This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

The AVoided Emissions and geneRation Tool (AVERT) evaluates how energy policies and programs such as energy efficiency, renewable energy, and electric vehicles lead to changes in emissions of particulate matter (PM2.5), nitrogen oxides (NOX), sulfur dioxide (SO2), carbon dioxide (CO2), volatile organic compounds (VOCs), and ammonia (NH3) from electric power plants at a county, state, or regional level. AVERT is a free tool designed to meet the needs of state air quality planners, energy officials, public utility commission staff, environmental agency staff, professionals in the clean energy field, people working on climate planning, and other interested stakeholders.

Building project data for 75,110 single-family homes upgraded between July 1, 2010, and September 30, 2013 from the Better Building Neighborhood Program. This dataset includes a documentation file and three data tables. Reported data for some elements have been transformed and data for some upgraded homes have been omitted to protect privacy. See documentation file for details.

The Wind/Flood Risk Correlation Explorer demo displays correlations for wind gust vs precipitation or river flow, as well as correlations between the team’s new Flood Severity and Storm Severity indices.

The dataset combines: (1) data from the Chicago Energy Benchmarking Ordinance for 1,325 multifamily residential buildings, (2) demographic and socioeconomic data collected from the United States Census Bureau, and (3) building attribute data provided by the Chicago Building Footprint (CBF) data set.

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.

The curated fault model simulation data set consists of tagged and fully-described time series representing simulated faults for the AFDD test building (ORNLs Flexible Research Platform (FRP)), including baseline performance, faulty performance, and corresponding energy impact. A total of 26 fault models are considered for 99 simulation scenarios with various fault intensity levels. Additional Contacts: Principal investigator: Matt Leach Matt.Leach@nrel.gov Simulation performer: Janghyun Kim Janghyun.Kim@nrel.gov

The curated fault experiment data set consists of tagged and fully described time series representing measured faults from the AFDD test building (ORNLs Flexible Research Platform [FRP]), including baseline performance and faulty performance. A total of 10 different faults are tested for 49 different faulted and unfaulted scenarios with various fault intensity levels. Additional Contacts: Principal investigator: Matt Leach Matt.Leach@nrel.gov Experiments coordinator: Piljae Im imp1@ornl.gov Document preparation: Janghyun Kim Janghyun.Kim@nrel.gov

This documentation and dataset can be used to test the performance of automated fault detection and diagnostics algorithms for buildings. The dataset was created by LBNL, PNNL, NREL, ORNL and ASHRAE RP-1312 (Drexel University). It includes data for air-handling units and rooftop units simulated with PNNL's large office building model.

ENERGY STAR® is the government-backed symbol for energy efficiency. The blue ENERGY STAR label provides simple, credible, and unbiased information that consumers and businesses rely on to make well-informed decisions. With this powerful tool, you can estimate your building’s GHG emissions, support GHG emissions inventories, evaluate emissions under local building performance standards, and forecast the impact of changes in building efficiency and energy procurement.

Local authority ESB Connections do not include second-hand houses acquired by them. New units acquired under Part V, Planning & Development Acts 2000-2008 for local authority rental purposes are included. Voluntary & co-operative housing consists of housing provided under the capital loan & subsidy and capital assistance schemes. Data on this variable was not available until 1993. ESB Connections data series are based on the number of new dwellings connected by ESB Networks to the electricity supply and may not accord precisely with local authority boundaries. These represent the number of homes completed and available, and do not reflect any work-in progress. For 2005Q1, direct comparisons cannot be made with 2006, as those figures included some units built in 2005. ESB Networks have indicated that there was a higher backlog in work-in-progress in 2005 than usual ( estimated as being in the region of 5,000 units). This backlog was cleared through the connection of an additional 2,000 houses in Quarter 1 2006 and 3,000 houses in Quarter 2 2006. Due to circumstances beyond the Department's control it has not been possible to obtain a separate set of figures for the first two quarters of 2005. Direct comparisons cannot be made between pre 2009 and post 2010 data onwards. Up to 2010, completions relating to long term voids and demountables were included as new build completions. For 2009Q1, 2011Q1 and 2011 Q3, discrepencies in these quarters is explained in the Qtrly hse compls-sector county tab The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. 2010 Q4 figure for Social Housing-Voluntary & Co-operative Housing; Malcolm Hillis - (DECLG): changed from 258 to 270 as 12 units ommitted from original 2010 figures 18/11/15 2015 Q3 figure for Social Housing – LA Housing; This was previously 8. It was changed on the 27-4-16 when revised data was received by the Department.

