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We have found 56 datasets for the keyword " agrivoltaics". You can continue exploring the search results in the list below.
Datasets: 106,579
Contributors: 42
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56 Datasets, Page 1 of 6
Solar Resource, NSRDB PSM Direct Normal Irradiance (DNI) - North American Cooperation on Energy Information
Average of the hourly Direct Normal Irradiance (DNI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE").The current version of the National Solar Radiation Database (NSRDB) (v2.0.1) was developed using the Physical Solar Model (PSM), and offers users the solar resource datasets from 1998 to 2014). The NSRDB comprises 30-minute solar and meteorological data for approximately 2 million 0.038-degree latitude by 0.038-degree longitude surface pixels (nominally 4 km2). The area covered is bordered by longitudes 25° W on the east and 175° W on the west, and by latitudes -20° S on the south and 60° N on the north. The solar radiation values represent the resource available to solar energy systems. The AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, a climatological albedo database and mixing ratio, temperature and pressure profiles from Modern Era-Retrospective Analysis (MERRA) to generate cloud masking and cloud properties. Cloud properties generated using PATMOS-x are used in fast radiative transfer models along with aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary sources to estimate Direct Normal Irradiance (DNI) and Global Horizontal Irradiance (GHI). A daily AOD is retrieved by combining information from the MODIS and MISR satellites and ground-based AERONET stations. Water vapor and other inputs are obtained from MERRA. For clear sky scenes the direct normal irradiance (DNI) and GHI are computed using the REST2 radiative transfer model. For cloud scenes identified by the cloud mask, Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute the GHI. The DNI for cloud scenes is then computed using the DISC model. The data in this layer is an average of the hourly GHI over 17 years (1998-2014). NOTE: The Geographical Information System (GIS) data and maps for solar resources for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) were developed by the U.S. National Renewable Energy Laboratory (NREL) and provided for Canada as an estimate. At present, neither the NREL data, nor the Physical Solar Model (PSM) on which the NREL data is based, have been either assessed or validated for the particular Canadian weather applications. A Canadian GHI map developed by the department of Natural Resources Canada (NRCan) is based on the State University of New York (SUNY) model and has been assessed and validated for the particular Canadian weather applications. The Canadian GHI map is available at http://atlas.gc.ca/cerp-rpep/en/.
Renewable Energy Power Plants, 1 MW or more - North American Cooperation on Energy Information
Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical into electric energy with an installed capacity of 1 Megawatt or more generated from renewable energy, including biomass, hydroelectric, pumped-storage hydroelectric, geothermal, solar, and wind.Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.Parent Collection:[North American Cooperation on Energy Information, Mapping Data](https://open.canada.ca/data/en/dataset/aae6619f-f9f3-435d-bc32-42decd58b674)
Power Plants, 100 MW or more - North American Cooperation on Energy Information
Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical, and/or fission energy into electric energy with an installed capacity of 100 megawatts or more.Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.Parent Collection:[North American Cooperation on Energy Information, Mapping Data](https://open.canada.ca/data/en/dataset/aae6619f-f9f3-435d-bc32-42decd58b674)
Annual Crop Inventory
Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based satellite images. Beginning with the 2011 growing season, this activity has been extended to other provinces in support of a national crop inventory. To date this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m (56m in 2009 and 2010).
