One-Hour Prediction of the Global Solar Irradiance from All-Sky Images Using Artificial Neural Networks

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dc.identifier.uri http://dx.doi.org/10.15488/4836
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/4879
dc.contributor.author Crisosto, Cristian
dc.contributor.author Hofmann, Martin
dc.contributor.author Mubarak, Riyad
dc.contributor.author Seckmeyer, Gunther
dc.date.accessioned 2019-05-21T10:57:55Z
dc.date.available 2019-05-21T10:57:55Z
dc.date.issued 2018
dc.identifier.citation Crisosto, C.; Hofmann, M.; Mubarak, R.; Seckmeyer, G.: One-Hour Prediction of the Global Solar Irradiance from All-Sky Images Using Artificial Neural Networks. In: Energies 11 (2018), Nr. 11, 2906. DOI: https://doi.org/10.3390/en11112906
dc.description.abstract We present a method to predict the global horizontal irradiance (GHI) one hour ahead in one-minute resolution using Artificial Neural Networks (ANNs). A feed-forward neural network with Levenberg–Marquardt Backpropagation (LM-BP) was used and was trained with four years of data from all-sky images and measured global irradiance as input. The pictures were recorded by a hemispheric sky imager at the Institute of Meteorology and Climatology (IMuK) of the Leibniz Universität Hannover, Hannover, Germany (52.23° N, 09.42° E, and 50 m above sea level). The time series of the global horizontal irradiance was measured using a thermopile pyranometer at the same site. The new method was validated with a test dataset from the same source. The irradiance is predicted for the first 10–30 min very well; after this time, the length of which is dependent on the weather conditions, the agreement between predicted and observed irradiance is reasonable. Considering the limited range that the camera and the ANN can “see”, this is not surprising. When comparing the results to the persistence model, we observed that the forecast accuracy of the new model reduced both the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of the one-hour prediction by approximately 40% compared to the reference persistence model under various weather conditions, which demonstrates the high capability of the algorithm, especially within the first minutes. eng
dc.language.iso eng
dc.publisher Basel : MDPI
dc.relation.ispartofseries Energies 11(2018), Nr. 11
dc.rights CC BY 4.0 Unported
dc.rights.uri http://www.creativecommons.org/licenses/by/4.0
dc.subject solar energy eng
dc.subject all-sky image eng
dc.subject solar irradiance prediction eng
dc.subject artificial neural networks eng
dc.subject.ddc 600 | Technik ger
dc.title One-Hour Prediction of the Global Solar Irradiance from All-Sky Images Using Artificial Neural Networks
dc.type article
dc.type Text
dc.relation.essn 1996-1073
dc.relation.doi https://doi.org/10.3390/en11112906
dc.bibliographicCitation.issue 11
dc.bibliographicCitation.volume 11
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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