik statistika (jõustus 2015. aasta veebruarist)

Keel: Inglise

Alternatiivne pealkiri: VAJADUS

The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling.

The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.

The data of city-and-county-level energy consumption and energy efficiency provided in this study are valuable with practical applications in the field of energy economics, management, and policy, including the following: First, the provided data have the characteristics of wide spatial coverage and long time span. This unique panel-structured dataset can be used to observe the trajectories and spatial differences of energy consumption and energy efficiency on a micro-level than at national and provincial levels. Therefore, it can also be used to analyze the factors driving the changes and spatial differences of energy consumption and energy efficiency in cities and counties. Second, the panel-structured dataset of energy consumption and energy efficiency can be used to match other economic data at the city and county levels, and studies such as the economic effects of energy consumption, the environmental and social effects of energy consumption, and the coupling relationship between energy efficiency and economic development may be conducted. Third, the development of energy consumption and energy efficiency data at the city and county levels can not only contribute to the energy management at China’s grassroot-level governments, such as in the formulation and implementation of road maps for energy transformation and energy efficiency improvement, but also provide a basis for the central and provincial governments to allocate the energy rights of cities and counties under the constraints of “carbon peak” and “carbon-neutral” targets. Fourth, the development of energy consumption and energy efficiency data at city and county levels could provide a more accurate assessment of the impact of the central government’s energy saving, emission reduction, and low-carbon green policies, as well as other socioeconomic policies, for example, assessment of the impact of the “central heating,” “coal to electricity,” low-carbon pilot city, and carbon emission trading right pilot policies on energy consumption and energy efficiency. Fifth, the method of retrieving micro-level energy consumption data by using satellite night-light data can also provide a reference for other developing countries and regions with limited energy statistics to evaluate their energy consumption and energy efficiency at the sub-national level.

Average Standard Assessment Procedure energy rating score

How the figure is calculated:

The sum of SAP energy rating scores for each new home for which an energy performance certificate has been issued in the reporting period, divided by the number of new homes for which a certificate has been issued. It is the average of the large number of scores calculated for new dwellings during the reporting period.

Why is this indicator in the business plan?

This is a key housing measure for which DCLG has policy responsibility. It monitors the energy efficiency of new build homes.

How often is it updated?

Quarterly

Where does the data come from?

National Energy Performance Certificate Register. Published figures are available here

What area does the headline figure cover?

England

Are further breakdowns of the data available?

Yes, can be split by dwelling type.

What does a change in this indicator show?

An increase in this indicator would show an average increase in the energy efficiency of new homes. The average SAP rating is expected to gradually rise over the long-term as a growing proportion of new homes are completed to the 2010 Building Regulations standard, which requires more energy efficient new homes.

Time Lag

Published within two months of the end of the reporting period

Next available update

To be confirmed.

Type of Data

Official Statistics.

Robustness and data limitations

Average figures are volatile due to a number of factors including the small number of new homes being assessed, the mix of dwelling types, the mix of heating systems used in new developments and the location of those developments.

Links to Further Information

https://www.gov.uk/government/organisations/department-for-communities-and-local-government/series/code-for-sustainable-homes-statistics#publications

Contact Details

CorporatePerformance@communities.gsi.gov.uk

This webpage contains sources of (1) Data tables of federal agency energy and water consumption; (2) Interactive graphics associated with most data tables; (3) Energy costs by end-use sector and efficiency investment information; (4) Progress toward key goals outlined in the National Energy Conservation Policy Act, as amended (42 U.S.C. 8253-8258); Energy Policy Act of 2005 (42 U.S.C. 15852); (5) Historical data tables of agency energy use and costs by facility and mobility sectors by energy type are also available for fiscal year (FY) 1975 through FY 2020