Sky camera imagery processing based on a sky classification using radiometric data

Alonso-Montesinos, Joaquín; Batlles, F.J.; López, Gabriel; Ternero, Antonio

doi:10.1016/j.energy.2014.02.035

Cited by 33 publications

(22 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combining sky camera images with radiometric data, the clouds were classified into four groups, showing radiation attenuation based on cloud type [11]. 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

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

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

Alonso-Montesinos

Batlles

2015

Energy

Self Cite

View full text Add to dashboard Cite

“…Many studies already determined cloud coverage based on all-sky camera images (e.g. Long et al, 2006;Kazantzidis et al, 2012;Alonso et al, 2014). Heinle et al (2010) presented a method for using all-sky camera images to classify cloud types.…”

Section: Introductionmentioning

confidence: 99%

Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras

et al. 2017

View full text Add to dashboard Cite

Abstract. The current study analyses the cloud radiative effect during the daytime depending on cloud fraction and cloud type at two stations in Switzerland over a time period of 3 to 5 years. Information on fractional cloud coverage and cloud type is retrieved from images taken by visible all-sky cameras. Cloud-base height (CBH) data are retrieved from a ceilometer and integrated water vapour (IWV) data from GPS measurements. The longwave cloud radiative effect (LCE) for low-level clouds and a cloud coverage of 8 oktas has a median value between 59 and 72 Wm −2 . For midand high-level clouds the LCE is significantly lower. It is shown that the fractional cloud coverage, the CBH and IWV all have an influence on the magnitude of the LCE. These observed dependences have also been modelled with the radiative transfer model MODTRAN5. The relative values of the shortwave cloud radiative effect (SCE rel ) for low-level clouds and a cloud coverage of 8 oktas are between −90 and −62 %. Also here the higher the cloud is, the less negative the SCE rel values are. In cases in which the measured direct radiation value is below the threshold of 120 Wm −2 (occulted sun) the SCE rel decreases substantially, while cases in which the measured direct radiation value is larger than 120 Wm −2 (visible sun) lead to a SCE rel of around 0 %. In 14 and 10 % of the cases in Davos and Payerne respectively a cloud enhancement has been observed with a maximum in the cloud class cirrocumulus-altocumulus at both stations. The calculated median total cloud radiative effect (TCE) values are negative for almost all cloud classes and cloud coverages.

show abstract

“…In addition to this, a cloud identification system was developed where the sky camera images were processed according to a sky classification [17], thus solving the pixel saturation problem in the solar area [18]. These studies were concluded with a solar radiation estimation using the pixel information from the sky image [19] applying the cloud motion vectors to obtain solar irradiance forecasting over the short term [20].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Sky camera imagery processing based on a sky classification using radiometric data

Cited by 33 publications

References 40 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

Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras

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

Contact Info

Product

Resources

About