Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast

Escrig, H.; Batlles, F.J.; Alonso-Montesinos, Joaquín; Baena, Ferdinando M Rodriguez Y; Bosch, J.L.; Salbidegoitia, Ibon B.; Burgaleta, J.I.

doi:10.1016/j.energy.2013.01.054

Cited by 75 publications

(43 citation statements)

References 40 publications

(43 reference statements)

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“…Cloud identification also using satellite images, was carried out in Ref. [9], where the authors classified clouds into three different heights (high, medium and low) and studied cloud motion using the maximum cross-correlation method. Cloud cover was likewise studied using another emerging technology, the total sky imager where sky cameras are installed at ground level, giving a different sky vision to the satellite images.…”

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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“…Ghosh et al (2006) [5] applied certain rules to METEOSAT-5 satellite images to study three different types of cloud coverage: overcast, partially covered and cloudless. One further step has been taken by Escrig et al (2013) [6], where satellite imagery has been used to do a classification of clouds.…”

Section: Introductionmentioning

confidence: 99%

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

Alonso-Montesinos

Ternero²,

Batlles

et al. 2014

Energy Procedia

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“…Given the geographical proximity, the forecast horizon of this class of methods is ranged from 20 seconds to 3 minutes [3]. On the other extreme of the forecast spatiotemporal scale, satellite images can be used to detect, classify the clouds [4]. However, obtaining the future clouds information does not complete the forecast; ground level irradiance must be derived from the forecast cloud images, and the mapping is not trivial [5].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of global horizontal irradiance by exponential smoothing, using decompositions

Yang

Sharma

Ye³

et al. 2015

Energy

121

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Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. Furthermore, satellite and sky camera based methods lose resolution at a 1-h time horizon. Exponential smoothing (ETS) has received great attention in the recent years due to the invention of its state space formulation. We explore 1-h ahead ETS forecasting of solar irradiance in this paper. Several knowledge based decompositions are considered to improve the forecast accuracy and computation speed.Three methods are proposed. The first method considers an additive seasonal-trend decomposition prior to the use of ETS. In such a way, the ETS state space is reduced, thus facilitates online forecasting applications. The second method decomposes the global horizontal irradiance (GHI) time series into a direct component and a diffuse component. These two components are used as forecasting model inputs separately; the results are recombined through the closure equation. We consider the time series of cloud cover index in the third method. ETS is first applied to the cloud cover time series to produce the forecast. The forecast cloud cover is then used to reconstruct GHI through regressions. It is found that the third method performs the best among three methods and all proposed methods outperforms persistence.

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Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast

Cited by 75 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

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

Forecasting of global horizontal irradiance by exponential smoothing, using decompositions

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