Short-term solar irradiance and irradiation forecasts via different time series techniques: A preliminary study

被引：0

作者：

Join, Cedric ^{[1
,2
,3
]}

Voyant, Cyril ^{[4
,5
]}

Fliess, Michel ^{[2
,6
]}

Muselli, Marc ^{[5
]}

Nivet, Marie-Laure ^{[5
]}

Paoli, Christophe ^{[7
]}

Chaxel, Frederic ^{[1
]}

机构：

[1] Univ Lorraine, CNRS, UMR 7039, CRAN, BP 239, F-54506 Vandoeuvre Les Nancy, France

[2] ALIEN, F-54003 Nancy, France

[3] INRIA Lille Nord Europe, Projet Non A, Villeneuve Dascq, France

[4] Hop Castelluccio, Unite Radiothe, F-20177 Ajaccio, France

[5] Univ Cors Pasquale Paoli, CNRS, UMR 6134, SPE, F-20250 Corte, France

[6] Ecole Polytech, CNRS, UMR 7161, LIX, F-91128 Palaiseau, France

[7] Univ Galatasaray, Dept Gen Informat, TR-34357 Istanbul, Turkey

来源：

3RD INTERNATIONAL SYMPOSIUM ON ENVIRONMENTAL FRIENDLY ENERGIES AND APPLICATIONS (EFEA 2014) | 2014年

关键词：

Meteorological forecasts; solar irradiance and irradiation; electricity production; time series; trends; quick fluctuations; machine learning; multilayer perceptron; big data; ARTIFICIAL NEURAL-NETWORKS; RADIATION; PREDICTION; MODELS;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This communication is devoted to solar irradiance and irradiation short-term forecasts, which are useful for electricity production. Several different time series approaches are employed. Our results and the corresponding numerical simulations show that techniques which do not need a large amount of historical data behave better than those which need them, especially when those data are quite noisy.

引用

页数：6

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