Feature selection using a genetic algorithm for solar power prediction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099082" target="_blank" >RIV/61989100:27240/16:86099082 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-33609-1_37" target="_blank" >http://dx.doi.org/10.1007/978-3-319-33609-1_37</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-33609-1_37" target="_blank" >10.1007/978-3-319-33609-1_37</a>

Result language
angličtina
Original language name
Feature selection using a genetic algorithm for solar power prediction
Original language description
We study an automatic procedure for selecting the most useful external variables for solar power forecasting. We use Genetic Algorithm (GA) as combinatorial optimisation tool of these feature variables. As forecasting model we use a particular case of Neural Network named Echo State Networks (ESN), which has been successfully used in the community for solving temporal learning problems. We study more than 20 weather variables that can impact on the solar power, and we compare the obtained results by GAs with the Spearman's rank correlation coefficient. Our approach is evaluated on a well-known public dataset, and we obtain promising results. (C) Springer International Publishing Switzerland 2016.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Advances in Intelligent Systems and Computing. Volume 450
ISBN
978-3-319-33608-4
ISSN
1615-3871
e-ISSN
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Number of pages
11
Pages from-to
409-419
Publisher name
Springer
Place of publication
Heidelberg
Event location
Soči
Event date
May 16, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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