These data are based on the number of new dwellings connected by the ESB to the electricity supply but exclude conversions and may not accord precisely with local authority boundaries. "Individual House" is where connection is provided to separate detached house. "Scheme House" is where connection is provided to two or more houses. "Apartments" is where all customer metering for the block is centrally located. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. These data are based on the number of new dwellings connected by the ESB to the electricity supply but exclude conversions and may not accord precisely with local authority boundaries.

ESB Connections data series are based on the number of new dwellings connected by ESB Networks to the electricity supply and may not accord precisely with local authority boundaries. These represent the number of homes completed and available, and do not reflect any work-in progress. From June 1st 2014 - North Tipperary and South Tipperary Coco's merged to form Tipperary County Council; Limerick City and Limerick Coco merged to form Limerick City & County Council and Waterford City and Waterford Coco merged to form Waterford City & County Council. Data for 2014 incorporates the seperate authorites into a final figure for the new authority area in 2014. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. ESB Connections data series are based on the number of new dwellings connected by ESB Networks to the electricity supply and may not accord precisely with local authority boundaries. These represent the number of homes completed and available, and do not reflect any work-in progress. Direct comparisons cannot be made with 2006, as those figures included some units built in 2005. ESB Networks have indicated that there was a higher backlog in work-in-progress in 2005 than usual ( estimated as being in the region of 5,000 units). This backlog was cleared through the connection of an additional 2,000 houses in Quarter 1 2006 and 3,000 houses in Quarter 2 2006. Due to circumstances beyond the Department's control it has not been possible to obtain a separate set of figures for the first two quarters of 2005. Direct comparisons cannot be made between pre 2009 and post 2010 data onwards. Up to 2010, completions relating to long term voids and demountables were included as new build completions. Dublin County was split in 1994 into Fingal, South Dublin and Dun Laoghaire. From June 1st 2014 - North Tipperary and South Tipperary Coco's merged to form Tipperary County Council; Limerick City and Limerick Coco merged to form Limerick City & County Council and Waterford City and Waterford Coco merged to form Waterford City & County Council. Data for 2014 incorporates the seperate authorites into a final figure for the new authority area in 2014. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

Local authority ESB Connections do not include second-hand houses acquired by them. New units acquired under Part V, Planning & Development Acts 2000-2008 for local authority rental purposes are included. Voluntary & co-operative housing consists of housing provided under the capital loan & subsidy and capital assistance schemes. Data on this variable was not available until 1993. ESB Connections data series are based on the number of new dwellings connected by ESB Networks to the electricity supply and may not accord precisely with local authority boundaries. These represent the number of homes completed and available, and do not reflect any work-in progress. For the first 6 months of 2005, direct comparisons cannot be made with 2006, as those figures included some units built in 2005. ESB Networks have indicated that there was a higher backlog in work-in-progress in 2005 than usual ( estimated as being in the region of 5,000 units). This backlog was cleared through the connection of an additional 2,000 houses in Quarter 1 2006 and 3,000 houses in Quarter 2 2006. Due to circumstances beyond the Department's control it has not been possible to obtain a separate set of figures for the first two quarters of 2005.. Direct comparisons cannot be made between pre 2009 and post 2010 data onwards. Up to 2010, completions relating to long term voids and demountables were included as new build completions. For 2009 Q1 Social housing, while there were 39 Voluntary and Co-operative units completed in Limerick City in Q1 2009, they were represented by 10 ESB connections due to the nature of the units (emergency units). 28 Voluntary and cooperative units were completed in Carlow in Q1 2011 however due to their nature they only represented 1 ESB connection 20 Voluntary and cooperative units were completed in Galway City in Q3 2011 however due to their nature they only represented 1 ESB connection The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

