Solar power forecasting: Difference between revisions

Revision as of 18:04, 20 March 2019

Revision as of 16:04, 16 April 2019

Line 29:

==Solar PV short-term forecasting==

''Short-term'' forecasting provides predictions up to 7 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 TransactionTransactions on Power Systems|date=2016|doi=10.1109/TPWRS.2016.2616902}}</ref>

Under this perspective, the meteorological resources are estimated at a different temporal and spatial resolution. This implies that meteorological variables and phenomena are looked from a more general perspective, not as local as nowcasting services do. In this sense, most of the approaches make use of different numerical weather prediction models (NWP) that provide an initial estimation of weather variables. Currently, several models are available for this purpose, such as [[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]] are the most representative of these models since they are widely used by different communities.

Line 65:

==External links==

* [http://www.solarelectricpower.org/media/147876/SEPA-ForecastReport-2014-ExecSummary.pdf SEPA – Predicting Solar Power Production]

[[Category:Photovoltaics]]