University of Boumerdes

Photovoltaic (PV) technology is one of the most promising forms of renewable energy. However, power generation from PV technologies is highly dependent on variable weather conditions, which are neither constant nor controllable, which can affect grid stability. Accurate forecasting of PV power production is essential to ensure reliable operation within the power system. The primary challenge of this study is to accurately predict photovoltaic energy production, considering that weather conditions, such as irradiance, temperature, and wind speed, are random variables. The key contribution of this article is developing a machine learning model to predict the energy production of a real PV power plant in Algeria. Using real measurements sourced from the Center of Renewable Energy Development (CDER) in Adrar, Algeria, in 2021. The data are from two PV power plants located in harsh desert climate conditions. The results presented in this study offer a comparison of several predictive methods applied to real-world data from a PV power plant situated in the Saharan Region. Our findings reveal that the artificial neural network (ANN) model yields the most accurate predictions of 94.96%, with the smallest prediction error: root mean square (RMSE) and mean absolute error (MAE) are 7.78% and 3.80%, respectively.

Numerous techniques, especially those based on deep learning (DL), have been developed and applied to a wide range of tasks, including image recognition, classification, object detection, and image segmentation, as a result of extensive research in the field of image processing. Image processing has become crucial in the medical field, with segmentation emerging as a crucial method for organ identification, disease detection, and abnormality analysis in medical images. Convolutional neural networks (CNNs), one of the many approaches, have recently demonstrated great promise in resolving intricate problems associated with medical image analysis because of their capacity to automatically learn hierarchical features. In this review, we discuss recent developments in deep CNNs for medical image segmentation. The architectures and features of the most popular CNN-based models are examined, along with the different publicly accessible medical imaging datasets that are used in studies and the evaluation metrics that are frequently used to gauge segmentation performance and accuracy, also the advantages and disadvantages of each one. In addition, we look at comparative research and the shortcomings of existing approaches, offering suggestions for future lines of clinical relevance.

This paper studies the Job Shop Scheduling Problem (JSSP) with Unit Processing Times evolving in an uncertain environment. Our aim is to determine a set of solutions instead of a unique solution in order to manage unanticipated events. To achieve this, an approach integrating two techniques is presented to achieve sequential flexibility. In the first time, the decomposition of the initial problem into sub-problems is exploited for producing a dominant schedule. Then, based on the Group Sequence (GS) method, a set of schedules is constructed, allowing for sequential flexibility with an evaluation of performance. The present work also develops a new lower bound for the GS and compares it with an existing lower bound from the literature. Using benchmark data sets, computational studies demonstrate that the suggested lower bound significantly outperforms the established lower bound found in the literature. In addition, our approach succeeded in finding a set of solutions for all tested benchmarks within a reasonable amount of CPU time. Moreover, two interesting flexibility indicators are presented to evaluate the efficacy of our approach.