Solar power forecasting: Difference between revisions

Revision as of 15:22, 6 June 2019

Revision as of 20:55, 12 August 2019

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The energy generation forecasting problem is closely linked to the problem of ''weather variables forecasting''. Indeed, this problem is usually split into two parts, on one hand focusing on the forecasting of solar PV or any other meteorological variable and on the other hand estimating the amount of energy that a concrete power plant will produce with the estimated meteorological resource.

In general, the way to deal with this difficult problem is usually related to the spatial and temporal scales we are interested in, which yields to different approaches that can be found in the literature. In this sense, it is useful to classify these techniques depending on the forecasting horizon, so it is possible to distinguish between ''now-casting'' (forecasting 3–4 hours ahead), ''short-term forecasting'' (up to 7 days ahead) and ''long-term forecasting'' (months, years…years...)

Solar radiation closely follows the physical and biological development of the earth. Its spatial and sequential heterogeneity powerfully influence the forcing of environmental and hydrological organisms by manipulating air temperature, soil moisture and vapor transpiration, snow cover and lots of photochemical procedures. Therefore, solar radiation drives place efficiency and plant life allotment, organism a key feature in undeveloped and forestry sciences that be obliged to be known precisely. The quantity of solar radiation obtainable at the earth’ surface is at the outset controlled at worldwide balance, organism above all precious by the Sun Earth geometry and the atmosphere. On the other hand, a complete explanation of its freedom time unpredictability require deliberation of limited procedure which frequently turn out to be also applicable, as is the casing in mountainous region. Predominantly, limited territory adjust the inward bound solar radiation by shadow casts, slope of elevation, surface gradient and compass reading, as a result, precise spatial model of inward bound solar radiation be supposed to regard as the pressure of the terrain surface. In the final time, more than a few events to consist of the confined terrain special effects in the solar radiation countryside have been projected, such as the use of Geographical Information Systems (GIS), artificial intelligence or post dispensation of satellite stand technique. Solar radiation can be also evaluated using numerical weather forecast (NWP) models. Nevertheless, the space and time balance determined with them and the incomplete computational ability frequently avoid the deliberation of terrain connected property.

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==Nowcasting==

The term "Nowcasting" in the context of solar power forecasting, generally refers to same spatial and temporal scales as [[Nowcasting (meteorology)|meteorological Nowcasting]], which focuses on forecast horizons from a few minutes ahead, out to 4-64–6 hours ahead. In general, the 'Nowcasting' forecast horizon refers to those not well-served by global numerical weather prediction models, which produce outputs at up to hourly resolutions and only update every 6 hours. [[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 very important for [[Electrical grid|electrical grid operators]] in order to guarantee the matching of supply and demand on energy markets. Such solar power nowcasting services are usually related to temporal resolutions of 5 to 15 minutes, with updates as frequent as every 5 minutes. The regular updates and relatively high resolutions required from these methods require automatic weather data acquisition and processing techniques, which are chiefly accomplished by two 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>:

#'''Statistical techniques.''' These 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.

#[[File:Satellite Based Solar Nowcasting.gif|thumb|An example of satellite-based cloud cover nowcasting, which is used to generate predication of solar power outputs. Credit: [http://solcastglobal.com Solcast]]]'''Satellite based methods.''' These methods leverage the several [[Geostationary orbit|geostationary]] Earth observing [[Weatherweather satellite|weather satellites]]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 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>. Relevant methods for applying physical models based on satellite image processing techniques provide an estimation of future atmospheric values can be found in ''Alvarez et al.'', 2010.

==Short-term solar power forecasting==