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Title: Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint

Abstract

A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmospheric measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.

Authors:
ORCiD logo [1];  [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1406990
Report Number(s):
NREL/CP-5D00-70030
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the International Workshop on the Integration of Solar Power into Power Systems (Solar Integration Workshop), 24-26 October 2017, Berlin, Germany
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; solar forecasting; solar power forecasting; machine learning; SURFRAD

Citation Formats

Florita, Anthony R, Elgindy, Tarek, Hodge, Brian S, and Dobbs, Alex. Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint. United States: N. p., 2017. Web.
Florita, Anthony R, Elgindy, Tarek, Hodge, Brian S, & Dobbs, Alex. Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint. United States.
Florita, Anthony R, Elgindy, Tarek, Hodge, Brian S, and Dobbs, Alex. Tue . "Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1406990.
@article{osti_1406990,
title = {Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint},
author = {Florita, Anthony R and Elgindy, Tarek and Hodge, Brian S and Dobbs, Alex},
abstractNote = {A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmospheric measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Oct 03 00:00:00 EDT 2017},
month = {Tue Oct 03 00:00:00 EDT 2017}
}

Conference:
Other availability
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