6 datasets found
  1. Prediction Of Worldwide Energy Resources (POWER)

    • catalog.data.gov
    • datasets.ai
    • +4more
    Updated Apr 11, 2025
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    National Aeronautics and Space Administration (2025). Prediction Of Worldwide Energy Resources (POWER) [Dataset]. https://catalog.data.gov/dataset/prediction-of-worldwide-energy-resources-power
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    Dataset updated
    Apr 11, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    The POWER Project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly (by year 12 months + annual averages), and climatology. The POWER Data Archive provides data at the native resolution of the source data products. The data is updated nightly to maintain Near Real Time (NRT) availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER Project targets three specific user communities: Renewable Energy (RE), Sustainable Buildings (SB), and Agroclimatology (AG). The POWER Projects provides community specific parameters, output formats, naming conventions, and units that are commonly employed by each user community. The POWER Services Catalog consists of a series of RESTful Application Programming Interfaces (API), geospatial enabled image services, and a web mapping Data Access Viewer (DAV). These three different service offerings support data discovery, access, and distribution to our user base as ARD and as direct application inputs to decision support tools.

  2. NASA Prediction of Worldwide Energy Resources (POWER)

    • registry.opendata.aws
    Updated Jun 1, 2022
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    NASA (2022). NASA Prediction of Worldwide Energy Resources (POWER) [Dataset]. https://registry.opendata.aws/nasa-power/
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    Dataset updated
    Jun 1, 2022
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.

    The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.

    The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.

    The latest data version update includes hourly-based source ARD, in addition to enhanced daily, monthly, annual, and climatology data. The daily time series for meteorology is available from 1981, while solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning from 1984 for meteorology and from 2001 for solar-based parameters. The hourly data equips users with the ARD needed to model building system energy performance, providing information directly amenable to decision support tools introducing the industry standard EnergyPlus Weather file format.

  3. c

    Meteorological and load profile data set

    • esango.cput.ac.za
    txt
    Updated Nov 22, 2022
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    Ibukun Fajuke (2022). Meteorological and load profile data set [Dataset]. http://doi.org/10.25381/cput.21546108.v1
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    txtAvailable download formats
    Dataset updated
    Nov 22, 2022
    Dataset provided by
    Cape Peninsula University of Technology
    Authors
    Ibukun Fajuke
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    2021FEBEREC-STD-117

    The data set consists of the hourly load demand, hourly solar irradiance and hourly wind speed of a community located in Northern part of Nigeria named Bara, Kirfi Local Government area of Bauchi state, Nigeria. The hourly solar resource data and wind resource data of the community for a period of one year is obtained from an existing database of the Power Data Access Viewer of National Aeronautic and Space Administration (NASA).

  4. a

    POWER Data Access Viewer

    • opengeoversity-geoap.hub.arcgis.com
    Updated Feb 14, 2019
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    NASA ArcGIS Online (2019). POWER Data Access Viewer [Dataset]. https://opengeoversity-geoap.hub.arcgis.com/items/db2adab099f246f6b4d4c113f2a5ea09
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    Dataset updated
    Feb 14, 2019
    Dataset authored and provided by
    NASA ArcGIS Online
    Area covered
    Description

    The Prediction of Worldwide Energy Resource (POWER) Project is funded through the NASA Applied Sciences Program within the Earth Science Mission Directorate. The POWER Project supports three user communities with solar and/or meteorological data: 1) Renewable Energy (RE), 2) Sustainable Buildings (SB), and 3) Agroclimatology (AG)POWER Data Sources:The POWER project provides access to community-based Analysis Ready Data (ARD) for meteorology and solar-related parameters, specifically formulated for assessing and designing renewable energy systems.The data is available on at the source models’ native latitude and longitude global grid.Temporal levels include Hourly, Daily, Monthly, Annual, and Climatology. Download options include single point, regional, and global data.Formats include NetCDF, CSV, ASCII, geoJSON, ICASA, & EPW.Meteorological parameters are derived from:NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – 3 Months Behind Near Real Time)NASA's GEOS 5.12.4 FP-IT archive (End of MERRA2 – Near Real Time)Solar parameters are derived from:NASA's GEWEX/SRB release 4.0 archive (Jan. 1, 1984 – Dec. 31, 2000) NASA's CERES SYN1deg (Jan. 1, 2001 – 3 Months Behind Near Real Time)NASA's FLASHFlux (3 Months Behind Near Real Time – Near Real Time)If you have any comments or questions, please do not hesitate to contact us at larc-power-project@mail.nasa.gov

  5. Model America - Energy Simulation Output for 2022 Summer in Arizona from...

    • zenodo.org
    csv
    Updated Feb 4, 2025
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    Hang Li; Hang Li; Fengqi Li; Fengqi Li; Joshua New; Joshua New; SHOVAN CHOWDHURY; SHOVAN CHOWDHURY; Avery Stubbings; Avery Stubbings (2025). Model America - Energy Simulation Output for 2022 Summer in Arizona from ORNL's AutoBEM [Dataset]. http://doi.org/10.5281/zenodo.14783657
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    csvAvailable download formats
    Dataset updated
    Feb 4, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Hang Li; Hang Li; Fengqi Li; Fengqi Li; Joshua New; Joshua New; SHOVAN CHOWDHURY; SHOVAN CHOWDHURY; Avery Stubbings; Avery Stubbings
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Arizona
    Description

    Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

    This dataset include the energy simulation output using NASA Power 2022 weather data (June 1st-August 31st) for each county in AZ. The output field includes basic energy simulation outputs and Anthropogenic Emissions.