These data are based on the number of new dwellings connected by the ESB to the electricity supply but exclude conversions and demountables. They may not accord precisely with local authority boundaries. The classification used for "type of dwelling" up to 2004, is no longer available. Data after 2004 is available on the website. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

Data on the energy efficiency of dwellings, environmental impact score and estimated CO2 emissions in England and Wales for five year groups. These are broken down by property type, tenure and whether a dwelling is new or existing.

This dataset contains point features representing building locations of fire stations in the United States, District of Columbia, Puerto Rico and the U.S. Virgin Islands. The purpose of this collection is to represent the fire station locations on general purpose cartographic products. The data includes manned fire stations and buildings from which a fire response occurs, such as a volunteer fire department building to which fire fighters report for duty, but which is not continuously manned. The data includes both private and governmental entities. In some cases, where fire stations are known to exist but could not be located on a specific building, such as on a military base, airport or manufacturing location, a point was placed at the entrance or the general center of the facility or complex. Some fire locations are approximate, most prevalently in rural areas. Fire training facilities are not included unless these locations also have an active fire station. Locations solely for storing or maintaining fire equipment, or fire stations without a permanent location, were generally excluded.

This data includes offices where Forest Service employees work or where IT equipment is housed. There is no Personally Identifiable Information (PII) data in this dataset, nor telework locations. It includes owned, leased and shared offices. Shared offices are buildings owned or leased by another entity (i.e. a university, other federal agency, etc.) but one or more Forest Service employee(s) work at the building or IT equipment is housed at the building.Depicts the spatial locations for Office locations from the Forest Service CIO Asset Management Office. It includes owned, leased and shared offices. Data is collected, maintained and stewarded by the CIO Asset Management Office. EDW data loading tools extract the office location data from the CIO Asset Mgt. database. Latitude and longitude values are validated and then converted to spatial point data. Spatial point data and associated attributed data describing the office location are inserted into the Office Location Feature class in the Enterprise Data Warehouse. Changes to the Office Location data are checked daily by EDW data loading tools. Data is updated weekly. Data is visible at all scales and zoom levels. Metadata and Downloads.

Office for National Statistics - Housing Census map provides a comprehensive insight into the various types of homes and the people living in them in England and Wales.

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit: https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities

The project this data comes from looks to make building wall retrofits less expensive and easier to install by using exterior insulating panels. This is done to make buildings more energy efficient without expensive wall retrofits or reconstruction. The "Measurement and Verification for Topic 1 Phase 1 Testing Guidance Draft" document includes the following: Standard Test Methods for Determining Thermal Performance: Controlled field testing and THERM simulations Standard Test Methods for Determining Air Leakage Rate: modified ASTM E779 Criteria for Moisture-Control Design Analysis in Buildings: WUFI simulations Moisture Management Plan: Relevant enclosure system design details. ABC Technology Scaling Framework: TRL characteristics relative to development phases Customer Discovery: Target customers and why they would buy/adopt the innovation. Also included in the submissions is the "Duct Blaster Test Report" from 8-25-2021 which includes a filled out test report form for a Duct Blaster which is used to directly pressure test a duct system for air leaks.

This data set, compiled by the Fraunhofer Center for Sustainable Energy Systems, includes long-term 10-minute temperature and relative humidity data, and HVAC system state data for 79 apartments in a low-income housing complex in Revere, MA. The monitoring period spans two winters and one summer between 2011 and 2013. Data were collected as part of a project sponsored by the U.S. Department of Energy Building America program to evaluate the impact of programmable thermostat usability on occupant behavior. This project was done in conjunction with NREL as part of the US Department of Energy's Building America program.

