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2019 | OriginalPaper | Buchkapitel

KloudNet: Deep Learning for Sky Image Analysis and Irradiance Forecasting

verfasst von : Dinesh Pothineni, Martin R. Oswald, Jan Poland, Marc Pollefeys

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

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Abstract

We present a novel image-based approach for estimating irradiance fluctuations from sky images. Our goal is a very short-term prediction of the irradiance state around a photovoltaic power plant 5–10 min ahead of time, in order to adjust alternative energy sources and ensure a stable energy network. To this end, we propose a convolutional neural network with residual building blocks that learns to predict the future irradiance state from a small set of sky images. Our experiments on two large datasets demonstrate that the network abstracts upon local site-specific properties such as day- and month-dependent sun positions, as well as generic properties about moving, creating, dissolving clouds, or seasonal changes. Moreover, our approach significantly outperforms the established baseline and state-of-the-art methods.

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Titel: KloudNet: Deep Learning for Sky Image Analysis and Irradiance Forecasting
verfasst von: Dinesh Pothineni
Martin R. Oswald
Jan Poland
Marc Pollefeys
Verlag: Springer International Publishing
Buch: Pattern Recognition
Print ISBN: 978-3-030-12938-5

Electronic ISBN: 978-3-030-12939-2

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-12939-2_37

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