Solar irradiance fluctuates mainly due to clouds. A sky camera offers images with high temporal and spatial resolutions for a specific solar photovoltaic plant. The cloud cover from sky images is suitable for forecasting local fluctuations of solar irradiance and thereby solar power. Because no study applied deep learning for forecasting cloud cover using sky images, this study attempted to apply the long short-term memory algorithm in deep learning. Cloud cover data were collected by image processing of sky images and used for developing the deep learning model to forecast cloud cover 10 minutes ahead. The forecasted cloud cover data were plugged into solar radiation models as input in order to predict global horizontal irradiance. The forecasted results were grouped into three categories based on sky conditions: clear sky, partly cloudy, and overcast sky. By comparison with solar irradiance measurement at a ground station, the proposed model was evaluated. The proposed model outperformed the persistence model under high variability of solar irradiance such as partly cloudy days with relative root mean square differences for 10-minute-ahead forecasting are 25.10% and 39.95%, respectively. Eventually, this study demonstrated that deep learning can forecast the cloud cover from sky images and thereby can be useful for forecasting solar irradiance under high variability.

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Solar irradiance models contribute to mitigating the lack of measurement data at a ground station. Conventionally, the models relied on physical calculations or empirical correlations. Recently, machine learning as a sophisticated statistical method has gained popularity due to its accuracy and potential. While some studies compared machine learning models with other models, a study has not yet been performed that compares them side-by-side to assess their performance using the same datasets in different locations. Therefore, this study aims to evaluate the accuracy of three representative models for estimating solar irradiance using atmospheric variables measurement and cloud amount derived from satellite images as the input parameters. Based on its applicability and performance, this study selected the fast all-sky radiation model for solar applications (FARMS) derived from the radiative transfer approach, the Hammer model that simplified atmospheric correlation, and the long short-term memory (LSTM) model specialized in sequential datasets. Global horizontal irradiance (GHI) data were modeled for five distinct locations in South Korea and compared with hourly measurement data of two years to yield the error metrics. When identical input parameters were used, LSTM outperformed the FARMS and the Hammer model in terms of relative root mean square difference (rRMSD) and relative mean bias difference (rMBD). Training an LSTM model using the input parameters of FARMS, such as ozone, nitrogen, and precipitable water, yielded more accurate results than using the Hammer model. The result shows unbiased and accurate estimation with an rRMSD and rMBD of 23.72% and 0.14%, respectively. Conversely, the FARMS has a faster processing speed and does not require significant data to make a fair estimation.