The NABERS ESC Estimator will help you understand whether your building is eligible to participate in the NSW Energy Savings Scheme (ESS) under the NABERS baseline method. It will also provide an estimation of how many ESCs your building could potentially generate by reducing its energy consumption. For this tool you will need an accurate estimation of your energy consumption and your predicted NABERS Energy rating. To generate ESCs, you will need to assign an Accredited Certificate Provider (ACP) before the end of the rating period. Note: results predicted by the ESC Estimator are an indication only.

NABERS has released prediction tools to support building owners in understanding the impact of NABERS Energy ratings on individual assets across different sectors. The tool includes forecasted scenarios estimating the impact on ratings in 2025 and 2030. The benchmarks which calculate NABERS Energy ratings are updated every five years with the next update scheduled for 2025. These changes are implemented in a way that does not affect the average rating of the sector at that time.

Use the NABERS rating calculators to get an idea of how well your building or tenancy is performing. Results are an indication only and cannot be promoted or published.

NABERS reverse calculators give you an indication of the maximum amount of energy and water your building can use to achieve your desired rating.

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Energy Model from the RSF Systems Model. Comparing actual performance (metered) with expected performance (modeled) is key to understand corrective actions to ensure performance as originally intended. The Energy Model in this submission was made to model energy usage in the RSF and was compared to actual metered data to verify the model. Measured data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Measured Energy Data from the RSF Systems Model. The data includes a csv with measured data from NREL's Research and Support Facility (RSF) for 2011. The dataset contains hourly data for: Total Cooling (kW)- Total Heating (kW)- Total Mechanical (kW)- Total Lighting (kW)- Total Plug Loads (kW)- Total Data Center (kW)- Total Building (kW)- PV (kW)- Building Net (kW). Energy Model data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

ESB Connections data series are based on the number of new dwellings connected by ESB Networks to the electricity supply and may not accord precisely with local authority boundaries. Due to circumstances beyond the Department's control it has not been possible to obtain a separate set of figures for the first 6 months of 2005. These data excludes conversions of buildings into residential units, which are estimated to total 400 each year since 1992. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

These data are based on the number of new dwellings connected by the ESB to the electricity supply but exclude conversions. The classification used for "type of dwelling" up to 2004, is no longer available.

The National Renewable Energy Laboratory's (NREL) Photovoltaic (PV) Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The PVRDB data are organized by city and year of lidar collection. Five geospatial layers are available for each city and year: 1) the raster extent of the lidar collection, 2) buildings identified from the lidar data, 3) suitable developable planes for each building, 4) aspect values of the developable planes, and 5) the technical potential estimates of the developable planes.

Explore the Saudi Arabia Private Sector Imports dataset featuring Grand Total, Foodstuff, Building Materials, Machinery, Fruits & Vegetables, and more. Access annual data from 1993 onwards. Grand Total, Foodstuff, Food Grains, Building Materials, Machinery, Other Goods, Fruits & Vegetables, Textiles & Clothings, Motor Vehicles, Appliances, Livestock and Meat, SAMA Monthly Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Note:The data are updated. The data of foreign bank branches operating in Saudi Arabia have been amended and updated as per international best practices and the Monetary and Financial Statistics Manual.

This Tool is intended to enable companies to develop appropriate science-based emissions reductions targets, as well as to assist companies understand and implement the level of climate ambition required to meet the 1.5°C goal of the Paris Agreement.

EPC data available is as follows: All buildings which were newly constructed, with a building warrant application made after 9 January 2013 Dwellings sold or rented to a new tenant after 1 December 2008; and Non-domestic buildings sold, rented to a new tenant and public buildings from January 2013.

