{"url":"/dataset/solar-power","name":"Solar-Power","full_name":"Solar Power Data for Integration Studies (Alabama)","description_markdown":"Solar Power Data for Integration Studies\r\nNREL's Solar Power Data for Integration Studies are synthetic solar photovoltaic (PV) power plant data points for the United States representing the year 2006.\r\n\r\nThe data are intended for use by energy professionals—such as transmission planners, utility planners, project developers, and university researchers—who perform solar integration studies and need to estimate power production from hypothetical solar plants.\r\n\r\nData Methodologies\r\nThe Solar Power Data for Integration Studies consist of 1 year (2006) of 5-minute solar power and hourly day-ahead forecasts for approximately 6,000 simulated PV plants. Solar power plant locations were determined based on the capacity expansion plan for high-penetration renewables in Phase 2 of the Western Wind and Solar Integration Study and the Eastern Renewable Generation Integration Study.\r\n\r\nNREL generated the 5-minute data set using the Sub-Hour Irradiance Algorithm. The day-ahead solar forecast data for locations in the western United States were generated by 3TIER based on numerical weather predication simulations for Phase 1 of the Western Wind and Solar Integration Study. NREL generated the day-ahead solar forecast data in eastern U.S. locations using the Weather Research and Forecasting model.\r\n\r\nThe data are for specific years and should not be assumed to be representative of typical radiation levels for a site. These data should not generally be used for site-specific project development work.\r\n\r\nNaming Convention\r\nThe naming convention of the state-wise solar power data (.csv files) from the Solar Integration Studies is as follows.\r\n\r\nData Type_Latitude_Longitude_Weather Year_PV Type_CapacityMW_Time Interval _Min.csv\r\n\r\nData Type\r\nActual: Real power output\r\nDA: Day ahead forecast\r\nHA4: 4 hour ahead forecast\r\nWeather Year: The PV data is based on the particular year's known weather condition.\r\nPV Type\r\nUPV: Utility scale PV\r\nDPV: Distributed PV\r\n\r\nNote: The practical difference between UPV and DPV is in the configurations (UPV has single axis tracking while DPV is fixed tilt equaling to latitude) and the smoothing (both are run through a low-pass filter, the DPV will have more of the high frequency variability smoothed out).\r\n\r\nCapacity: Installed capacity in MW\r\nTime Interval: PV generation data reading interval in minutes.\r\nContact\r\nYingchen Zhang\r\nManager, Sensing, Measurement, and Forecasting Group\r\nYingchen.Zhang@nrel.gov\r\n303-384-7090","description_withheld":null,"homepage":"https://www.nrel.gov/grid/solar-power-data.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series Analysis","url":"/task/time-series","datasets_with_task":"/datasets/task/time-series"},{"name":"Correlated Time Series Forecasting","url":"/task/correlated-time-series-forecasting","datasets_with_task":"/datasets/task/correlated-time-series-forecasting"},{"name":"Univariate Time Series Forecasting","url":"/task/univariate-time-series-forecasting","datasets_with_task":"/datasets/task/univariate-time-series-forecasting"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Solar-Power"],"data_loaders":[{"repo":"https://github.com/cure-lab/SCINet","url":"https://github.com/cure-lab/SCINet","frameworks":["pytorch"]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/correlated-time-series-forecasting-on-solar","task":"Correlated Time Series Forecasting","dataset_variant":"Solar-Power","rows":1,"metrics":["FLOPs(M)","Parameters(K)"],"first_row_in_archive_order":{"model":"LightCTS","paper":"/paper/lightcts-a-lightweight-framework-for","metrics":{"FLOPs(M)":"169","Parameters(K)":"38"},"code_links":[{"title":"ai4cts/lightcts","url":"https://github.com/ai4cts/lightcts"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/univariate-time-series-forecasting-on-solar","task":"Univariate Time Series Forecasting","dataset_variant":"Solar-Power","rows":1,"metrics":["RRSE"],"first_row_in_archive_order":{"model":"LST-Skip (24 step)","paper":"/paper/modeling-long-and-short-term-temporal","metrics":{"RRSE":"0.4643"},"code_links":[{"title":"laiguokun/multivariate-time-series-data","url":"https://github.com/laiguokun/multivariate-time-series-data"},{"title":"laiguokun/LSTNet","url":"https://github.com/laiguokun/LSTNet"},{"title":"fbadine/LSTNet","url":"https://github.com/fbadine/LSTNet"},{"title":"Vsooong/pattern_recognize","url":"https://github.com/Vsooong/pattern_recognize"},{"title":"opringle/multivariate_time_series_forecasting","url":"https://github.com/opringle/multivariate_time_series_forecasting"},{"title":"GokulKarthik/LSTNet.pytorch","url":"https://github.com/GokulKarthik/LSTNet.pytorch"},{"title":"aaqib-ali/LSTNet_Pytorch","url":"https://github.com/aaqib-ali/LSTNet_Pytorch"},{"title":"Liut2016/AwesomeDeepLearningGuidance-TimeSeriesData","url":"https://github.com/Liut2016/AwesomeDeepLearningGuidance-TimeSeriesData"},{"title":"hubtru/LTBoost","url":"https://github.com/hubtru/LTBoost"},{"title":"flaviagiammarino/lstnet-tensorflow","url":"https://github.com/flaviagiammarino/lstnet-tensorflow"},{"title":"Goochaozheng/LSTNet","url":"https://github.com/Goochaozheng/LSTNet"},{"title":"Zhao-zi-jun/multivariate_prediction_master","url":"https://github.com/Zhao-zi-jun/multivariate_prediction_master"},{"title":"zhaozijun102548/multivariate_prediction_master","url":"https://github.com/zhaozijun102548/multivariate_prediction_master"},{"title":"sw6-aau/LSTnet-demo","url":"https://github.com/sw6-aau/LSTnet-demo"},{"title":"Enforcer03/LSTNet","url":"https://github.com/Enforcer03/LSTNet"},{"title":"appleparan/mise.py","url":"https://github.com/appleparan/mise.py"},{"title":"sw6-aau/lastnet-backup","url":"https://github.com/sw6-aau/lastnet-backup"},{"title":"sw6-aau/gcp-fixed","url":"https://github.com/sw6-aau/gcp-fixed"},{"title":"quocanuit/lstnet-solar-gen","url":"https://github.com/quocanuit/lstnet-solar-gen"},{"title":"zyf0220/pattern_recognize","url":"https://github.com/zyf0220/pattern_recognize"},{"title":"sw6-aau/production-gcp","url":"https://github.com/sw6-aau/production-gcp"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/lightcts-a-lightweight-framework-for","title":"LightCTS: A Lightweight Framework for Correlated Time Series Forecasting","date":"2023-02-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/modeling-long-and-short-term-temporal","title":"Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks","date":"2017-03-21","rows_on_this_dataset":1,"code_links":21,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}