Dataset Open Access
Carreira Pedro, Hugo; Larson, David; Coimbra, Carlos
{ "files": [ { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_irradiance.csv" }, "checksum": "md5:f7deba7ccd089dbd3f52a46405a7dfc2", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_irradiance.csv", "type": "csv", "size": 76536976 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_NAM_lat38.579454_lon-121.260320.csv" }, "checksum": "md5:3d917eeecdf967d1f90f803fad5e5467", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_NAM_lat38.579454_lon-121.260320.csv", "type": "csv", "size": 1599165 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_NAM_lat38.599891_lon-121.126680.csv" }, "checksum": "md5:30024faae0123990cf29c81c281eaccc", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_NAM_lat38.599891_lon-121.126680.csv", "type": "csv", "size": 1593101 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_NAM_lat38.683880_lon-121.286556.csv" }, "checksum": "md5:c0d6db7093b957603cb05c90fff23167", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_NAM_lat38.683880_lon-121.286556.csv", "type": "csv", "size": 1599189 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_NAM_lat38.704328_lon-121.152788.csv" }, "checksum": "md5:792f830c261e2c041d35ebeb6eadbeac", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_NAM_lat38.704328_lon-121.152788.csv", "type": "csv", "size": 1590411 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_satellite.csv" }, "checksum": "md5:f68086048ee5d764d1d992404147c421", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_satellite.csv", "type": "csv", "size": 15711562 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_sky_image_features.csv" }, "checksum": "md5:86d58b6b84393399735a93ce1657cfab", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_sky_image_features.csv", "type": "csv", "size": 104681298 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_sky_images_2014.tar.bz2" }, "checksum": "md5:fb2dee79429725ac91df539b310a9f98", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_sky_images_2014.tar.bz2", "type": "bz2", "size": 13759682249 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_sky_images_2015.tar.bz2" }, "checksum": "md5:bce043f846a4dd01668a32943578b652", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_sky_images_2015.tar.bz2", "type": "bz2", "size": 16945355105 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_sky_images_2016.tar.bz2" }, "checksum": "md5:af72cd28b398fb531ae1ab877c19eba0", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_sky_images_2016.tar.bz2", "type": "bz2", "size": 18616207524 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Folsom_weather.csv" }, "checksum": "md5:b04e0dc7edf3513a769ea2c8c59beb27", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Folsom_weather.csv", "type": "csv", "size": 138793384 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Forecast_day-ahead.py" }, "checksum": "md5:763f1666ff1485d631b7417cc8c4a5e8", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Forecast_day-ahead.py", "type": "py", "size": 5071 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Forecast_intra-day.py" }, "checksum": "md5:6030752b33ce675859d131833a5e127d", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Forecast_intra-day.py", "type": "py", "size": 5112 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Forecast_intra-hour.py" }, "checksum": "md5:7dd387b298e4c75f84a5fe7093bde2dd", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Forecast_intra-hour.py", "type": "py", "size": 5134 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Irradiance_features_day-ahead.csv" }, "checksum": "md5:889efab48e0c0c690c45b11e641ba388", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Irradiance_features_day-ahead.csv", "type": "csv", "size": 725564 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Irradiance_features_intra-day.csv" }, "checksum": "md5:971eee5f86677536b6238e73d923cedc", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Irradiance_features_intra-day.csv", "type": "csv", "size": 8266520 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Irradiance_features_intra-hour.csv" }, "checksum": "md5:9e25e78b816e51b95d4349f304155f56", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Irradiance_features_intra-hour.csv", "type": "csv", "size": 49579520 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/NAM_nearest_node_day-ahead.csv" }, "checksum": "md5:978905d0c0d1b1488325b33456446d23", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "NAM_nearest_node_day-ahead.csv", "type": "csv", "size": 519327 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Postprocess.py" }, "checksum": "md5:73601ae78e2e49942673688650abfa3d", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Postprocess.py", "type": "py", "size": 4801 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Sat_image_features_intra-day.csv" }, "checksum": "md5:8af401d02a090108b1863cb953ef64cf", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Sat_image_features_intra-day.csv", "type": "csv", "size": 20753628 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Sky_image_features_intra-hour.csv" }, "checksum": "md5:a81c753c308213e2b506b94e0412403a", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Sky_image_features_intra-hour.csv", "type": "csv", "size": 23572470 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Target_day-ahead.csv" }, "checksum": "md5:ed4959b21d282177cedcefe2e8e27f83", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Target_day-ahead.csv", "type": "csv", "size": 1158472 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Target_intra-day.csv" }, "checksum": "md5:9d530ea7cbe0f122bc26041e9da74afd", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Target_intra-day.csv", "type": "csv", "size": 10663334 }, { "links": { "self": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed/Target_intra-hour.csv" }, "checksum": "md5:ac6ebc385b6f6112c68ea967fc437c69", "bucket": "68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "key": "Target_intra-hour.csv", "type": "csv", "size": 64523351 } ], "owners": [ 60965 ], "doi": "10.5281/zenodo.2826939", "stats": { "version_unique_downloads": 3126.0, "unique_views": 987.0, "views": 1103.0, "version_views": 1103.0, "unique_downloads": 3126.0, "version_unique_views": 987.0, "volume": 277085885021559.0, "version_downloads": 18937.0, "downloads": 18937.0, "version_volume": 277085885021559.0 }, "links": { "doi": "https://doi.org/10.5281/zenodo.2826939", "conceptdoi": "https://doi.org/10.5281/zenodo.2826938", "bucket": "https://zenodo.org/api/files/68c7feea-d2e8-4e9f-a55d-b50df76f91ed", "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.2826938.svg", "html": "https://zenodo.org/record/2826939", "latest_html": "https://zenodo.org/record/2826939", "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.2826939.svg", "latest": "https://zenodo.org/api/records/2826939" }, "conceptdoi": "10.5281/zenodo.2826938", "created": "2019-06-25T19:47:27.652664+00:00", "updated": "2020-01-24T19:25:10.862705+00:00", "conceptrecid": "2826938", "revision": 5, "id": 2826939, "metadata": { "access_right_category": "success", "doi": "10.5281/zenodo.2826939", "description": "<p><strong>Description</strong><br>\nThis repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. <br>\nThe goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. <br>\nThe data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. <br>\nIn addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. <br>\nWe also include sample codes of baseline models for benchmarking of more elaborated models.</p>\n\n<p><strong>Data usage</strong><br>\nThe usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper:<br>\n<em>Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494</em></p>\n\n<p>Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data.</p>\n\n<p><strong>Sample code</strong><br>\nAs part of the data release, we are also including the sample code written in Python 3. <br>\nThe preprocessed data used in the scripts are also provided. <br>\nThe code can be used to reproduce the results presented in this work and as a starting point for future studies. <br>\nBesides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. <br>\nAll required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. </p>\n\n<p><strong>Units</strong><br>\nAll time stamps are in UTC (YYYY-MM-DD HH:MM:SS).<br>\nAll irradiance and weather data are in SI units.<br>\nSky image features are derived from 8-bit RGB (256 color levels) data.<br>\nSatellite images are derived from 8-bit gray-scale (256 color levels) data.</p>\n\n<p><strong>Missing data</strong><br>\nThe string "NAN" indicates missing data</p>\n\n<p><strong>File formats</strong><br>\nAll time series data files as in CSV (comma separated values)<br>\nImages are given in tar.bz2 files</p>\n\n<p><strong>Files </strong></p>\n\n<ul>\n\t<li><em>Folsom_irradiance.csv</em> Primary One-minute GHI, DNI, and DHI data.</li>\n\t<li><em>Folsom_weather.csv </em> Primary One-minute weather data.</li>\n\t<li><em>Folsom_sky_images_{YEAR}.tar.bz2</em> Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.</li>\n\t<li><em>Folsom_NAM_lat{LAT}_lon{LON}.csv </em> Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. </li>\n\t<li><em>Folsom_sky_image_features.csv </em> Secondary Features derived from the sky images.</li>\n\t<li><em>Folsom_satellite.csv </em> Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. </li>\n\t<li><em>Irradiance_features_{horizon}.csv</em> Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). </li>\n\t<li><em>Sky_image_features_intra-hour.csv</em> Secondary Sky image features for the intra-hour forecasting issuing times. </li>\n\t<li><em>Sat_image_features_intra-day.csv</em> Secondary Satellite image features for the intra-day forecasting issuing times. </li>\n\t<li><em>NAM_nearest_node_day-ahead.csv </em> Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.</li>\n\t<li><em>Target_{horizon}.csv</em> Secondary Target data for the different forecasting horizons.</li>\n\t<li>F<em>orecast_{horizon}.py </em> Code Python script used to create the forecasts for the different horizons. </li>\n\t<li><em>Postprocess.py</em> Code Python script used to compute the error metric for all the forecasts.</li>\n</ul>\n\n<p> </p>", "license": { "id": "CC-BY-4.0" }, "title": "A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods", "relations": { "version": [ { "count": 1, "index": 0, "parent": { "pid_type": "recid", "pid_value": "2826938" }, "is_last": true, "last_child": { "pid_type": "recid", "pid_value": "2826939" } } ] }, "version": "V1", "references": [ "Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494" ], "keywords": [ "solar irradiance forecasting", "sky images", "satellite images", "numerical weather prediction", "forecast benchmarking" ], "publication_date": "2019-06-24", "creators": [ { "affiliation": "University of California San Diego", "name": "Carreira Pedro, Hugo" }, { "affiliation": "University of California San Diego", "name": "Larson, David" }, { "affiliation": "University of California San Diego", "name": "Coimbra, Carlos" } ], "access_right": "open", "resource_type": { "type": "dataset", "title": "Dataset" }, "related_identifiers": [ { "scheme": "doi", "identifier": "10.1063/1.5094494", "relation": "isCompiledBy" }, { "scheme": "doi", "identifier": "10.5281/zenodo.2826938", "relation": "isVersionOf" } ] } }
All versions | This version | |
---|---|---|
Views | 1,103 | 1,103 |
Downloads | 18,937 | 18,937 |
Data volume | 277.1 TB | 277.1 TB |
Unique views | 987 | 987 |
Unique downloads | 3,126 | 3,126 |