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Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 21121

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Special Issue Editors

Prof. Dr. Joaquín Alonso-Montesinos

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Guest Editor

Department of Chemistry and Physics, University of Almería, 04120 Almeria, Spain
Interests: solar irradiance forecasting; cloud forecasting; CSP plants; PV plants; atmospheric extinction; sky cameras; satellite images; remote sensing; artificial neural networks; image processing; cloud detection; solar irradiance estimation
Special Issues, Collections and Topics in MDPI journals

Dr. Jesús Polo

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Guest Editor

Photovoltaic Solar Energy Unity (Renewable Energy Division) CIEMAT, 28040 Madrid, Spain
Interests: solar radiation; atmospheric physics; solar systems modeling; radiative transfer; remote sensing; solar power plant performance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In order to carry out a better characterization of the atmospheric state in any region or in specific sites, one of the key aspects to study in depth is the appearance and evolution of clouds. Many resources have been invested in promoting studies that reveal the impact of cloud cover on different types of systems, as well as in predicting when a cloud front may affect a specific geographical point.

To achieve these objectives, the first phase of cloud detection has traditionally been carried out with visual inspections by humans, but this has been relegated to the appearance of new technologies that have made automation and optimization possible in comparison with these more primitive techniques. In this sense, sky cameras play a very important role, since they are devices capable of capturing the appearance of clouds in the sky, providing a view of the sky from a terrestrial perspective. The growing appearance and improvement of these devices is allowing us to precisely and with certainty detect and monitor clouds, which can have great importance in any environment, especially those operated under renewable energy sources, becoming parties that contribute to improve the performance of equipment and systems involved.

The main objective of this Special Issue is to present works related to cloud prediction using images from sky cameras, where they can be combined with remote sensing techniques for an optimal and accurate prediction.

Dr. Joaquín Alonso-Montesinos
Dr. Jesús Polo
Guest Editors

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Keywords

Cloud forecasting
Remote sensing techniques
Sky camera imagery
Cloud motion estimation
Artificial intelligence techniques and cloudiness forecasting
Cloud evolution using sky cams

Published Papers (7 papers)

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Research

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15 pages, 586 KiB

Open AccessArticle

Multivariate Analysis for Solar Resource Assessment Using Unsupervised Learning on Images from the GOES-13 Satellite

by Jared D. Salinas-González, Alejandra García-Hernández, David Riveros-Rosas, Gamaliel Moreno-Chávez, Luis F. Zarzalejo, Joaquín Alonso-Montesinos, Carlos E. Galván-Tejada, Alejandro Mauricio-González and Adriana E. González-Cabrera

Remote Sens. 2022, 14(9), 2203; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092203 - 05 May 2022

Cited by 2 | Viewed by 1483

Abstract

Solar resource assessment is of paramount importance in the planning of solar energy applications. Solar resources are abundant and characterization is essential for the optimal design of a system. Solar energy is estimated, indirectly, by the processing of satellite images. Several analyses with satellite images have considered a single variable—cloudiness. Other variables, such as albedo, have been recognized as critical for estimating solar irradiance. In this work, a multivariate analysis was carried out, taking into account four variables: cloudy sky index, albedo, linke turbidity factor (TL2), and altitude in satellite image channels. To reduce the dimensionality of the database (satellite images), a principal component analysis (PCA) was done. To determine regions with a degree of homogeneity of solar irradiance, a cluster analysis with unsupervised learning was performed, and two clustering techniques were compared: k-means and Gaussian mixture models (GMMs). With respect to k-means, the GMM method obtained a smaller number of regions with a similar degree of homogeneity. The multivariate analysis was performed in Mexico, a country with an extended territory with multiple geographical conditions and great climatic complexity. The optimal number of regions was 17. These regions were compared for annual average values of daily irradiation data from ground stations using multiple linear regression. A comparison between the mean of each region and the ground station measurement showed a linear relationship with a

R^{2}

score of 0.87. The multiple linear regression showed that the regions were strongly related to solar irradiance. The optimal sites found are shown on a map of Mexico. Full article

(This article belongs to the Special Issue Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery)

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17 pages, 15996 KiB

Open AccessArticle

Nowcasting System Based on Sky Camera Images to Predict the Solar Flux on the Receiver of a Concentrated Solar Plant

by Joaquín Alonso-Montesinos, Rafael Monterreal, Jesus Fernandez-Reche, Jesús Ballestrín, Gabriel López, Jesús Polo, Francisco Javier Barbero, Aitor Marzo, Carlos Portillo and Francisco Javier Batlles

Remote Sens. 2022, 14(7), 1602; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071602 - 26 Mar 2022

Cited by 4 | Viewed by 1764

Abstract

As part of the research for techniques to control the final energy reaching the receivers of central solar power plants, this work combines two contrasting methods in a novel way as a first step towards integrating such systems in solar plants. To determine the effective power reaching the receiver, the direct normal irradiance was predicted at ground level using a total sky camera, TSI-880 model. Subsequently, these DNI values were used as the inputs for a heliostat model (Fiat-Lux) to trace the sunlight’s path according to the mirror features. The predicted valuex of flux, obtained from these simulations, differ of less than 20% from the real values. This represents a significant advance in integrating different technologies to quantify the losses produced in the path from the heliostats to the central receiver, which are normally caused by the presence of atmospheric attenuation factors. Full article

(This article belongs to the Special Issue Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery)

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16 pages, 4147 KiB

Open AccessArticle

Assessment of Nighttime Cloud Cover Products from MODIS and Himawari-8 Data with Ground-Based Camera Observations

by Nofel Lagrosas, Alifu Xiafukaiti, Hiroaki Kuze and Tatsuo Shiina

Remote Sens. 2022, 14(4), 960; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040960 - 16 Feb 2022