Solar Resource, NSRDB PSM Global Horizontal Irradiance (GHI) - North American Cooperation on Energy Information
Average of the hourly Global Horizontal Irradiance (GHI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE").The current version of the National Solar Radiation Database (NSRDB) (v2.0.1) was developed using the Physical Solar Model (PSM), and offers users the solar resource datasets from 1998 to 2014). The NSRDB comprises 30-minute solar and meteorological data for approximately 2 million 0.038-degree latitude by 0.038-degree longitude surface pixels (nominally 4 km2). The area covered is bordered by longitudes 25° W on the east and 175° W on the west, and by latitudes -20° S on the south and 60° N on the north. The solar radiation values represent the resource available to solar energy systems. The AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, a climatological albedo database and mixing ratio, temperature and pressure profiles from Modern Era-Retrospective Analysis (MERRA) to generate cloud masking and cloud properties. Cloud properties generated using PATMOS-x are used in fast radiative transfer models along with aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary sources to estimate Direct Normal Irradiance (DNI) and Global Horizontal Irradiance (GHI). A daily AOD is retrieved by combining information from the MODIS and MISR satellites and ground-based AERONET stations. Water vapor and other inputs are obtained from MERRA. For clear sky scenes the direct normal irradiance (DNI) and GHI are computed using the REST2 radiative transfer model. For cloud scenes identified by the cloud mask, Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute the GHI. The DNI for cloud scenes is then computed using the DISC model. The data in this layer is an average of the hourly GHI over 17 years (1998-2014). NOTE: The Geographical Information System (GIS) data and maps for solar resources for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) were developed by the U.S. National Renewable Energy Laboratory (NREL) and provided for Canada as an estimate. At present, neither the NREL data, nor the Physical Solar Model (PSM) on which the NREL data is based, have been either assessed or validated for the particular Canadian weather applications. A Canadian GHI map developed by the department of Natural Resources Canada (NRCan) is based on the State University of New York (SUNY) model and has been assessed and validated for the particular Canadian weather applications. The Canadian GHI map is available at http://atlas.gc.ca/cerp-rpep/en/.
Natural Gas Processing Plants - North American Cooperation on Energy Information
Facilities designed to recover natural gas liquids from a stream of natural gas. These facilities control the quality of the natural gas to be marketed.Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.Parent Collection:[North American Cooperation on Energy Information, Mapping Data](https://open.canada.ca/data/en/dataset/aae6619f-f9f3-435d-bc32-42decd58b674)
Agriculture
A series of relevant spatial datasets were compiled to construct an Agricultural Potential Index model which combines multiple criteria that influence agricultural suitability. For more information on the Agriculutural Potential Index Model, please see the metadata link.
Clean power generating stations by type in megawatts (MW)
This Web Map Service depicts the location of clean electricity generating facilities by type of clean energy source and power generation capacity. Clean energy sources shown on the map include biomass, hydro, nuclear, solar, tidal and wind. The data comes from the provinces and territories, other federal departments and clean energy associations in Canada. The service is one of many themes mapped in the web mapping application Map of Clean Energy Resources and Projects (CERP) in Canada.
Submitted effluent quality results under the Wastewater Systems Effluent Regulations effluent quality standards
The Wastewater Systems Effluent Regulations (WSER), developed under the Fisheries Act, came into force in 2012 to manage wastewater releases by systems that collect an average daily influent volume of 100 cubic metres or more. The WSER also does not apply to any wastewater system located in the Northwest Territories, Nunavut and north of the 54th parallel in the provinces of Quebec and Newfoundland and Labrador. The WSER set national baseline effluent quality standards that are achievable through secondary wastewater treatment. The map below presents data regarding compliance with the WSER effluent quality standards according to their averaging period. Wastewater systems must report on either a monthly, quarterly or annual basis. The dataset also includes the carbonaceous biochemical oxygen demand (CBOD) and suspended solids (SS) results for each wastewater system. Annual reporting: This map layer shows whether or not the wastewater systems met the WSER effluent quality limit according to an annual averaging period for carbonaceous biochemical oxygen demand CBOD or suspended solids SS. Quarterly reporting: This map layer shows whether or not the wastewater systems met the WSER effluent quality standards according to a quarterly averaging period. Note that a system that meets its CBOD limit or its SS limit for more than 75% of the time is considered to be in compliance with the effluent quality standards of the WSER.Monthly reporting: This map layer shows whether or not the wastewater systems met the WSER effluent quality standards according to a monthly averaging period. Note that a system that meets its CBOD limit or its SS limit for more than 75% of the time is considered to be in compliance with the effluent quality standards of the WSER.The map is available in both ESRI REST (to use with ARC GIS) and WMS (open source) formats. For more information about the individual reporting wastewater systems, datasets are available in either CSV or XLS formats.Data from Quebec and YukonThe data for Quebec municipalities (after 2017) is provided to the department separately through the equivalency agreement that was finalized in October 2018. Beginning in 2015, the data reported for the Yukon is provided to the department separately through an equivalency agreement finalized in 2014.More information on the wastewater sector including the regulations, agreements, contacts and resource documents is available at: https://www.canada.ca/wastewater
Environment
ENV - Environment and conservation (environment)Environmental resources, protection, and conservation. For example, resources describing pollution, waste storage and treatment, environmental impact assessment, environmental risk, and nature reserves. )
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