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

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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

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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.
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Mathematik und Physik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 69 67.65%
2 image of flag of United States United States 21 20.59%
3 image of flag of Russian Federation Russian Federation 3 2.94%
4 image of flag of Pakistan Pakistan 2 1.96%
5 image of flag of France France 2 1.96%
6 image of flag of Ukraine Ukraine 1 0.98%
7 image of flag of Poland Poland 1 0.98%
8 image of flag of United Kingdom United Kingdom 1 0.98%
9 image of flag of Czech Republic Czech Republic 1 0.98%
10 image of flag of China China 1 0.98%

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