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{{Sustainable energy}}
'''Solar power forecasting''' is the process of gathering and analyzing data in order to predict [[solar power]] generation on various time horizons with the goal to mitigate the impact of solar intermittency. Solar power forecasts are used for efficient management of the [[Electrical grid|electric grid]] and for power trading.<ref>{{Cite journal|date=2016-06-01|title=Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest|url=https://www.sciencedirect.com/science/article/abs/pii/S0960148116300398|journal=Renewable Energy|language=en|volume=91|pages=11–20|doi=10.1016/j.renene.2016.01.039|issn=0960-1481|last1=Larson |first1=David P. |last2=Nonnenmacher |first2=Lukas |last3=Coimbra |first3=Carlos F.M. |bibcode=2016REne...91...11L |url-access=subscription}}</ref>
As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore.<ref>{{Cite book|title=Solar Energy Forecasting and Resource Assessment - 1st Edition|url=https://www.elsevier.com/books/solar-energy-forecasting-and-resource-assessment/kleissl/978-0-12-397177-7|access-date=2021-06-29|website=www.elsevier.com|date=25 June 2013 |isbn=978-0-12-397177-7 }}</ref> The intermittency issue has been successfully addressed and mitigated by solar forecasting in many cases.<ref>{{Cite journal|date=2016-02-01|title=Benefits of solar forecasting for energy imbalance markets|url=https://www.sciencedirect.com/science/article/abs/pii/S0960148115302901|journal=Renewable Energy|language=en|volume=86|pages=819–830|doi=10.1016/j.renene.2015.09.011|issn=0960-1481|last1=Kaur |first1=Amanpreet |last2=Nonnenmacher |first2=Lukas |last3=Pedro |first3=Hugo T.C. |last4=Coimbra |first4=Carlos F.M. |bibcode=2016REne...86..819K |url-access=subscription}}</ref><ref>{{Cite journal|date=2019-10-01|title=Operational solar forecasting for the real-time market|url=https://www.sciencedirect.com/science/article/abs/pii/S0169207019300755|journal=International Journal of Forecasting|language=en|volume=35|issue=4|pages=1499–1519|doi=10.1016/j.ijforecast.2019.03.009|issn=0169-2070|last1=Yang |first1=Dazhi |last2=Wu |first2=Elynn |last3=Kleissl |first3=Jan |s2cid=195463551 }}</ref><ref>{{Cite journal|date=2018-01-15|title=Solar photovoltaic generation forecasting methods: A review|url=https://www.sciencedirect.com/science/article/abs/pii/S0196890417310622|journal=Energy Conversion and Management|language=en|volume=156|pages=459–497|doi=10.1016/j.enconman.2017.11.019|issn=0196-8904|last1=Sobri |first1=Sobrina |last2=Koohi-Kamali |first2=Sam |last3=Rahim |first3=Nasrudin Abd. |bibcode=2018ECM...156..459S |url-access=subscription}}</ref>
Information used for the solar power forecast usually includes the [[Sun]]´s path, the [[atmosphere|atmospheric]] conditions, the scattering of light and the characteristics of the [[solar energy]] plant.
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=== Satellite based methods ===
These methods leverage the several [[Geostationary orbit|geostationary]] Earth observing [[weather satellite]]s (such as [[Meteosat|Meteosat Second Generation (MSG) fleet]]'')'' to detect, characterise, track and predict the future locations of [[cloud cover]]. These satellites make it possible to generate solar power forecasts over broad regions through the application of [[image processing]] and forecasting [[algorithm]]s. Some satellite based forecasting algorithms include cloud motion vectors (CMVs)<ref>{{Cite web|title=Cloud motion vector - AMS Glossary|url=http://glossary.ametsoc.org/wiki/Cloud_motion_vector|access-date=2019-05-08|website=glossary.ametsoc.org}}</ref> or [[Streamlines, streaklines, and pathlines|streamline]] based approaches.<ref>{{Cite journal|date=2014-10-01|title=Streamline-based method for intra-day solar forecasting through remote sensing|url=https://www.sciencedirect.com/science/article/abs/pii/S0038092X14003752|journal=Solar Energy|language=en|volume=108|pages=447–459|doi=10.1016/j.solener.2014.07.026|issn=0038-092X|last1=Nonnenmacher |first1=Lukas |last2=Coimbra |first2=Carlos F.M. |bibcode=2014SoEn..108..447N |url-access=subscription}}</ref>
=== Numerical weather prediction ===
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