Cited by 2 | Viewed by 2862

Abstract

Comparing cloud cover (CC) products from different satellites with the same ground-based CC dataset provides information on the similarities or differences of values among satellite products. For this reason, 42-month CC products from Moderate Resolution Imaging Spectrometer’s (MODIS) Collection 6.1 daily cloud cover products (MOD06_L2, MYD06_L2, MOD08_D3, and MYD08_D3) and Himawari-8 are compared with the ground-based camera datasets. The comparison shows that CC from MODIS differs from ground measurement CC by as much as 57% over Chiba, Japan, when low CC is observed by the camera. This indicates MODIS’s ability to capture high-level clouds that are not effectively seen from the ground. When the camera detects high CC, an indication of the presence of low-level clouds, CC from MODIS is relatively higher than the CC from the camera. In the case of Himawari-8 data, when the camera observes low CC, this difference is around 0.7%. This result indicates that high-level clouds are not effectively observed, but the Himawari-8 data correlates well with camera observations. When the camera observes high CC, Himawari-8-derived CC is lower by around 10% than CC from the camera. These results show the potential of continuous observations of nighttime clouds using the camera to provide a dataset that can be used for intercomparison among nighttime satellite CC products. Full article

(This article belongs to the Special Issue Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery)

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18 pages, 4586 KiB

Open AccessArticle

Angular Calibration of Visible and Infrared Binocular All-Sky-View Cameras Using Sun Positions

by Wanyi Xie, Yiren Wang, Yingwei Xia, Zhenyu Gao and Dong Liu

Remote Sens. 2021, 13(13), 2455; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132455 - 23 Jun 2021

Cited by 1 | Viewed by 1932

Abstract

Visible and infrared binocular all-sky-view cameras can provide continuous and complementary ground-based cloud observations. Accurate angular calibration for every pixel is an essential premise to further cloud analysis and georeferencing. However, most current calibration methods mainly rely on calibration plates, which still remains difficult for simultaneously calibrating visible and infrared binocular cameras, especially with different imaging resolutions. Thus, in this study, we present a simple and convenient angular calibration method for wide field-of-view visible and infrared binocular cameras. Without any extra instruments, the proposed method only utilizes the relation between the angular information of direct sun lights and the projected sun pixel coordinates to compute the geometric imaging parameters of the two cameras. According to the obtained parameters, the pixel-view-angle for the visible and infrared all-sky images is efficiently computed via back projection. Meanwhile, the projected pixel coordinates for the incident lights at any angle can also be computed via reprojection. Experimental results show the effectiveness and accuracy of the proposed angular calibration through the error estimation of reprojection and back projection. As a novel application, we successfully achieve visible and infrared binocular image registration at the pixel level after finishing angular calibration, which not only verifies the accuracy of calibration results, but also contributes to further cloud parameter analysis under these two different imaging features. The registration results, to our knowledge, also provide a reference for the current blank in visible and infrared binocular cloud image registration. Full article

(This article belongs to the Special Issue Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery)

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Graphical abstract

17 pages, 4833 KiB

Open AccessArticle

The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

by Junxia Jiang, Qingquan Lv and Xiaoqing Gao

Remote Sens. 2020, 12(21), 3671; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213671 - 09 Nov 2020

Cited by 15 | Viewed by 3177

Abstract

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time. Full article

(This article belongs to the Special Issue Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery)

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14 pages, 5020 KiB

Open AccessArticle

Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery

by Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas and Roberto Bonifaz

Remote Sens. 2020, 12(16), 2643; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162643 - 16 Aug 2020

Cited by 11 | Viewed by 3581

Abstract

The atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajectory in real time is one of the functions and tools that CSP operators must develop for plant optimization, and to anticipate drops in solar irradiance. For short forecast of cloud movement (10 min) is enough with describe the cloud advection while for longer forecast (over 15 min), it is necessary to predict both advection and cloud changes. In this paper, we present a model that predict only the cloud advection displacement trajectory for different sky conditions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. In the short term, up to 10 min the future position of the cloud front is predicted with 92% certainty while for 25–30 min, the best predicted precision was 82%. Full article

(This article belongs to the Special Issue Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery)

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Other

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17 pages, 4135 KiB

Open AccessTechnical Note

Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera

by Yiren Wang, Dong Liu, Wanyi Xie, Ming Yang, Zhenyu Gao, Xinfeng Ling, Yong Huang, Congcong Li, Yong Liu and Yingwei Xia

Remote Sens. 2021, 13(9), 1852; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091852 - 10 May 2021

Cited by 14 | Viewed by 4853

Abstract

The formation and evolution of clouds are associated with their thermodynamical and microphysical progress. Previous studies have been conducted to collect images using ground-based cloud observation equipment to provide important cloud characteristics information. However, most of this equipment cannot perform continuous observations during the day and night, and their field of view (FOV) is also limited. To address these issues, this work proposes a day and night clouds detection approach integrated into a self-made thermal-infrared (TIR) all-sky-view camera. The TIR camera consists of a high-resolution thermal microbolometer array and a fish-eye lens with a FOV larger than 160°. In addition, a detection scheme was designed to directly subtract the contamination of the atmospheric TIR emission from the entire infrared image of such a large FOV, which was used for cloud recognition. The performance of this scheme was validated by comparing the cloud fractions retrieved from the infrared channel with those from the visible channel and manual observation. The results indicated that the current instrument could obtain accurate cloud fraction from the observed infrared image, and the TIR all-sky-view camera developed in this work exhibits good feasibility for long-term and continuous cloud observation. Full article

(This article belongs to the Special Issue Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery)

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