Prediction of Cloudiness in Short Time Periods using Techniques of Remote Sensing and Image Processing

Alonso-Montesinos, Joaquín; Ternero, Antonio; Batlles, F.J.; López, Gabriel; Rodríguez, Jorge Armando; Burgaleta, J.I.

doi:10.1016/j.egypro.2014.03.241

Cited by 18 publications

(10 citation statements)

References 13 publications

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“…in addition to this, a cloud identification system was developed where the sky camera images were processed according to a sky classification [12], thus solving the saturation problem in the solar area [13]. By combining both satellite and sky camera imagery, a cloudiness forecast was made for the short-and medium-term [14], where a user interface provided cloudiness forecasting in real-time [15].…”

Section: Introductionmentioning

confidence: 99%

Solar radiation forecasting in the short- and medium-term under all sky conditions

Alonso-Montesinos

Batlles

2015

Energy

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Section: Introductionmentioning

confidence: 99%

Solar radiation forecasting in the short- and medium-term under all sky conditions

Alonso-Montesinos

Batlles

2015

Energy

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“…This objective was presented in a study, where satellite and sky camera imagery where combined to make a cloudiness forecast for the short-and medium-term [25] with satisfactory results. The cloudiness forecast was represented using a real-time GUI designed especially for solar power plant operators, providing useful information about the cloud presence over a solar field in near time horizons [26]. Therefore, the importance of cloud classification is valued in various fields, but overall in solar power plant management.…”

Section: Introductionmentioning

confidence: 99%

The application of Bayesian network classifiers to cloud classification in satellite images

et al. 2016

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a b s t r a c tThe need to reduce the impact of traditional electricity generation necessitates an increase in the optimization of alternative systems that produce less environmental contamination. Renewables play a key role, with solar energy considered one of the most important energy supply sources. Solar power plants have to be perfectly designed to optimize electricity generation, and their placement must be as suitable as possible for the meteorological conditions. Clouds are the most mitigating factor in solar energy production and their study is decisive in locating the plant. Apart from the importance of studying clouds before building the solar plants, cloud detection is equally decisive in adapting plant operation to cloud types during solar power plant operation.This adaptation benefits plant performance and allows electricity management to be integrated into the electricity grid. Nonetheless, the majority of cloud studies determine atmospheric parameters, which are sometimes not available. In this work, we have developed an automatic, fully-exportable cloud classification model, where Bayesian network classifiers were applied to satellite images so as to determine the presence of clouds, classifying the sky as cloudless or with high, medium and low cloud presence. There was an average success probability of 90% for all sky conditions.

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“…Chiefly, the sequence resulting by collecting successive images have been useful to study the characteristics and the spatial distribution of clouds. Many satellite image based studies have focused on detecting the movement of clouds to forecast the solar irradiance which is essential for the effective operation of various solar applications such as solar thermal systems, photovoltaic systems, and grid regulation [2]. However, the low resolution of satellite images with respect to space and time is not adequate to satisfy the requirements of many real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Clouds Motion Estimation from Ground-Based Sky Camera and Satellite Images

Zaher¹,

Ghanem²

2018

Colorimetry and Image Processing

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Estimation of cloud motion is a challenging task due to the non-linear phenomena of cloud formation and deformation. Satellite images processing is a popular tool used to study the characteristics of clouds which constitute major factors in forecasting the meteorological parameters. Due to the low resolution of satellite images, researchers have turned towards analyzing the high-resolution images captured by ground-based sky cameras. The first objective of this chapter is to demonstrate the different techniques used to estimate clouds motion and to compare them with respect to the accuracy and the computational time. The second aim is to propose a fast and efficient block matching technique based on combining the two types of images. The first idea of our approach is to analyze the low-resolution satellite images to detect the direction of motion. Then, the direction is used to orient the search process to estimate the optimal motion vectors from the high-resolution ground-based sky images. The second idea of our method is to use the entropy technique to find the optimal block sizes. The third idea is to imply an adaptive cost function to perform the matching process. The comparative study demonstrates the high performance of the proposed method with regards to the robustness, the accuracy and the computation time.

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Prediction of Cloudiness in Short Time Periods using Techniques of Remote Sensing and Image Processing

Cited by 18 publications

References 13 publications

Solar radiation forecasting in the short- and medium-term under all sky conditions

Solar radiation forecasting in the short- and medium-term under all sky conditions

The application of Bayesian network classifiers to cloud classification in satellite images

Clouds Motion Estimation from Ground-Based Sky Camera and Satellite Images

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