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 BuildingsBench datasets consist of: - Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. - 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: 1. ElectricityLoadDiagrams20112014 2. Building Data Genome Project-2 3. Individual household electric power consumption (Sceaux) 4. Borealis 5. SMART 6. IDEAL 7. Low Carbon London

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Data and statistics on energy consumption in homes, commercial buildings, manufacturing, and transportation. Data released monthly or annually.

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

Interactive map of wildfires currently active in Montana.Current wildfire locations, perimeters, and historic fire perimeters are based on the National Interagency Fire Center (NIFC) Wildland Fire Open Data.Cadastral, public lands, and structures/addresses layers are based on the Cadastral and Structures/Addresses Framework datasets maintained by the Montana State Library.Photo by John McColgan - Edited by Fir0002 [Public domain], via Wikimedia Commons.For more information or to report issues with this application, please contact the Montana State Library: geoinfo@mt.gov

DOE NEPA Documents are available to members of the public, including categorical exclusion determinations, environmental assessments, environmental impact statements, findings of no significant impact, records of decision, mitigation action plans, notices of availability, notices of intent, supplement analyses, and other notices and documents.

Derelict Sites Street address, building number and Dublin area code of derelict sites within the City Council Boundary. Indicates whether the buildings are listed or otherwise. Also gives a brief description as to where sites are in proximity to in the case of no building number.

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 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.

Gas Consumption List of properties we own or manage and the consumption rate of gas used for each. The dataset contains 4 worksheets based on different service providers.

The ONS Geography Linked Data site is the access point for information on statistical geographies required to support the use of official statistics. It is designed to allow users to discover, view and use geospatial data. The site is complementary to the Open Geography Portal. It allows access directly to data within the geography products, in machine-readable form, and using an Application Programming Interface.

The Solar API provides access to the solar potential of hundreds of millions of buildings.

Monthly average hourly CO2, NOx, and SO2 emission factors for each U.S. eGRID subregion. This project utilized GridViewTM, an electric grid dispatch software package, to estimate hourly emission factors for all of the [eGRID](https://www.epa.gov/egrid) subregions in the continental United States. These factors took into account electricity imports and exports across the eGRID subregion boundary, and included estimated transmission and distribution (T) losses. Emission types accounted for included carbon dioxide (CO2), nitrogen oxides (NOx), and sulfur dioxide (SO2).Data reported as part of this project include hourly average, minimum, and maximum emission factors by month; that is, the average, minimum, and maximum emission factor for the same hour of each day in a month. Please note that the data are reported in lbs/MWh, where the MWh value reported is site electricity use (the actual electricity used at the building) and the pounds of emissions reported are the emissions created at the generator to meet the building load, including transmission and distribution losses. The demand profiles used to generate the data pertain to the following years: eastern interconnect - 2005; Electricity Reliability Council of Texas (ERCOT) - 2008; Western Electricity Coordinating Council (WECC) - 2008.

Middle-Layer Super Output Areas (MSOAs) are a statistical geography created for the Census of England and Wales. In 2021 there are 7,264 MSOAs in England and Wales, with a typical population of 7,000-10,000 people. Each MSOA sits within a local authority, and each local authority is divided into between one and 132 MSOAs depending on how many people live there.

This page contains available Vermont land cover data in GIS format.

This data has been produced through the Modern energy Partnerships programme. This is a collaborative programme led by the Department for Business, Energy and Industrial Strategy (BEIS), alongside Cabinet Office, HM Treasury and the estate-owning departments, while Energy Systems Catapult provides programme delivery, analytical and technical support. It focussed on campus style sites such as hospitals, prisons and military bases, and sought to develop a mechanism that allows the public estate to achieve at least 50% untraded carbon emissions reductions by 2032 against a 2017 baseline. The data consists of typical average, maximum and minimum costs to purchase and install different low carbon technologies on these types of site. This has been gathered through the development of 24 decarbonisation pathways (Concept Designs) for a range of different public sector sites owned by National Health Service (NHS), Ministry of Defence (MOD) and Ministry of Justice (MOJ).

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.

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 Weather data form the RSF systems energy model. The weather data was used to contextualize and compare energy use data like heating and cooling. The weather data was also used by the RSF Energy Model to model the energy use of the RSF. Energy Model data and Measured Energy data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

The Overwater Structures in Marine Waters of Washington State is made up of thousands of digitized overwater structures such as docks, bridges, floats, structural support fill, and other structures such as floating homes. Structures were digitized from three (3) foot/one (1) meter resolution color orthophotos taken between 2002 to 2006 by either the Washington State Department of Natural Resources (Washington DNR) or the United States Department of Agriculture National Agriculture Imagery Program (NAIP). The data layer is statewide and includes all marine areas classified as definitely or probably navigable by the Revised Code of Washington (RCW 90.56.010(15)), as well as a limited set of areas classified as possibly navigable. The datatable has been standardized so that overwater structures can be looked up based on structure type, structure size, waterbody name, township, county, or waterbody navigability. NOTE: While Washington DNR is willing to share the data with other entities, there are currently no plans to QA/QC the data or maintain it.To map and quantify the extent of overwater structures on and adjacent to state-owned aquatic lands as part of the take analysis for a proposed State-owned Aquatic Lands Habitat Conservation Plan (HCP).

The Facility Inventory and Condition Assessment Program (FICAP) system is used effectively to monitor and plan for facility preservation, renovation and construction activities at Washington State Parks. The facility inventory GIS dataset provides the location of built structures owned or managed by the Washington State Parks & Recreation Commission - showing name, condition, size, construction date & cost. Also includes links to photos of each facility in the attribute table. Data is updated annually. Last updated August 2022. To download this and other data from Washington State Parks, go to geo.wa.gov and search for "wsprc" (Washington State Parks and Recreation Commission).

