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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
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.
Generally, the solar forecasting techniques depend on the forecasting horizon
* ''Nowcasting'' (forecasting 3–4 hours ahead),
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==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> Solar power nowcasting services are usually related to temporal resolutions of 5 to 15 minutes, with updates as frequent as every minute.
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 then frequently enhanced by e.g. [[
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 |bibcode=2011SoEn...85.2881C }}</ref>
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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 [[
=== 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.
In order to increase spatial and temporal resolution of these models, other models have been developed which are generally called mesoscale models. Among others, [[HIRLAM]], [[Weather Research and Forecasting Model|WRF]] or [[MM5 (weather model)|MM5]]. Since these NWP models are highly complex and difficult to run on local computers, these variables are usually considered as exogeneous inputs to [[solar irradiance]] models and ingested form the respective data provider. Best forecasting results are achieved with [[data assimilation]].
Some researchers argue for the use of post-processing techniques, once the models’ output is obtained, in order to obtain a [[Probability|probabilistic]] point of view of the accuracy of the output. This is usually done with ensemble techniques that mix different outputs of different models perturbed in strategic meteorological values and finally provide a better estimate of those variables and a degree of uncertainty, like in the model proposed by Bacher et al. (2009).
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==Long-term solar power forecasting==
''Long-term'' forecasting usually refers to forecasting techniques applied to time horizons on the order of weeks to years. These time horizons can be relevant for energy producers to negotiate contracts with financial entities or [[Public utility|utilities]] that distribute the generated energy.
In general, these long-term forecasting horizons usually rely on [[Numerical weather prediction|NWP]] and [[Climatology|climatological]] models. Additionally, most of the forecasting methods are based on [[Mesoscale meteorology|mesoscale]] models fed with reanalysis data as input. Output can also be postprocessed with [[Statistics|statistical]] approaches based on measured data. Due to the fact that this time horizon is less relevant from an operational perspective and much harder to model and validate, only about 5% of solar forecasting publications consider this horizon.
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==Energetic models==
Any output from a model must then be converted to the electric energy that a particular solar PV plant will produce. This step is usually done with statistical approaches that try to correlate the amount of available resource with the metered power output. The main advantage of these methods is that the meteorological prediction error, which is the main component of the global error, might be reduced taking into account the uncertainty of the prediction.
As it was mentioned before and detailed in ''Heinemann et al.'', these statistical approaches comprises from ARMA models, neural networks, support vector machines, etc.
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{{reflist}}
==External links==▼
* [https://www.nrel.gov/grid/solar-wind-forecasting.html Solar and Wind Forecasting projects], by National Renewable Energy Laboratory (NREL).▼
[[Category:Photovoltaics]]
[[Category:Solar power]]
[[Category:Weather prediction]]
▲==External links==
▲* [https://www.nrel.gov/grid/solar-wind-forecasting.html Solar and Wind Forecasting projects], by National Renewable Energy Laboratory (NREL).
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