*This submission updates a 2015 submission for the utilization analysis (https://gdr.openei.org/submissions/623)* The files document the analysis of utilization potential in support of Phase 1 Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This 2016 submission includes data pertinent to the methods and results of an analysis of the Surface Levelized Cost of Heat (SLCOH) for US Census Bureau 'Places' within the study area. The SLCOHis was calculated using a modification of a program called GEOPHIRES, available at http://koenraadbeckers.net/geophires/index.php. In addition to calculating SLCOH, this task also identified many industrial sites that may be prospects for use of a geothermal district heating system, based on their size and industry, rather than on the SLCOH. An industry sorted listing and maps of the sites have been plotted as a layer onto different iterations of maps combining the three geologic risk factors (Thermal Quality, Natural Reservoir Quality, and Risk of Seismicity). In addition, a shapefile of the industrial sites is also included with 7 associated files. Supporting files are also supplied.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive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

Note: Updates to this data product are discontinued. The Commodity and Food Elasticities Database is a collection of elasticities from research on consumer demand published in working papers, dissertations, and peer-reviewed journals and as presented at professional conferences in the United States.

Data showing the demand suppression attributed to the Coronavirus Pandemic. Demand suppression is calculated by taking the National Demand + Embedded Generation outturns and comparing this with the modelled demand assuming no Coronavirus pandemic given the same model parameters (day of week, temperature, etc.). This is split into various periods of the day (timings are half-hour ending): + Night: 00:30 to 07:00 (Trough) + Morning: 07:30 to 13:00 (Peak) + Afternoon: 13:30 to 16:30 (Trough) + Peak: 17:00 to 20:30 (Peak) + Evening: 21:00 to 00:00 (Peak) It also shows the overall suppression (Whole Day). Demand drops over bank holidays are capturing both the pandemic effect as well as the effect of the bank holidays themselves which under normal conditions cause drops in demand also. We do not have sufficient data to separate these effects. This methodology works at a national level with a linear model using least squares regression and cannot be adapted for use in our regional time-based demand models. In agreement with Ofgem & BEIS we are ceasing the publication and reporting of the demand suppression assessment with effect of 19 May 2021. Data on demand suppression was being calculated using a comparison with pre-COVID demand levels, and we can no longer be sure that any changes are attributable to COVID alone. We hope that you found the ESO's assessment of the demand suppression insightful and useful.

This project estimates hourly demand response availability across the continental U.S. for the year 2006. The resulting data set is disaggregated by balancing authority area, end use, and grid application. End uses include 14 categories across residential, commercial, industrial and municipal sectors. Grid applications include the 5 bulk power system services of regulation reserve, flexibility (or ramping) reserve, contingency reserve, energy, and capacity. Based on the physical requirements of the various bulk power system services and the estimated end use electric load shapes, potential availability of demand response is calculated and provided as a series of csv files.

This data set contains the full-resolution and state-level data described in the linked technical report (https://www.nrel.gov/docs/fy18osti/71492.pdf). It can be accessed with the NREL-dsgrid-legacy-efs-api, available on GitHub at https://github.com/dsgrid/dsgrid-legacy-efs-api and through PyPI (pip install NREL-dsgrid-legacy-efs-api). The data format is HDF5. The API is written in Python. This initial dsgrid data set, whose description was originally published in 2018, covers electricity demand in the contiguous United States (CONUS) for the historical year of 2012. It is a proof-of-concept demonstrating the feasibility of reconciling bottom-up demand modeling results with top-down information about electricity demand to create a more detailed description than is possible with either type of data source on its own. The result is demand data that is more highly resolved along geographic, temporal, sectoral, and end-use dimensions as may be helpful for conducting electricity sector-wide "what-if" analysis of, e.g., energy efficiency, electrification, and/or demand flexibility. Although we conducted bottom-up versus top-down validation, the final residuals were significant, especially at higher geographic and temporal resolution. Please see the Executive Summary and/or Section 3 of the report to obtain an understanding of the data set limitations before deciding whether these data are suitable for any particular use case. New dsgrid datasets are under development. Please visit https://www.nrel.gov/analysis/dsgrid.html for the latest information which is also linked in the data resources.

Demand.ninja Tool is a customisable model for hourly heating and cooling demand applicable globally at all spatial scales.

This data was compiled for the 'Early Market Opportunity Hot Spot Identification' project. The data and scripts included were used in the 'MHK Energy Site Identification and Ranking Methodology' Reports (see resources below). The Python scripts will generate a set of results--based on the Excel data files--some of which were described in the reports. The scripts depend on the 'score_site' package, and the score site package depends on a number of standard Python libraries (see the score_site install instructions).

Annual data on electricity generating capacity, electricity generation and useful thermal output, fuel receipts, fuel stocks, sales, consumption, and emissions in the United States. Based on Form EIA-861 and Form EIA-860 data. Annual time series extend back to 1994.