This layer contains data (in point geometry) on park accommodations for visitors to Washington State Parks (Restroom facilities, kitchen shelters, picnic tables, ranger stations and so forth.) The layer is derived from the agency's facilities inventory dataset.Attribute Definitions:ParkName - Name of the State Park or State Parks property the structure is in.Name – Type of the public accommodation structure.UFI - ID number assigned by WA DES's (Washington Department of Enterprise Services) Facility Inventory Condition Assessment Program (FICAP).Photos - A weblink to photo attachments for the structure.DateConst - Date the facility was constructed.Long - Longitude of the structure.Lat – Latitude of the structure. To download this and other data from Washington State Parks, go to geo.wa.gov and search for "wsprc" (Washington State Parks and Recreation Commission).

The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.

Office for National Statistics - ONS Postcode Directory Look-up Tool enables you to locate and obtain further information for a postcode on an interactive map. The tool will also allow you to export the desired postcode information as a CSV.

The State Energy Efficient Appliance Rebate Program (SEEARP) reports database includes rebate reports (xls) and summary factsheets (pdf) from states and territories that participated in SEEARP. With funding provided by the American Recovery and Reinvestment Act of 2009 (ARRA), the U.S. Department of Energy (DOE) developed SEEARP to spur economic activity and invest in long-term energy savings by helping consumers replace older, inefficient appliances with new, efficient models. SEEARP provided almost $300 million to the 56 U.S. states and territories to support state-level consumer rebate programs for efficient appliances from December 1, 2009 to February 17, 2012. The successes and challenges of SEEARP provide valuable lessons for designing and running a consumer-focused appliance rebate program. In addition to the SEEARP reports database, the two reports included provide a results and program design lessons.

To further transparency and openness, DOE established a policy to document and post online all CX determinations involving classes of actions listed in Appendix B to Subpart D of the DOE NEPA regulations (10 CFR Part 1021). This database contains CX determinations required to be posted under the policy, and also some for which documentation and posting are optional, i.e., determinations involving classes of actions listed in Appendix A or made before the policy's effective date of November 2, 2009.

Points depict the locations of the Washington State Department of Transportation's (WSDOT) buildings. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Area Maintenance FacilityPrimary maintenance facility centrally located to serve a designated Area within a Region. Typically includes auto mechanic shop, fuel service, and storage for vehicles and consumable inventory (e.g., gravel, sand, salt, paint).

Points depict the locations of the Washington State Department of Transportation's (WSDOT) buildings. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Headquarters/Region Headquarters FacilityStatewide or regionwide headquarters facility. Regionwide HQ typically includes auto, welding, carpentry, fabrication shops and supporting ancillary storage buildings as well as office space for project engineering and administrative functions.

Points depict the locations of the Washington State Department of Transportation's (WSDOT) buildings. GPS data was collected for both state-owned and leased facilities used by the WSDOT.

Points depict the locations of the Washington State Department of Transportation's (WSDOT) buildings. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Pits, Quarries, Stockpile, and Waste SiteLess-developed land used intermittently to quarry, process, and store consumable highway maintenance products such as sand or gravel. Typically supports one or no buildings and serves highway segments that are in proximity to the site.

Points depict the locations of the Washington State Department of Transportation's (WSDOT) buildings. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Project Engineering SiteTypically a stand-alone office building that supports planning, design, and project engineering activities. May support more than one Maintenance Area.

Points depict the locations of the Washington State Department of Transportation's (WSDOT) buildings. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Regionwide Support FacilitySpecialized facilities that support statewide or regionwide needs such as materials laboratories, sign fabrication, or traffic and signals shops.

Points depict the locations of the Washington State Department of Transportation's (WSDOT) buildings. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description -Safety Rest AreasEach Safety Rest Area is comprised of a network of buildings and major site utilities that support services to the travelling public at that location (e.g., a public restroom, wellhouse, and wastewater pump station).

Points depict the locations of the Washington State Department of Transportation's (WSDOT) buildings. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Section Maintenance FacilitySecondary maintenance facility located to serve specific highway segments within a Maintenance Area. This type of facility may specialize in materials or equipment storage that meet the specific needs of a particular locale (e.g., snow and ice fighting equipment in mountainous areas)

Points depict the locations of the Washington State Department of Transportation's (WSDOT) sites. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Safety Rest AreasEach Safety Rest Area is comprised of a network of buildings and major site utilities that support services to the travelling public at that location (e.g., a public restroom, wellhouse, and wastewater pump station).

Points depict the locations of the Washington State Department of Transportation's (WSDOT) sites. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Headquarters/Region Headquarters FacilityStatewide or regionwide headquarters facility. Regionwide HQ typically includes auto, welding, carpentry, fabrication shops and supporting ancillary storage buildings as well as office space for project engineering and administrative functions.

Points depict the locations of the Washington State Department of Transportation's (WSDOT) sites. GPS data was collected for both state-owned and leased facilities used by the WSDOT.

Points depict the locations of the Washington State Department of Transportation's (WSDOT) sites. GPS data was collected for both state-owned and leased facilities used by the WSDOT.Facility Type & Description - Project Engineering SiteTypically a stand-alone office building that supports planning, design, and project engineering activities. May support more than one Maintenance Area.

These datasets contain points and polygons (of buildings over a certain size) of all buildings in West Virginia. The data was collected from 2003 natural color orthophotography by the WV Statewide Addressing and Mapping Board. These datasets contain all buildings, regardless of occupancy status. This data has never been validated. These data were originally published as two separate shapefiles, north and south, in WV State Plane projection. Upon receipt of the data, the West Virginia GIS Technical Center reprojected the datasets to UTM NAD83 Zone 17 North and merged the north and south shapefiles into one, statewide dataset.