English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

When a territory is poorly instrumented, geostationary satellites data can beuseful to predict global solar radiation. In this paper, we use geostationarysatellites data to generate 2-D time series of solar radiation for the nexthour. The results presented in this paper relate to a particular territory, theCorsica Island, but as data used are available for the entire surface of theglobe, our method can be easily exploited to another place. Indeed 2-D hourlytime series are extracted from the HelioClim-3 surface solar irradiationdatabase treated by the Heliosat-2 model. Each point of the map have been usedas training data and inputs of artificial neural networks (ANN) and as inputsfor two persistence models (scaled or not). Comparisons between these modelsand clear sky estimations were proceeded to evaluate the performances. We founda normalized root mean square error (nRMSE) close to 16.5% for the two bestpredictors (scaled persistence and ANN) equivalent to 35-45% related to groundmeasurements. Finally in order to validate our 2-D predictions maps, weintroduce a new error metric called the gamma index which is a criterion forcomparing data from two matrixes in medical physics. As first results, we foundthat in winter and spring, scaled persistence gives the best results (gammaindex test passing rate is respectively 67.7% and 86%), in autumn simplepersistence is the best predictor (95.3%) and ANN is the best in summer(99.8%).

Time series modeling and large scale global solar radiation forecasting from geostationary satellites data