    Building Information Data Fields:

    • ID
    • CZ
    • Centroid
    • State_Abbr
    • Footprint2D
    • Height,Area2D
    • BuildingType
    • NumFloors
    • Area
    • Standard
    • NumWalls
    • WWR_surfaces

    Energy Simulation Data Fields:

    • Electricity_Facility[kBTU]
    • NaturalGas_Facility[kBTU]
    • Heating_Electricity[kBTU]
    • Cooling_Electricity[kBTU]
    • Heating_NaturalGas[kBTU]
    • Heating_Total[kBTU]
    • WaterSystems_Electricity[kBTU]
    • Lighting_Electricity[kBTU]
    • Equipment_Electricity[kBTU]
    • Fans_Electricity[kBTU]
    • Pumps_Electricity[kBTU]
    • HeatRejection_Electricity[kBTU]
    • HeatRecovery_Electricity[kBTU]
    • Surface_Outside_Face_Heat_Emission[GJ]
    • Zone_Exfiltration_Heat_Loss[GJ]
    • Zone_Exhaust_Air_Heat_Loss[GJ]
    • Heat_Rejection_Energy[GJ]
    • Anthropogenic_Emissions[GJ]
    This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).
  6. d

    The data of COVID-19 and their correlation with wind speed

    • search.dataone.org
    • data.niaid.nih.gov
    • +1more
    Updated Nov 30, 2023
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    Dewi Susanna (2023). The data of COVID-19 and their correlation with wind speed [Dataset]. http://doi.org/10.5061/dryad.6djh9w14v
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    Dataset updated
    Nov 30, 2023
    Dataset provided by
    Dryad Digital Repository
    Authors
    Dewi Susanna
    Time period covered
    Jan 1, 2022
    Description

    In 2020 the world was presently burdened with the COVID-19 pandemic. World Health Organization confirms 34,874,744 cases with 1,097,497 deaths (case fatality rate (CFR) 3.1%) were reported in 216 countries. In Indonesia, the number of people who have been infected and the number who have died are approximately 287,008 and 10,740 (CFR 3.7%), respectively, with the most predominant regions being Jakarta (73,700), East Java (43,536) and Central Java (22,440). Many factors can increase the transmission of COVID-19. One of them is wind speed. This data set contains covid-19 data in DKI Jakarta from June 2020 until August 2022 and wind speed in daily power point form. This data can be analyzed to see the correlation between wind speed and the COVID-19 cases., The records of COVID-19 were obtained from the special website of coronavirus for the Daerah Khusus Ibukota (DKI) Jakarta at the Provincial Health Office (https://corona.jakarta.go.id/en/data-pemantauan). The COVID-19 data (n = 4,740) covered six administrative city areas and 261 sub-districts in DKI Jakarta as research locations, namely Kepulauan Seribu, West Jakarta, Central Jakarta, South Jakarta, East Jakarta, and Nort Jakarta. The wind speed data was taken from the Meteorology, Climatology and Geophysics Agency's data website. The wind speed data collected for the period June 2020 to August 2022 (n = 790) was obtained from the POWER LaRC Data Access Viewer, Jakarta. The wind speed data in .csv format is downloaded by specifying the type of daily data unit, data period (time extent), and parameter (in this case wind/pressure). The type of data extraction is POWER Single Point, where the location of the centroid or midpoint of DKI Jakarta Province is determined at latitude -6.1805 an...,

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National Aeronautics and Space Administration (2025). Prediction Of Worldwide Energy Resources (POWER) [Dataset]. https://catalog.data.gov/dataset/prediction-of-worldwide-energy-resources-power
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Prediction Of Worldwide Energy Resources (POWER)

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Apr 11, 2025
Dataset provided by
NASAhttp://nasa.gov/
Description

The POWER Project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly (by year 12 months + annual averages), and climatology. The POWER Data Archive provides data at the native resolution of the source data products. The data is updated nightly to maintain Near Real Time (NRT) availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER Project targets three specific user communities: Renewable Energy (RE), Sustainable Buildings (SB), and Agroclimatology (AG). The POWER Projects provides community specific parameters, output formats, naming conventions, and units that are commonly employed by each user community. The POWER Services Catalog consists of a series of RESTful Application Programming Interfaces (API), geospatial enabled image services, and a web mapping Data Access Viewer (DAV). These three different service offerings support data discovery, access, and distribution to our user base as ARD and as direct application inputs to decision support tools.

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