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A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods

Carreira Pedro, Hugo; Larson, David; Coimbra, Carlos


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        <foaf:name>Coimbra, Carlos</foaf:name>
        <foaf:givenName>Carlos</foaf:givenName>
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    <dct:title>A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>solar irradiance forecasting</dcat:keyword>
    <dcat:keyword>sky images</dcat:keyword>
    <dcat:keyword>satellite images</dcat:keyword>
    <dcat:keyword>numerical weather prediction</dcat:keyword>
    <dcat:keyword>forecast benchmarking</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-06-24</dct:issued>
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    <dct:description>&lt;p&gt;&lt;strong&gt;Description&lt;/strong&gt;&lt;br&gt; This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset.&amp;nbsp;&lt;br&gt; The 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.&amp;nbsp;&lt;br&gt; The data consist of three years (2014&amp;ndash;2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California.&amp;nbsp;&lt;br&gt; In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data.&amp;nbsp;&lt;br&gt; We also include sample codes of baseline models for benchmarking of more elaborated models.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Data usage&lt;/strong&gt;&lt;br&gt; The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper:&lt;br&gt; &lt;em&gt;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.&amp;nbsp;Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494&lt;/em&gt;&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Sample code&lt;/strong&gt;&lt;br&gt; As part of the data release, we are also including the sample code written in Python 3.&amp;nbsp;&lt;br&gt; The preprocessed data used in the scripts are also provided.&amp;nbsp;&lt;br&gt; The code can be used to reproduce the results presented in this work and as a starting point for future studies.&amp;nbsp;&lt;br&gt; Besides 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.&amp;nbsp;&lt;br&gt; All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Units&lt;/strong&gt;&lt;br&gt; All time stamps are in UTC (YYYY-MM-DD HH:MM:SS).&lt;br&gt; All irradiance and weather data are in SI units.&lt;br&gt; Sky image features are derived from 8-bit RGB (256 color levels) data.&lt;br&gt; Satellite images are derived from 8-bit gray-scale (256 color levels) data.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Missing data&lt;/strong&gt;&lt;br&gt; The string &amp;quot;NAN&amp;quot; indicates missing data&lt;/p&gt; &lt;p&gt;&lt;strong&gt;File formats&lt;/strong&gt;&lt;br&gt; All time series data files as in CSV (comma separated values)&lt;br&gt; Images are given in tar.bz2 files&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Files&amp;nbsp;&lt;/strong&gt;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;&lt;em&gt;Folsom_irradiance.csv&lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;Primary&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;One-minute GHI, DNI, and DHI data.&lt;/li&gt; &lt;li&gt;&lt;em&gt;Folsom_weather.csv&amp;nbsp;&lt;/em&gt; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; Primary&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;One-minute weather data.&lt;/li&gt; &lt;li&gt;&lt;em&gt;Folsom_sky_images_{YEAR}.tar.bz2&lt;/em&gt; &amp;nbsp; &amp;nbsp;Primary&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.&lt;/li&gt; &lt;li&gt;&lt;em&gt;Folsom_NAM_lat{LAT}_lon{LON}.csv &lt;/em&gt;&amp;nbsp; &amp;nbsp;Primary&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node&amp;rsquo;s coordinates listed in Table I in the paper.&amp;nbsp;&lt;/li&gt; &lt;li&gt;&lt;em&gt;Folsom_sky_image_features.csv &lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; Secondary&amp;nbsp; &amp;nbsp; Features derived from the sky images.&lt;/li&gt; &lt;li&gt;&lt;em&gt;Folsom_satellite.csv &lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; Secondary &amp;nbsp; 10 pixel by 10 pixel GOES-15 images centered in the target location.&amp;nbsp;&lt;/li&gt; &lt;li&gt;&lt;em&gt;Irradiance_features_{horizon}.csv&lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; Secondary &amp;nbsp; Irradiance features for the different forecasting horizons ({horizon} 1&amp;frasl;4 {intra-hour, intra-day, day-ahead}).&amp;nbsp;&lt;/li&gt; &lt;li&gt;&lt;em&gt;Sky_image_features_intra-hour.csv&lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;Secondary &amp;nbsp; Sky image features for the intra-hour forecasting issuing times.&amp;nbsp;&lt;/li&gt; &lt;li&gt;&lt;em&gt;Sat_image_features_intra-day.csv&lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;Secondary &amp;nbsp; Satellite image features for the intra-day forecasting issuing times.&amp;nbsp;&lt;/li&gt; &lt;li&gt;&lt;em&gt;NAM_nearest_node_day-ahead.csv &lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp;Secondary &amp;nbsp; 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.&lt;/li&gt; &lt;li&gt;&lt;em&gt;Target_{horizon}.csv&lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; Secondary &amp;nbsp; Target data for the different forecasting horizons.&lt;/li&gt; &lt;li&gt;F&lt;em&gt;orecast_{horizon}.py &lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;Code&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; Python script used to create the forecasts for the different horizons.&amp;nbsp;&lt;/li&gt; &lt;li&gt;&lt;em&gt;Postprocess.py&lt;/em&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; Code&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;Python script used to compute the error metric for all the forecasts.&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;</dct:description>
    <dct:description xml:lang="">{"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"]}</dct:description>
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