Monthly, quarterly, and annual data on electricity generation, consumption, retail sales, price, revenue from retail sales, useful thermal output, fossil fuel stocks, fossil fuel receipts, and quality of fossil fuel. Data organized by fuel type, i.e., coal petroleum, natural gas, nuclear, hydroelectric, wind, solar, geothermal, and wood. Also, data organized by sector, i.e., electric power, electric utility, independent power producers, commercial, and industrial. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

The Bolivian governments concerns that are related to reducing the consumption of diesel fuel, which is imported, subsidized, and provided to isolated electric plants in rural communities, have led to the implementation of hybrid power systems. The data in this submissions was created to compare a photovoltaic (PV)/Stirling battery system to a more traditional (PV)/diesel/battery system. The data includes: - MATLAB Simulink model of a Parabolic dish-Stirling engine-battery system. - Input data (Meteorological and load demand) for El Carmen, Tablani, and Pojo Pata communities

This is a final report summarizing a two-year (2014-16) DOE funded Geothermal Play Fairway Analysis of the Low-Temperature resources of the Appalachian Basin of New York, Pennsylvania and West Virginia. Collaborators included Cornell University, Southern Methodist University, and West Virginia University. As a result of the research, 'play fairways' were identified for further study, based on four risk criteria: 1) the Thermal Resource Quality, 2) the Natural Reservoir Quality, 3) the Risk of Seismic Activity, and the 4) Utilization Viability. In addition to the final report document, this submission includes project 'memos' referred to throughout the report. Many of these same memos are also provided in the submissions with the detailed data products accompanying the relevant risk factor (thermal, reservoir, seismicity, and utilization). This report updates a preliminary version submitted in late 2015 (Submission 559 - See "Reservoir Analysis 2015" below) This file presents the Final Report and Supporting Documents for a Geothermal Play Fairway Analysis of the Appalachian Basin sectors of New York, Pennsylvania and West Virginia. The purpose of this Department of Energy funded effort was to assess the potential for viable low temperature (50-150 degrees C) geothermal energy exploration and development using the methods of Play Fairway Analysis. The resources analyzed occur at depths of 1000 m and greater below the surface, and the application scenarios considered are for direct utilization of the heat. This report illustrates the lateral variability of each of the four risk criteria. This report also illustrates multiple alternative methods to combine those factors in order to communicate the estimated overall favorability of geothermal development. Uncertainty in the risk estimation is also quantified. Based on these metrics, geothermal plays in the Appalachian Basin were identified as potentially viable for a variety of direct-use-heat applications. The methodologies developed in this project and presented in this report may be applied in other sedimentary basins as a foundation for geothermal resource, risk, and uncertainty assessment. Accompanying this report is an Appendix that describes in greater detail the methods used in the analysis, and 17 other technical memos that document criteria, methods and decisions on which the final product was built.

This dataset contains approximate GIS (geographic information system) geospatial boundaries for each of the DNO License Areas in Great Britain. There are 14 licensed Distribution Network Operators (DNOs) in Britain and each is responsible for a regional distribution services area. DNO License Areas are sometimes referred to as GSP Groups and historically have also been known as Public Electricity Supplier (PES) regions, though the latter name is no longer widely used. The DNO License Area boundaries are unusual because they are only well defined when tested. The boundaries tend to run through rural areas and change over time whenever a new connection is added close to a boundary. For example, a new connection close to an existing boundary can approach the DNO responsible for both neighbouring License Areas to ask for a connection, and is free to choose either depending on how much they will charge - once the new connection is made the boundary is, theoretically, re-drawn to ensure that the new connection falls into the correct License Area. The boundaries in this dataset were shared by Western Power Distribution (the DNO responsible for 4 of the 14 License Areas) and come with the disclaimer that they are probably a little outdated and not 100% accurate. That said, they are a good indication of the geography of DNO License Areas. DNO License Areas are widely used as an aggregation entity when reporting electricity network data, for example, in Elexon's publication of electricity settlement data. As such, they are also useful for regional modelling of the GB electricity transmission network, for example to aggregate distributed embedded generator energy flows onto the transmission network. A good example of this is the Regional PV_Live outturn estimates published by The University of Sheffield (https://www.solar.sheffield.ac.uk/pvlive/regional/).

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.

