Solar power forecasting: Difference between revisions

Revision as of 21:36, 9 December 2019

Revision as of 01:26, 9 March 2020

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#'''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 [[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 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.

#[[File:Steadysun_All_Sky_Imager_Solar_Power_Forecasting_SolutionDiagram-of-the-UCSD-sky-imager-USI-and-related-solar-and-sky-geometries-th-0-is-the.pngjpg|alt=|thumb|An example of sky-imager used for detecting, tracking and predicting cloud imagecover acquisitionconditions in the vicinity of a solar energy facility of interest. Most often, these devices are used to simulatemake estimates of solar powerirradiance from the images using local calibration by a forecastingpyranometer. The solar irradiance short-term forecasts are then fed into PV power modelling routines to generate a solar power forecast. <br />Credit: [https://wwwtopex.steady-sunucsd.comedu/rs/presentations/cortes.pdf UC San SteadysunDiego]]]'''Ground based techniques.''' These techniques are generally 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==

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* 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.

==External links==

* [https://www.steady-sun.com/ STEADYSUN - Solar Power Forecasting Services] providing solar production forecasts from 1 minute to 15 days ahead.

* [http://www.enfor.dk ENFOR – Solar Power Production Forecasting]

[[Category:Photovoltaics]]