Solar power forecasting: Difference between revisions

Revision as of 13:33, 23 October 2022

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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. }}</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 web|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}}</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. }}</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. }}</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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The high resolution required for accurate nowcast techniques require high resolution data input including ground imagery, as well as fast data acquisition form irradiance sensors and fast processing speeds.

The actual nowcast is than frequently enhanced by e.g. [[Statistical technique|Statistical techniques]]. In the case of nowcasting, these techniques are usually based on [[time series]] processing of measurement data, including [[Surface weather observation|meteorological observations]] and power output measurements from a solar power facility. What then follows is the creation of a [[Training, validation, and test sets|training dataset]] to tune the parameters of a model, before evaluation of model performance against a separate testing dataset. This class of techniques includes the use of any kind of statistical approach, such as [[Autoregressive–moving-average model|autoregressive moving averages]] (ARMA, ARIMA, etc.), as well as machine learning techniques such as [[Artificial neural network|neural networks]], [[support vector machine]]s (etc.)<ref>{{cite journal|last1=Sanjari|first1=M.J.|last2=Gooi|first2=H.B.|date=2016|title=Probabilistic Forecast of PV Power Generation based on Higher-order Markov Chain|journal=IEEE Transactions on Power Systems|volume=32|issue=4|pages=2942–2952|doi=10.1109/TPWRS.2016.2616902|s2cid=43911568 }}</ref>.

An important element of nowcasting solar power are ground based sky observations and basically all intra-day forecasts.<ref>{{Cite journal|date=2011-11-01|title=Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed|url=https://www.sciencedirect.com/science/article/abs/pii/S0038092X11002982|journal=Solar Energy|language=en|volume=85|issue=11|pages=2881–2893|doi=10.1016/j.solener.2011.08.025|issn=0038-092X|last1=Chow |first1=Chi Wai |last2=Urquhart |first2=Bryan |last3=Lave |first3=Matthew |last4=Dominguez |first4=Anthony |last5=Kleissl |first5=Jan |last6=Shields |first6=Janet |last7=Washom |first7=Byron }}</ref>

==Short-term solar power forecasting==

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=== Ground based sky observations ===

For intra-day forecasts, local cloud information is acquired by one or several ground-based sky imagers at high frequency (1 minute or less). The combination of these images and local weather measurement information are processed to simulate cloud motion vectors and [[optical depth]] to obtain forecasts up to 30 minutes ahead.<ref>{{Cite journal|date=2011-11-01|title=Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed|url=https://www.sciencedirect.com/science/article/abs/pii/S0038092X11002982|journal=Solar Energy|language=en|volume=85|issue=11|pages=2881–2893|doi=10.1016/j.solener.2011.08.025|issn=0038-092X|last1=Chow |first1=Chi Wai |last2=Urquhart |first2=Bryan |last3=Lave |first3=Matthew |last4=Dominguez |first4=Anthony |last5=Kleissl |first5=Jan |last6=Shields |first6=Janet |last7=Washom |first7=Byron }}</ref>

=== 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|algorithms]]. 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. }}</ref>

=== Numerical weather prediction ===

Most of the short term forecast approaches use [[numerical weather prediction]] models (NWP) that provide an important estimation of the development of weather variables. The models used included the [[Global Forecast System]] (GFS) or data provided by the European Center for Medium Range Weather Forecasting ([[ECMWF]]). These two models are considered the state of the art of global forecast models, which provide meteorological forecasts all over the world.