Cloud Detection and Tracking Based on Object Detection with Convolutional Neural Networks

• Published in: Algorithms Vol. 16; no. 10; p. 487
• Main Authors: Carballo, Jose Antonio, Bonilla, Javier, Fernández-Reche, Jesús, Nouri, Bijan, Avila-Marin, Antonio, Fabel, Yann, Alarcón-Padilla, Diego-César
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2023
• Subjects: Accuracy; Algorithms; Alternative energy sources; Analysis; Artificial intelligence; Artificial neural networks; Availability; Cameras; central receiver system; Cloud computing; Computer vision; Deep learning; Electricity distribution; Machine vision; Methods; neural network; Neural networks; nowcasting; Object recognition; Power; Radiation; Semantics; Solar energy; Solar radiation; Sun
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a16100487

Due to the need to know the availability of solar resources for the solar renewable technologies in advance, this paper presents a new methodology based on computer vision and the object detection technique that uses convolutional neural networks (EfficientDet-D2 model) to detect clouds in image series. This methodology also calculates the speed and direction of cloud motion, which allows the prediction of transients in the available solar radiation due to clouds. The convolutional neural network model retraining and validation process finished successfully, which gave accurate cloud detection results in the test. Also, during the test, the estimation of the remaining time for a transient due to a cloud was accurate, mainly due to the precise cloud detection and the accuracy of the remaining time algorithm.