Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

• Published in: Energies (Basel) 2024-01, Vol.17 (2), p.438
• Main Authors: Barancsuk, Lilla, Groma, Veronika, Günter, Dalma, Osán, János, Hartmann, Bálint
• Format: Article
• Language: English
• Subjects: Accuracy; Cameras; Clouds; deep learning; Energy industry; Forecasting; Ground stations; Image processing; Machine learning; Machine vision; Methods; Neural networks; Radiation; resource efficiency; Solar energy industry; solar irradiance estimation; Time series
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17020438

In recent years, with the growing proliferation of photovoltaics (PV), accurate nowcasting of PV power has emerged as a challenge. Global horizontal irradiance (GHI), which is a key factor influencing PV power, is known to be highly variable as it is determined by short-term meteorological phenomena, particularly cloud movement. Deep learning and computer vision techniques applied to all-sky imagery are demonstrated to be highly accurate nowcasting methods, as they encode crucial information about the sky’s state. While these methods utilize deep neural network models, such as Convolutional Neural Networks (CNN), and attain high levels of accuracy, the training of image-based deep learning models demands significant computational resources. In this work, we present a computationally economical estimation technique, based on a deep learning model. We utilize both all-sky imagery and meteorological data, however, information on the sky’s state is encoded as a feature vector extracted using traditional image processing methods. We introduce six all-sky image features utilizing detailed knowledge of meteorological and physical phenomena, significantly decreasing the amount of input data and model complexity. We investigate the accuracy of the determined global and diffuse radiation for different combinations of meteorological parameters. The model is evaluated using two years of measurements from an on-site all-sky camera and an adjacent meteorological station. Our findings demonstrate that the model provides comparable accuracy to CNN-based methods, yet at a significantly lower computational cost.