As part of a commitment to drive greater transparency and accountability for the delivery of social housing across the country, Minister for Housing, Planning & Local Government Eoghan Murphy today (20 February, 2019) published social housing delivery figures for the high-level programmes of build, acquisition, leasing, HAP and RAS for all 31 local authorities. In 2018, delivery against target has been published, on a quarterly basis, on the Rebuilding Ireland website and the full year of activity by each local authority can now be reviewed on the website. Overall, the target for social housing delivery in 2018, under Rebuilding Ireland, was exceeded by 6% and the housing needs of over 27,103 households were met. Of note is the following: • 8,422 new homes were brought into the active social housing stock through build, acquisitions, voids and leasing programmes in 2018. (4,251 build; 560 renovated voids; 2,610 acquisitions & 1,001 long-term leased). • There was an 85% increase in new build social homes in 2018 when compared to 2017 (excluding voids). • The number of new social housing homes built in 2018 was eight times greater than the number built in 2015, the year before Rebuilding Ireland (excluding voids). • Construction figures from December 2018 show almost 5,000 new social housing homes currently being built across 291 sites and this is being added to on a weekly basis. • 38% delivered by AHB’s in partnership with local authorities. • New build and long term leasing is helping us move away from HAP solutions as demonstrated by the fact that new HAP solutions did not increase significantly in 2018, but all other delivery streams did. At last week’s Housing Summit Minister Murphy took the opportunity to discuss individual rates of delivery for each local authority against their target and also stressed the need to further accelerate and enhance delivery pipelines, in particular new-build activity. Minister Murphy again reaffirmed the Department of Housing, Planning and Local Government commitment to supporting local authorities and Approved Housing Bodies deliver much-needed homes across the country. He emphasised that funding and resources are not an issue and that any delivery targets set for local authorities are minimum targets and stressed the importance of each local authority doing as much as possible. In Galway, where there are significant delivery challenges, Minister Murphy is establishing a Galway Housing Delivery Task Force, which will be chaired by Ms. Geraldine Tallon, former Secretary General. Ms. Tallon currently chairs the Cork Housing Delivery Task Force, which has supported Cork City Council deliver 117% of their build target and Cork County Council 126% of their build target.

The non-gas map is a detailed map of Great Britain showing the distribution of properties without a gas grid connection across local authorities, LSOAs (lower-level super output areas) and, for registered users, postcodes. For each region, the map displays: Distances from gas grid; Central heating energy source; Energy efficiency measures; Property type; Tenure; Number of rooms; Energy performance rating (EPC)

ESB Connections data series are based on the number of new dwellings connected by ESB Networks to the electricity supply and may not accord precisely with local authority boundaries. These represent the number of homes completed and available, and do not reflect any work-in progress.

ESB Connections data series are based on the number of new dwellings connected by ESB Networks to the electricity supply and may not accord precisely with local authority boundaries. These represent the number of homes completed and available, and do not reflect any work-in progress. Direct comparisons cannot be made with 2006, as those figures included some units built in 2005. ESB Networks have indicated that there was a higher backlog in work-in-progress in 2005 than usual ( estimated as being in the region of 5,000 units). This backlog was cleared through the connection of an additional 2,000 houses in Quarter 1 2006 and 3,000 houses in Quarter 2 2006. Due to circumstances beyond the Department's control it has not been possible to obtain a separate set of figures for the first two quarters of 2005. Direct comparisons cannot be made between pre 2009 and post 2010 data onwards. Up to 2010, completions relating to long term voids and demountables were included as new build completions. Dublin County was split in 1994 into Fingal, South Dublin and Dun Laoghaire. From June 1st 2014 - North Tipperary and South Tipperary Coco's merged to form Tipperary County Council; Limerick City and Limerick Coco merged to form Limerick City & County Council and Waterford City and Waterford Coco merged to form Waterford City & County Council. Data for 2014 incorporates the seperate authorites into a final figure for the new authority area in 2014. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.