This is the data for the first of three National Grid Electricity Distribution (NGED) short data challenges! The aims of these challenges include: - Demonstrating the value in making data openly available - Increasing the visibility of some of the challenges network operators face - Increase the understanding of the different ways to tackle some of these problems - Providing high quality and accurate benchmarks with which to enable innovation and research Note all the slides from the kick-off and other information can be found on our LinkedIn group: https://www.linkedin.com/groups/9025332/ This initial challenge aims to understand how accurately high resolution features can be estimated given only information from lower resolution data. Specifically we are asking participants to estimate the highest peak value and lowest trough at a one minute resolution within each half hourly period given only half hourly measurements. This is an interesting problem to a distribution network operator as the spikes in demand can mean strain on their network. Such issues may become increasingly common, especially on the lower voltages of the network, due to the expanding use of lower carbon technologies such as electric vehicles, and heat pumps. However, monitoring can be expensive (especially in the long term) as it requires investment in additional storage, communications equipment and processing units.

This is the second of three National Grid Electricity Distribution (NGED) short data challenges! The aims of these challenges include: 1. Demonstrating the value in making data openly available 2. Increasing the visibility of some of the challenges network operators face 3. Increase the understanding of the different ways to tackle some of these problems 4. Providing high quality and accurate benchmarks with which to enable innovation and research This challenge aims to estimate the increased demand created on primary substations by the installation of electric vehicle (EV) chargers. High uptakes of EVs will cause more strain on the distribution networks, especially at the low voltage level where there is limited headroom for high demand technologies such as EVs. Unfortunately, the amount of EV chargers installed, and hence the potential extra demand on a network, may be unknown, even to a distribution network operator (DNOs). However, DNOs do monitor the demand at the substation level and this shows the aggregated demand from all connected appliances and, in addition, many DNOs do have example data for EV demand. The ask for this challenge is whether this information can be combined with other data to help predict the increased demand generated from EV chargers on a network. All the slides from the kick-off and other information can be found on the [LinkedIn group](https://www.linkedin.com/groups/9025332/) and the recording from the kick-off event will be uploaded shortly after the event. The challenge page is here: https://codalab.lisn.upsaclay.fr/competitions/1324

This is the final of three National Grid Electricity Distribution (NGED) short data challenges! The aims of these challenges include: 1. Demonstrating the value in making data openly available 2. Increasing the visibility of some of the challenges network operators face 3. Increase the understanding of the different ways to tackle some of these problems 4. Providing high quality and accurate benchmarks with which to enable innovation and research This final challenge is a unique spin on the traditional missing data problem which considers different scenarios for missing data across the hierarchy of a distribution network (Primary up to grid supply point). Traditional missing data problems typically focus on an isolated time series and often require imputation approaches based on local measurements. In this challenge, we are asking participants to utilise monitoring data across the network (as well as weather and other publicly available data) to solve various missing data scenarios which extend the problem to situations where multiple connected substations may lose data. All the slides from the kick-off and other information can be found on the [LinkedIn group](https://www.linkedin.com/groups/9025332/) and the recording from the kick-off event will be uploaded shortly after the event.

In April 2018, the ESO initiated strategic transformation project to develop and implement state of art forecasting capability to deliver value to consumers by providing accurate possible, user-friendly comprehensive forecasts to our stakeholders to make informed decisions ahead of real-time Our strategic forecasting project aims to replace our existing energy forecasting system (EFS) with an advanced cloud-based platform for energy forecasting (PEF) while designing & improving forecasting models, methodologies and apply advanced statistical learning & machine learning modelling techniques & automation. On this page, you will find updates to the roadmap for the project.

This data set was used to calculate the technical potential and economic feasibility of transported geothermal energy, according to the methodology outlined in the final report included below.

This submission of Utilization Analysis data to the Geothermal Data Repository (GDR) node of the National Geothermal Data System (NGDS) is in support of Phase 1 Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. The submission includes data pertinent to the methods and results of an analysis of the Surface Levelized Cost of Heat (SLCOH) for US Census Bureau Places within the study area. This was calculated using a modification of a program called GEOPHIRES, available at http://koenraadbeckers.net/geophires/index.php. The MATLAB modules used in conjunction with GEOPHIRES, the MATLAB data input file, the GEOPHIRES output data file, and an explanation of the software components have been provided. Results of the SLCOH analysis appear on 4 .png image files as mapped risk of heat utilization. For each of the 4 image (.png) files, there is an accompanying georeferenced TIF (.tif) file by the same name. In addition to calculating SLCOH, this Task 4 also identified many sites that may be prospects for use of a geothermal district heating system, based on their size and industry, rather than on the SLCOH. An industry sorted listing of the sites (.xlsx) and a map of these sites plotted as a layer onto different iterations of maps combining the three geological risk factors (Thermal Quality, Natural Reservoir Quality, and Risk of Seismicity) has been provided. In addition to the 6 image (.png) files of the maps in this series, a shape (.shp) file and 7 associated files are included as well. Finally, supporting files (.pdf) describing the utilization analysis methodology and summarizing the anticipated permitting for a deep district heating system are supplied. UPDATE: Newer version of the Utilization Analysis has been added here: https://gdr.openei.org/submissions/878