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Eduardo W. Luiz
Institute for Meteorology and Climatology, Leibniz Universität Hannover, Herrenhäuser Straße 2, 30419 Hannover, Germany

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Journal article
Published: 01 February 2021 in Energies
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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.

ACS Style

Cristian Crisosto; Eduardo W. Luiz; Gunther Seckmeyer. Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images. Energies 2021, 14, 753 .

AMA Style

Cristian Crisosto, Eduardo W. Luiz, Gunther Seckmeyer. Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images. Energies. 2021; 14 (3):753.

Chicago/Turabian Style

Cristian Crisosto; Eduardo W. Luiz; Gunther Seckmeyer. 2021. "Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images." Energies 14, no. 3: 753.

Journal article
Published: 28 November 2019 in Energies
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PV modules tilted and oriented toward east and west directions gain gradually more importance as an alternative to the presently-preferred south (north in the Southern Hemisphere) orientation and it is shown to become economically superior even under the reimbursement of feed-in tariff (FIT). This is a consequence of the increasing spread between the decreasing costs of self-consumed solar power and the costs for power from the grid. One-minute values of irradiance were measured by silicon sensors at different orientations and tilt angles in Hannover (Germany) over three years. We show that south-oriented collectors give the highest electrical power during the day, whereas combinations of east and west orientations (E-W) result in the highest self-consumption rate (SC), and combinations of southeast and southwest (SE-SW) orientations result in the highest degree of autarky (AD), although they reduce the yearly PV Power by 5–6%. Moreover, the economic analysis of PV systems without FIT shows that the SE-SW and E-W combinations have the lowest electricity cost and they are more beneficial in terms of internal rate of return (IRR), compared to the S orientation at the same tilt. For PV systems with FIT, the S orientation presently provides the highest transfer of money from the supplier. However, as a consequence of the continuing decline of FIT, the economic advantage of S orientation is decreasing. E-W and SE-SW orientations are more beneficial for the owner as soon as FIT decreases to 7 Ct/kWh. East and west orientations of PV modules do not only have benefits for the individual owner but avoid high costs for storing energy—regardless who would own the storage facilities—and by avoiding high noon peaks of solar energy production during sunny periods, which would become an increasing problem for the grid if more solar power is installed. Furthermore, two types of commonly used PV software (PVSOL and PVsyst) were used to simulate the system performance. The comparison with measurements showed that both PV software underestimate SC and AD for all studied orientations, leading to the conclusion that improvements are necessary in modelling.

ACS Style

Riyad Mubarak; Eduardo Weide Luiz; Gunther Seckmeyer. Why PV Modules Should Preferably No Longer Be Oriented to the South in the Near Future. Energies 2019, 12, 4528 .

AMA Style

Riyad Mubarak, Eduardo Weide Luiz, Gunther Seckmeyer. Why PV Modules Should Preferably No Longer Be Oriented to the South in the Near Future. Energies. 2019; 12 (23):4528.

Chicago/Turabian Style

Riyad Mubarak; Eduardo Weide Luiz; Gunther Seckmeyer. 2019. "Why PV Modules Should Preferably No Longer Be Oriented to the South in the Near Future." Energies 12, no. 23: 4528.