Solar power forecasting: Difference between revisions

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{{short description|Power forecasting}}

{{Multiple issues|{{more footnotes|date=July 2012}}{{Expert needed|Energy|date=May 2021}}

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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>

'''Solar power forecasting''' is the gathering and analysis of data in order to predict the optimal conditions for [[Solar energy|solar power generation.]]

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 |doi-access=free}}</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>

This information includes the [[Sun]]´s path, the [[atmosphere]]'s condition, the scattering processes and the characteristics of the [[solar energy]] plant. The power output depends on the incoming radiation and on the solar panel characteristics. Solar power forecast information is used for efficient management of the [[Electrical grid|electric grid]] and for solar energy trading.

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.

Common solar forecasting method include [[stochastic]] learning methods, local and remote sensing methods, and hybrid methods (Chu et al. 2016).

ItGenerally, isthe usefulsolar to classify theseforecasting techniques dependingdepend on the forecasting horizon

* ''now-castingNowcasting'' (forecasting 3–4 hours ahead),

* ''shortShort-term forecasting'' (up to seven days ahead) and

* ''longLong-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 then refers to the prediction of solar power output (or energy generation) 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> It has historically been important for [[Electrical grid|electrical grid operators]] in order to guarantee the matching of supply and demand on [[Energy market|energy markets]]. Solar power nowcasting services are usually related to temporal resolutions of 5 to 15 minutes, with updates as frequent as every 5 minutesminute.

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 regular updates and relatively high resolutions required for nowcast require automatic weather data acquisition and processing techniques. These are accomplished by three primary means:<ref>{{Cite web|url= https://www.elsevier.com/books/solar-energy-forecasting-and-resource-assessment/kleissl/978-0-12-397177-7 |title=Solar Energy Forecasting and Resource Assessment - 1st Edition|website=www.elsevier.com |access-date=2019-05-08}}</ref>

TheseThe actual nowcast is then frequently enhanced by e.g. [[statistical technique]]s. 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 (I. Espino eta al, 2011), 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 Thesejournal|last1=Sanjari|first1=M.J.|last2=Gooi|first2=H.B.|date=2016|title=Probabilistic approachesForecast areof usuallyPV benchmarkedPower toGeneration abased persistence approach inon Higher-order toMarkov evaluateChain|journal=IEEE theirTransactions improvements.on ThisPower persistenceSystems|volume=32|issue=4|pages=2942–2952|doi=10.1109/TPWRS.2016.2616902|s2cid=43911568 approach just assumes that any variable at time step t is the value it took in a previous time.|hdl=10072/409742|hdl-access=free}}</ref>

=== Statistical techniques ===

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 (I. Espino eta al, 2011), 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.). These approaches are usually benchmarked to a persistence approach in order to evaluate their improvements. This persistence approach just assumes that any variable at time step t is the value it took in a previous time.

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>

=== 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]]. Key forecasting algorithms include cloud motion vectors (CMVs).<ref>{{Cite web|url= http://glossary.ametsoc.org/wiki/Cloud_motion_vector |title=Cloud motion vector - AMS Glossary|website=glossary.ametsoc.org |access-date=2019-05-08}}</ref> [[File:Diagram-of-the-UCSD-sky-imager-USI-and-related-solar-and-sky-geometries-th-0-is-the.jpg|alt=|thumb|An example of sky-imager used for detecting, tracking and predicting cloud cover conditions in the vicinity of a solar energy facility of interest. Most often, these devices are used to make estimates of solar irradiance from the images using local calibration by a pyranometer. The solar irradiance short-term forecasts are then fed into PV power modelling routines to generate a solar power forecast. <br />Credit: [https://topex.ucsd.edu/rs/presentations/cortes.pdf UC San Diego]]]

=== Ground based techniques. ===

These techniques are used to derive [[irradiance]] forecasts with much higher spatial and temporal resolution compared with the satellite-based forecasts. Local cloud information is acquired by one or several ground-based sky imagers at a 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.

==Short-term solar power forecasting==

[[File:Diagram-of-the-UCSD-sky-imager-USI-and-related-solar-and-sky-geometries-th-0-is-the.jpg|alt=|thumb|An example of sky-imager used for detecting, tracking and predicting cloud cover conditions in the vicinity of a solar energy facility of interest. Most often, these devices are used to make estimates of solar irradiance from the images using local calibration by a pyranometer. The solar irradiance short-term forecasts are then fed into PV power modelling routines to generate a solar power forecast. <br />Credit: [https://topex.ucsd.edu/rs/presentations/cortes.pdf UC San Diego]]]''Short-term'' forecasting provides predictions up to seven days ahead. Due to the power market regulation in many jurisdictions, intra-day forecasts and day-ahead solar power forecasts are the most important time horizons in this category. Basically all highly accurate short term forecasting methods leverage several data input streams such as meteorological variables, local weather phenomena and ground observations along with complex mathematical models.

