Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images
We present here a new method to predict cloud concentration five minutes in advance from all-sky images using the Artificial Neural Networks (ANN). An autoregressive neural network with backpropagation (Ar-BP) was created and trained with four years of all-sky images as inputs. The pictures were tak...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2019/4375874 |
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| _version_ | 1849408948212334592 |
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| author | Cristian Crisosto |
| author_facet | Cristian Crisosto |
| author_sort | Cristian Crisosto |
| collection | DOAJ |
| description | We present here a new method to predict cloud concentration five minutes in advance from all-sky images using the Artificial Neural Networks (ANN). An autoregressive neural network with backpropagation (Ar-BP) was created and trained with four years of all-sky images as inputs. The pictures were taken with a hemispheric sky imager fixed on the roof at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Firstly, a statistical method is presented to obtain key information of the pictures. Secondly, a new image-processing algorithm is suggested to optimize the cloud detection process starting with the Haze Index. Finally, the cloud concentration five minutes in advance at the IMUK is forecasted using machine learning methods. A persistence model forecast to provide a reference for comparison was generated. The results are quantified in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The new algorithm reduced both the RMSE and the MAE of the prediction by approximately 30% compared to the reference persistence model under diverse cloud conditions. The new algorithm could be used as a tool for the stable maintenance of the network for the transmission system operators, i.e., the primary control reserve (within 30 seconds) and the secondary control reserve (within 5 minutes). |
| format | Article |
| id | doaj-art-77efb1b0100441aab5404a243e6fd424 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-77efb1b0100441aab5404a243e6fd4242025-08-20T03:35:38ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2019-01-01201910.1155/2019/43758744375874Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky ImagesCristian Crisosto0Leibniz Universität Hannover, Institute for Meteorology and Climatology, Herrenhäuser Straße 2, 30419 Hannover, GermanyWe present here a new method to predict cloud concentration five minutes in advance from all-sky images using the Artificial Neural Networks (ANN). An autoregressive neural network with backpropagation (Ar-BP) was created and trained with four years of all-sky images as inputs. The pictures were taken with a hemispheric sky imager fixed on the roof at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Firstly, a statistical method is presented to obtain key information of the pictures. Secondly, a new image-processing algorithm is suggested to optimize the cloud detection process starting with the Haze Index. Finally, the cloud concentration five minutes in advance at the IMUK is forecasted using machine learning methods. A persistence model forecast to provide a reference for comparison was generated. The results are quantified in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The new algorithm reduced both the RMSE and the MAE of the prediction by approximately 30% compared to the reference persistence model under diverse cloud conditions. The new algorithm could be used as a tool for the stable maintenance of the network for the transmission system operators, i.e., the primary control reserve (within 30 seconds) and the secondary control reserve (within 5 minutes).http://dx.doi.org/10.1155/2019/4375874 |
| spellingShingle | Cristian Crisosto Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images International Journal of Photoenergy |
| title | Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images |
| title_full | Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images |
| title_fullStr | Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images |
| title_full_unstemmed | Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images |
| title_short | Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images |
| title_sort | autoregressive neural network for cloud concentration forecast from hemispheric sky images |
| url | http://dx.doi.org/10.1155/2019/4375874 |
| work_keys_str_mv | AT cristiancrisosto autoregressiveneuralnetworkforcloudconcentrationforecastfromhemisphericskyimages |