DOI: 10.5281/zenodo.2826939
title: A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods

description: <strong>Description</strong><br>
This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. <br>
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. <br>
The 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>
In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. <br>
We also include sample codes of baseline models for benchmarking of more elaborated models.

<strong>Data usage</strong><br>
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:<br>
<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>

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.

<strong>Sample code</strong><br>
As part of the data release, we are also including the sample code written in Python 3. <br>
The preprocessed data used in the scripts are also provided. <br>
The code can be used to reproduce the results presented in this work and as a starting point for future studies. <br>
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. <br>
All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. 

<strong>Units</strong><br>
All time stamps are in UTC (YYYY-MM-DD HH:MM:SS).<br>
All irradiance and weather data are in SI units.<br>
Sky image features are derived from 8-bit RGB (256 color levels) data.<br>
Satellite images are derived from 8-bit gray-scale (256 color levels) data.

<strong>Missing data</strong><br>
The string "NAN" indicates missing data

<strong>File formats</strong><br>
All time series data files as in CSV (comma separated values)<br>
Images are given in tar.bz2 files

<strong>Files </strong>


	<em>Folsom_irradiance.csv</em>                           Primary       One-minute GHI, DNI, and DHI data.
	<em>Folsom_weather.csv </em>                             Primary       One-minute weather data.
	<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.
	<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. 
	<em>Folsom_sky_image_features.csv </em>          Secondary    Features derived from the sky images.
	<em>Folsom_satellite.csv </em>                              Secondary   10 pixel by 10 pixel GOES-15 images centered in the target location. 
	<em>Irradiance_features_{horizon}.csv</em>          Secondary   Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). 
	<em>Sky_image_features_intra-hour.csv</em>       Secondary   Sky image features for the intra-hour forecasting issuing times. 
	<em>Sat_image_features_intra-day.csv</em>         Secondary   Satellite image features for the intra-day forecasting issuing times. 
	<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.
	<em>Target_{horizon}.csv</em>                              Secondary   Target data for the different forecasting horizons.
	F<em>orecast_{horizon}.py </em>                           Code            Python script used to create the forecasts for the different horizons. 
	<em>Postprocess.py</em>                                      Code             Python script used to compute the error metric for all the forecasts.
description: {"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"]}