# Cloud Detection and Tracking Based on Object Detection with Convolutional Neural Networks
## 4 Conclusions and Future Work  
As mentioned above, knowing the estimated time for a transient in the available solar radiation is key to the proper operation of solar technologies and the grid, since, with this information, it is possible to activate storage systems to supply the transient and keep the grid stable. Therefore, the prediction of transients is an increasingly demanded feature for solar technologies, since this feature makes the solar technologies more competitive and attractive. The method proposed in this paper is not only able to provide the required information (cloud detection and transient remaining time), but it is also capable of being implemented in low-cost systems in a simple and robust way. In this work, with the exception of retraining, which was carried out in CETA facilities, all the processing was performed with low-cost hardware (object detection model inference, trajectory, and remaining time estimation). This shows the feasibility of implementing this methodology on low-cost hardware. Thanks to this, a large number of sensors can be deployed over large solar fields, increasing the precision of the system without a huge financial investment. In intelligent tracking systems such as Hel-IoT, the new cloud detection approach can be implemented in existing hardware together with the tracking system. As a result, the implementation of this new approach does not imply additional costs. The methodology is based on computer vision and a CNN. It uses a technique known as object detection and an algorithm to compute the trajectory together with the remaining time for a transient to occur. The model presented produced accurate object detection results, both in detection and location estimations during the test, as expected from the accurate results obtained in the retraining and validation process. Other available models can be used if a reduction in the consumption of computational resources or an increase in accuracy is desired. The generation of the dataset for the retraining and the subsequent datasets for successive training was carried out using the Hel-IoT web server. This tool, which was developed for this purpose, proved to be of great help. Despite the low number of images in the dataset compared to others, the results of the retraining and test were sufficient thanks to the transfer learning and data augmentation techniques used. However, the dataset needs to be improved and expanded despite it proving sufficient for the first test of the methodology. The prediction results regarding the trajectory and remaining time were in good agreement with the real values in the test. Further periodic tests are planned. Therefore, it can be assured that this first test was a success and the approach should be further studied and tested. Regarding the model and cloud detection, the test, training, and validation results show highly accurate values. On the other hand, regarding the estimated remaining time, the test result demonstrated that the algorithm for calculating the remaining time works properly given the accuracy of the performance of the model among other factors. In conclusion, the first test of the methodology was positive and showed its great potential and room for improvement; however, the methodology must be tested and validated in more depth. New techniques to improve the approach are already being tested, such as the use of semantic segmentation techniques to obtain greater precision in the estimated time for the transient. Also, a new dataset is being created with different classes of clouds based on the impact they have on solar radiation. With this, an estimate of the reduction in solar radiation caused by a cloud can be obtained, together with the estimated time for the transient event.