Time-coincident load, wind, and solar data including actual and probabilistic forecast datasets at 5-min resolution for ERCOT, MISO, NYISO, and SPP. Wind and solar profiles are supplied for existing sites as well as planned sites based on interconnection queue projects as of 2021. For ERCOT actuals are provided for 2017 and 2018 and forecasts for 2018, and for the remaining ISOs actuals are provided for 2018 and 2019 and forecasts for 2019. There datasets were produced by NREL as part of the ARPA-E PERFORM project, an ARPA-E funded program that aim to use time-coincident power and load seeks to develop innovative management systems that represent the relative delivery risk of each asset and balance the collective risk of all assets across the grid. For more information on the datasets and methods used to generate them see https://github.com/PERFORM-Forecasts/documentation.

The Annual Energy Outlook presents longterm annual projections of energy supply, demand, and prices focused on the U.S. through 2050, based on results from EIA's National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internally-consistent sets of assumptions, the results of which are presented as cases. The analysis in AEO2014 focuses on five primary cases: a Reference case, Low and High Economic Growth cases, and Low and High Oil Price cases. 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 Annual Energy Outlook API presents long-term annual projections of energy supply, demand, and prices through 2040. The projections, focused on U.S. energy markets, are based on results from EIA’s National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internally-consistent sets of assumptions, the results of which are presented as cases. The projections cover natural gas, petroleum, coal, electricity and renewable fuels by sector (residential, commercial, industrial, electric generation, and transportation) and by region (census). 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 KNMI Climate Explorer is a web application to analysis climate data statistically. It contains more than 10 TB of climate data and dozens of analysis tools. It is part of the WMO Regional Climate Centre at KNMI.

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.

ECMWF produces and disseminates weather forecast data for the National Meteorological and Hydrological services (NHMSs) of ECMWF member and co-operating states and their authorised users. The data are released 1 hour after the real-time dissemination schedule. A Python package called ecmwf-opendata is available from PyPi that greatly facilitates the access to this dataset. In addition, a series of Jupyter Notebooks have been developed to demonstrate the use of this package. Copyright statement: Copyright "© [year] European Centre for Medium-Range Weather Forecasts (ECMWF)". Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.

Great Lakes temperature and precipitation forecast tools. History from 08-2011 - 05-2016 Forecasts with initiation from 08-2011 to 01-2017 https://web.archive.org/web/*/https://www.glerl.noaa.gov/data/climateForecasts/

This endpoint provides a forward view of availability (also referred to as Output Useable data under the Grid Code) for generation and interconnector capacity, accounting for planned outages covering 2 days ahead to 14 days ahead; it is aggregated by Fuel Types categories.

This endpoint provides a forward view of availability (also referred to as Output Useable data under the Grid Code) for generation and interconnector capacity, accounting for planned outages covering availability data from 2 weeks ahead to 156 weeks ahead; it is aggregated by Fuel Types categories.

These data sets contain freight forecast, performance and other statistics. The data includes: * Strategic Freight Forecasts - NSW freight commodity demand volume forecasts for the 40 year period between 2016 to 2056 * Freight performance dashboard – Strategic Targets from NSW Freight and Ports Plan 2018-2023 including + Use of rail freight + Road safety + Rail freight access + Rail freight capability + Port Botany Efficiency Detailed information for drivers and rationale used to produce NSW freight commodity demand volume forecasts can be found in the [NSW Freight Commodity Demand Forecasts 2016-56 Report](https://www.transport.nsw.gov.au/system/files/media/documents/2018/NSW%20Freight%20Commodity%20Demand%20Forecasts%202016-56%5Baccessible%5D_0.pdf). A visualisation of the Strategic Freight Forecasts is available on the Transport for NSW Website under [Freight data](https://www.transport.nsw.gov.au/data-and-research/freight-data). Additional information on above Strategic Targets is available in the [NSW Freight and Ports Plan 2018-2023](https://www.transport.nsw.gov.au/projects/strategy/nsw-freight-and-ports-plan). Visualisations of the Strategic Targets are available on the Transport for NSW Website under [Freight data](https://www.transport.nsw.gov.au/data-and-research/freight-data).

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

The International Energy Outlook (IEO) presents EIA's long-term assessment of world energy markets. The IEO includes projections of world energy demand by region and primary energy source through 2040; electricity generation by fuel type; and energy-related carbon dioxide emissions.

Collection of presentations: GOES-R Proving Ground Status: CIMSS/ASPB Participation; GOES-R Proving Ground: CIRA/RAMMB Progress Report; GOES-R Proving Ground: SPoRT Update; JPSS Proving Ground and Risk Reduction; Overview of Satellite and Above-Boundary Layer Observations

This endpoint provides a forward view of availability (also referred to as Output Useable data under the Grid Code) for generation and interconnector capacity, accounting for planned outages covering 2 days ahead to 14 days ahead. The data is aggregated at national level.

This endpoint provides a forward view of availability (also referred to as Output Useable data under the Grid Code) for generation and interconnector capacity, accounting for planned outages covering availability data from 2 weeks ahead to 156 weeks ahead. The data is an aggregation of all Fuel Type categories at national level.

