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{{Sustainable energy}}
'''Solar power forecasting''' is the process of gathering and analyzing data in order to predict [[Solar energy|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}}</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>
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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* ''Short-term forecasting'' (up to seven days ahead) and
* ''Long-term forecasting'' (weeks, months, years)
Many solar resource forecasting methodologies were proposed since the 1970 and most authors agree that different forecast horizons require different methodologies. Forecast horizons below 1 hour typically require ground based sky imagery and sophisticated time series and machine learning models. Intra-day horizons, normally forecasting irradiance values up to 4 or 6 hours ahead, require satellite images and irradiance models. Forecast horizons exceeding 6 hours usually rely on outputs from numerical weather prediction (NWP) models.<ref>{{Cite thesis|title=Solar Energy Resourcing and Forecasting for Optimized Grid Integration|url=https://escholarship.org/uc/item/8kd3b038|publisher=UC San Diego|date=2015|language=en|first=Lukas|last=Nonnenmacher}}</ref>
==Nowcasting==
Solar power nowcasting refers to the prediction of solar power output over time horizons of tens to hundreds of minutes ahead of time with up to 90% predictability.<ref>{{cite web |last1=Vorrath |first1=Sophie |title=New APVI solar tool shows daily, time-based forecast for each state |url=https://reneweconomy.com.au/new-apvi-solar-tool-shows-daily-time-based-forecast-for-each-state-52445/ |website=RenewEconomy |language=en-AU |date=31 May 2019}}</ref>
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.
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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}}</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}}</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}}</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}}</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.
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{{reflist}}
[[Category:Photovoltaics]]
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