Influence of input parameters on artificial neural networks for off-grid solar photovoltaic power forecasting
Author: | Aminu Bugaje, Akhilesh Yadav, Kedar MehtaORCiD, Felicia Ojinji, Mathias EhrenwirthORCiD, Christoph TrinklORCiD, Wilfried Zörner |
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Language: | English |
Document Type: | Conference Paper |
Conference: | NEIS 2021: Conference on Sustainable Energy Supply and Energy Storage Systems, Hamburg (Germany), 13.-14.09.2021 |
Year of first Publication: | 2021 |
published in (English): | NEIS 2021 |
Publisher: | VDE Verlag |
Place of publication: | Berlin |
ISBN: | 978-3-8007-5651-3 |
ISBN: | 978-3-8007-5652-0 |
First Page: | 135 |
Last Page: | 141 |
Review: | editorial review |
Open Access: | ja |
Tag: | artificial neural networks; correlation coefficient; mean absolute error; mean squared error; off-grid solar system; solar power forecast |
URL: | https://ieeexplore.ieee.org/document/9698257 |
URL: | https://www.vde-verlag.de/proceedings-en/565651017.html |
Faculties / Institutes / Organizations: | Fakultät Maschinenbau |
Institut für neue Energie-Systeme (InES) | |
Release Date: | 2022/03/11 |