=== Ground based techniques.sky observations ===

''Short-term'' forecasting provides predictions up to seven days ahead. This kind of forecast is also valuable for grid operators in order to make decisions of grid operation, as well as, for electric market operators.<ref>{{cite journal|last1=Sanjari|first1=M.J.|last2=Gooi|first2=H.B.|title=Probabilistic Forecast of PV Power Generation based on Higher-order Markov Chain|journal=IEEE Transactions on Power Systems|date=2016|volume=32|issue=4|pages=2942–2952|doi=10.1109/TPWRS.2016.2616902}}</ref>

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 |bibcode=2011SoEn...85.2881C }}</ref>

Meteorological variables and phenomena are looked from a more general perspective, not as local as nowcasting services do.

=== 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 ===

Most of the short term forecast approaches use [[numerical weather prediction]] models (NWP) that provide an initialimportant estimation of the development of weather variables. TheseThe 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 thehighly mostcomplex representativeand ofdifficult to run on local computers, these variables are usually considered as exogeneous inputs to [[solar irradiance]] models sinceand theyingested areform widelythe respective data provider. Best forecasting results are usedachieved bywith different[[data communitiesassimilation]].

To run these models a wide expertise is needed in order to obtain accurate results, due to the wide variety of parameters that can be configured in the models. In addition, sophisticated techniques such as [[data assimilation]] might be used in order to produce more realistic simulations.

Some communitiesresearchers 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).

==Long-term solar power forecasting==

''Long-term'' forecasting usually refers to forecasting oftechniques applied to time horizons on the annualorder orof monthlyweeks availableto resourceyears. ThisThese istime usefulhorizons can be relevant for energy producers and to negotiate contracts with financial entities or [[Public utility|utilities]] that distribute the generated energy.

In general, these long-term forecasting ishorizons usually donerely aton a[[Numerical lowerweather scaleprediction|NWP]] thanand any[[Climatology|climatological]] of the two previous approachesmodels. HenceAdditionally, most of thesethe modelsforecasting methods are runbased withon [[Mesoscale meteorology|mesoscale]] models fed with reanalysis data as input. andOutput whosecan outputalso isbe 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.

==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}}

* Y. Chu, M. Li and C.F.M. Coimbra (2016) “Sun-Tracking Imaging System for Intra-Hour DNI Forecasts” Renewable Energy (96), Part A, pp.&nbsp;792–799.

==External links==

* Luis Martín, Luis F. Zarzalejo, Jesús Polo, Ana Navarro, Ruth Marchante, Marco Cony, Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning, Solar Energy, Volume 84, Issue 10, October 2010, Pages 1772-1781, {{ISSN|0038-092X}}, {{DOI|10.1016/j.solener.2010.07.002}}.

* [https://www.nrel.gov/grid/solar-wind-forecasting.html Solar and Wind Forecasting projects], by National Renewable Energy Laboratory (NREL).

* Heinemann, D., Lorenz E., Girodo M. Forecasting of solar radiation. Oldenburg University, Institute of Physics, Energy Meteorology Group.

* Alonso, M, Chenlo F. Estimación de la energía generada por un sistema fotovoltaico conectado a red. CIEMAT. Laboratorio de sistemas fotovoltaicos.

* Alvarez, L., Castaño, C.A., Martín, J. A computer vision approach for solar radiation nowcasting using MSG images. EMS Annual Meeting Abstracts. Vol. 7, EMS2010-495, 2010. 10th EMS/8th ECAC.

* Espino, I., Hernández, M.. Nowcasting of wind speed using support vector regression. Experiments with Time Series from Gran Canaria. Renewable Energy and Power Quality Journal, {{ISSN|2172-038X}}, N9, 12 May 2011.

* Bacher, P., Madsen, H., Nielsen H.A. Online short-term solar power forecasting. Solar Energy. Vol 83, Issue 10, October 2009: 1772-1783.

* Diagne, H.M., David, M., Lauret, P., Boland, J. Solar irradiation forecasting: state-of-the-art and proposition for future developments for small-scale insular grids. In Proceedings of the World Renewable Energy Forum 2012 (WREF 2012), Denver, USA, May 2012.

[[Category:Photovoltaics]]

[[Category:Solar power]]

[[Category:Weather prediction]]

==External links==

* [https://netlabe.com/how-to-predict-solar-energy-production-887ce31ec9d1 How to predict solar energy production], by Rafał Rybnik, case study on predicting solar electricity production from weather data.

* [https://www.nrel.gov/grid/solar-wind-forecasting.html Solar and Wind Forecasting projects], by National Renewable Energy Laboratory (NREL).

* [https://www.meteoswift.fr/en/solar-forecasts/] Short-term Solar Power Forecasts by meteo*swift