Prediction of HSV color model parameter values of cloud movement picture based on artificial neural networks

Radovan, Aleksander; Ban, Ž.

doi:10.23919/mipro.2018.8400202

Cited by 2 publications

(4 citation statements)

References 3 publications

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“…The standard error level shown in Figures 13,15,17 and 19 shows that it is in range of 10-20%. The results show that it is the highest in periods when clouds start and with sun coverage, especially if the clouds are transparent and the sunlight can break through them.…”

Section: Resultsmentioning

confidence: 94%

“…The data shown covers the period on 13 July 2020. from 13:45 to 14:40 and from 12:00 to 12:45. Figures [12][13][14][15][16][17][18][19] show the situation in the sky where several clouds were moving towards the sun and corresponding error levels. The red curve, which represents the real-time sun un-coverage level on images that compare the real-time and predicted sun-uncoverage level, shows that the visible area of the sun changes significantly in a nonlinear manner as the clouds begin to cover or uncover the sun.…”

Section: Resultsmentioning

confidence: 99%

“…It has been shown that HSV color space gives the best results, so the same approach is used in this work to accurately detect the cloud edges, cloud centroid, and cloud motion. Optimal HSV thresholds for best results in cloud edge detection can be determined by an artificial neural network-based system described in our previous work [15] since the sky image often contains different parameters (number of clouds, cloud sizes, and histogram values) and colors that may require different settings for image processing. Other variants of cloud detection on sky/cloud images captured by ground-based cameras are based on cloud segmentation [16], which is learning-based and not suitable for rapidly changing weather conditions.…”

Section: Introductionmentioning

confidence: 99%

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Solar Irradiance Forecast Based on Cloud Movement Prediction

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Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solar Irradiance Forecast Based on Cloud Movement Prediction

Radovan¹,

Šunde

Kučak

et al. 2021

Energies

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“…Hue coloring power is a basic item of ink testing, which has a great influence on the color of ink [13]. Hue is a specific feature of the ink color, and coloring power refers to the size of the degree to which one ink affects the color change of another ink.…”

Section: Color Performance Testmentioning

confidence: 99%

Performance of water-based functional inks and their application in packaging printing

2024

jmpd

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This study is concerned with the preparation, performance testing and application of aqueous functional inks in packaging printing. The preparation process of aqueous functional inks is firstly introduced, focusing on the rheological and color properties. A comprehensive understanding of the fluidity and deformation behavior of the ink in printing is provided, which provides important process parameters for practical application. Attention is also paid to the color reproduction and stability of aqueous functional inks to ensure that the printed products have excellent visual effects in the packaging field. Finally, the wide application of aqueous functional inks in packaging printing is outlined. The correlation coefficients of red, green and blue are 0.961, 0.985 and 0.950. It provides a scientific basis for the optimization of the performance of aqueous functional inks, and at the same time provides an environmentally friendly and efficient choice for the packaging printing industry.

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Prediction of HSV color model parameter values of cloud movement picture based on artificial neural networks

Cited by 2 publications

References 3 publications

Solar Irradiance Forecast Based on Cloud Movement Prediction

Solar Irradiance Forecast Based on Cloud Movement Prediction

Performance of water-based functional inks and their application in packaging printing

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