# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
###### Abstract
[PERSON], [PERSON]; [PERSON]; [PERSON], H.-J. A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera [PERSON], [PERSON]; [PERSON] [PERSON], H.-J. A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera [PERSON], [PERSON]; [PERSON]; [PERSON], H.-J. A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera [PERSON]. [PERSON], [PERSON], [PERSON]; [PERSON]; [PERSON], [PERSON] A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera [PERSON], [PERSON], [PERSON]; [PERSON]; [PERSON], [PERSON] A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera [PERSON], [PERSON], [PERSON]; [PERSON]; [PERSON], [PERSON] A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera [PERSON], [PERSON], [PERSON], [PERSON]; [PERSON]; [PERSON], [PERSON] A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera [PERSON], [PERSON], R.  
sky images and satellite images are used for forecasting solar irradiance in the short-term forecast horizon. These methods are able to forecast solar irradiance several minutes to several hours in advance. This short-term forecasting is useful in the anticipation of ramp events caused by variations in solar irradiance. Compared to satellite images, sky images offer more detailed cloud information with higher spatial resolution. Therefore, the solar irradiance forecasting based on sky images is appropriate for management of a specific PV system, in spite of shorter time horizon. In general, information about clouds, especially for cloud cover data, is collected through human observations, and the World Meteorological Organization provides the rules for the registration of cloud cover. Observers estimate the cloud cover in oktas or tenths, in which the sky is divided into 8 or 10 regions and the regions that are covered by clouds are evaluated. However, the accuracy of this traditional method is deemed unsatisfactory: it provides low temporal and spatial resolutions, and errors can be introduced through the subjective nature of observers' judgments. Hence, a hemispherical sky camera is an alternative solution that can be used to address some of these problems. The camera gathers images at high frequency during the hours of daylight. The fast evolution of low-cost hemispherical sky cameras has been preferred due to their application in gathering cloud information [4], and they have become very popular in the fields of solar energy and cloud motion detection. The sky camera is able to produce cloud cover information by image processing from pixel distribution. The data are calculated based on an algorithm that applies the red, green, and blue (RGB) channels of an image and combines them with an adaptive threshold in order to distinguish the cloud and sky pixels. Besides, this data can be combined with physical models to estimate solar irradiance. For example, [PERSON] et al. [5] used cloud cover to estimate solar irradiance in South Korea using the cloud cover radiation model. This model is a regression-type model that was developed by [PERSON] et al. [6] to estimate GHI on an hourly basis. Furthermore, some researchers have also used sky images to forecast solar irradiance in short-term forecast horizons. [PERSON] et al. [7] forecasted GHI using sky images and real-time GHI measurements. They applied the cloud correlation model (CCM) to these images to predict GHI up to 10 min in advance. These results indicate that the proposed model was able to predict GHI in a very short-term forecast horizon under high variability of solar irradiance. So far, the GHI forecasting using sky images was calculated using conventional methods, such as CCM and cloud motion vector (CMV). They estimated the cloud motion by calculating the motion of the pixels on images. However, these methods did not provide good accuracy because the cloud motion is not linear and hard to predict. Meanwhile, deep learning, which is the subpart of artificial intelligence (AI), has been increasingly used in solar irradiance and PV power forecasting [8; 9; 10; 11].