Abstract
A novel high-resolution method for forecasting cloud motion from all-sky images using deep learning is presented. A convolutional neural network (CNN) was created and trained with more than two years of all-sky images, recorded by a hemispheric sky imager (HSI) at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Using the haze indexpostprocessing algorithm, cloud characteristics were found, and the deformation vector of each cloud was performed and used as ground truth. The CNN training process was built to predict cloud motion up to 10 min ahead, in a sequence of HSI images, tracking clouds frame by frame. The first two simulated minutes show a strong similarity between simulated and measured cloud motion, which allows photovoltaic (PV) companies to make accurate horizon time predictions and better marketing decisions for primary and secondary control reserves. This cloud motion algorithm principally targets global irradiance predictions as an application for electrical engineering and in PV output predictions. Comparisons between the results of the predicted region of interest of a cloud by the proposed method and real cloud position show a mean Sørensen–Dice similarity coefficient (SD) of 94 ± 2.6% (mean ± standard deviation) for the first minute, outperforming the persistence model (89 ± 3.8%). As the forecast time window increased the index decreased to 44.4 ± 12.3% for the CNN and 37.8 ± 16.4% for the persistence model for 10 min ahead forecast. In addition, up to 10 min global horizontal irradiance was also derived using a feed-forward artificial neural network technique for each CNN forecasted image. Therefore, the new algorithm presented here increases the SD approximately 15% compared to the reference persistence model.
Highlights
Published: 1 February 2021Short-time cloud motion prediction has a huge impact on the future behavior of the power generation output of solar photovoltaic (PV) power plants [1]
The results showed a 30% reduction of errors when compared to the persistence predict cloud concentrations one minute in advance using artificial neural networks model under diverse cloud conditions
A convolutional neural network (CNN) was trained using hemispherical sky images as inputs, and a statistical approach for forecasting future cloud motion was per
Summary
Published: 1 February 2021Short-time cloud motion prediction has a huge impact on the future behavior of the power generation output of solar photovoltaic (PV) power plants [1]. Clouds can even increase the solar radiation at the surface by reflection and/or forward scattering [2,3,4] To compensate for these ramp events, very short-term forecasting/forecasts can help power plant operators to accurately manage. The analyses of clouds play an important role in both scientific and business enterprises, where these severe fluctuations in the energy output are incompatible with the established safety standards for the electricity distribution systems [5]. In this context, the introduction of hemispheric sky imager (HSI) systems as efficient ground surface equipment for cloud data assessment have already been proven by various authors [6].
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