The "Operational Forecasting" dataset provides a forecast view of demand and generation for each Grid and Primary network group for our SPD and SPM licence areas, for each half-hourly period.Note, this data has been triaged to remove information pertaining to individual customers or where the dataset contains sensitive information.This dataset is updated on a daily basis.Definitions:NameDescriptionUnit of MeasurementLicence AreaSPD: Licenced Distribution Network Operator for Central Belt and South of Scotland.SPM = Licenced Distribution Network Operator for  North Wales, Merseyside, Cheshire and North Shropshire.N/ADistrictA geographic split of each of licence areas. Our SPD licence area is geographically split into 6 Districts and our SPM licence area is split into 5 Districts. For further information please refer to: Our Distribution Network - SP Energy Networks.N/ANetwork Group NameThe unique name given to the network demand group supplied by the Primary or Grid transformers.In our SP Manweb license, some of our network operates solidly interconnected. For example, geographically dispersed single transformers may supply interconnected network instead of multi-transformer substations. For more information on network arrangements, including both Single Line schematic diagrams and geographical maps please refer to our Long Term Development Statement (LTDS).N/ANominal VoltageRefers to the nominal operating voltage of the network group.kVTimestampDate / time that the data was recorded - data is provided at half hourly intervals over a 12 month period.N/AGeneration Forecast (MW)The average generation forecast for each Grid and Primary network group.Mega Watt (MW)Net Demand Forecast (MW)The average demand forecast for each Grid and Primary network group.Mega Watt (MW)Uderlying Demand (MW)The indicative level of demand in this network group during this half-hour period.Mega Watt (MW)

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 product provides three Excel file spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). #### Using Futures Prices to Forecast the Season-Average Price and Counter-Cyclical Payment Rate for Corn, Soybeans, and Wheat Farmers and policymakers are interested in the level of counter-cyclical payments (CCPs) provided by the 2008 Farm Act to producers of selected commodities. CCPs are based on the season-average price received by farmers. (For more information on CCPs, see the ERS 2008 Farm Bill Side-By-Side, Title I: Commodity Programs.) This data product provides three Excel spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). Users can view the model forecasts or create their own forecast by inserting different values for futures prices, basis values, or marketing weights. Example computations and data are provided on the Documentation page. #### Spreadsheet Models For each of the three major U.S. field crops, the Excel spreadsheet model computes a forecast for: 1. the national-level season-average price received by farmers and 2. the implied counter-cyclical payment rate. Note: the model forecasts are not official USDA forecasts. See USDA's World Agricultural Supply and Demand Estimates for official USDA season-average price forecasts. See USDA's Farm Service Agency information for official USDA CCP rates.

The PV_Live web API (Application Programming Interface) provides access to near-real-time and historical estimates of PV generation on the GB transmission network. It returns a computer-friendly (JSON) response for use in software, websites, apps etc. The Sheffield Solar research group is formed in the Physics & Astronomy department, at the University of Sheffield, as part of the Grantham Centre for Sustainable Futures. It works to bridge the gap between the research lab and how solar photovoltaic (PV) technology is used in the real world and to understand its performance and impact.

The PV_Forecast service supplies forecasts of nationally- and regionally-aggregated electricity generation from solar Photovoltaics (PV) connected to the GB transmission network. Sheffield Solar currently run three tiers of PV_Forecast: Nationally-aggregated, regionally-aggregated by DNO License Area (a.k.a PES or GSP Group) and regionally-aggregated by Grid Supply Point (GSP). The Sheffield Solar research group is formed in the Physics & Astronomy department, at the University of Sheffield, as part of the Grantham Centre for Sustainable Futures. It works to bridge the gap between the research lab and how solar photovoltaic (PV) technology is used in the real world and to understand its performance and impact.

The API provides data back to 1990 and projections annually, monthly, and quarterly for 18 months. Summarizes the outlook for demand, supply and prices for petroleum, natural gas, electricity and coal as well as projections of carbon dioxide emissions from the production of fossil fuels, and a discussion of price forecast uncertainty. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

This API provides data back to 1990 and projections annually, monthly, and quarterly for 18 months. It provides data on economic output, income, expenditures, employment, production and price indexes. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

This API provides data back to 1990 and projections annually, monthly, and quarterly for 18 months. Summarizes the outlook for U.S. petroleum and other liquids supply, consumption, inventories, refining, and prices. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

This endpoint provides a forward view of availability (also referred to as Output Useable data under the Grid Code) for generation and interconnector capacity, accounting for planned outages covering availability data from 2 days ahead to 14 days ahead; it is aggregated by National Grid Balancing Mechanism Units (NGC BMUs). In the context of this report, BMUs can be considered as generating units. Elexon BMUs differs from NGC BMUs by including a prefix e.g. 'T_'. The mapping between NGC and Elexon BMUs can be retrieved via reference data API endpoints.

This endpoint forward view of availability (also referred to as Output Useable data under the Grid Code) for generation and interconnector capacity, accounting for planned outages covering availability data from 2 weeks ahead to 156 weeks ahead; it is aggregated by Balancing Mechanism Units (BMUs). In the context of this report, BMUs can be considered as generating units. Elexon BMUs differs from NGC BMUs by including a prefix e.g. 'T_'. The mapping between NGC and Elexon BMUs can be retrieved via reference data API endpoints.