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Renewable Energy (RE) is becoming more important and dominant for a cost-effective and sustainable power supply. Due to this, accurate forecasting and efficient grid integration have become even more critical in recent times. This study offers a unique approach at the optimum level by combining Machine-Learning (ML) techniques in RE prediction and grid integration. The key objective of this research is to use the inherent complexity of RE data by exploring various ML approaches. These techniques include Neural Networks (NN), Decision Trees (DT), and Support Vector Machines (SVM). The suggested approach incorporates several methods into an ensemble model that maximizes the advantages of each method to improve accuracy during forecasting. This study also provides curtailments using a grid integration technique that minimizes imbalances and adjusts dynamically to anticipated energy generation. In this research, the simulation findings demonstrate that the combined model performs better than individual ML techniques based on RE data, by achieving the highest accuracy of R² score of 98.9%, as compared with other models such as SVR and DTs of 82.7% and 97.1%, respectively. This method optimizes grid operations and improves prediction accuracy, lowering costs and increasing energy efficiency. This research adds exceptional value to the field of RE by developing a versatile model applicable to various energy sources and integration with the grid. There are various issues while integrating renewable energy with the grid, such as uncertainty, unpredictability, and intermittent. These issues can be resolved by implementing ML techniques. Using ML techniques enables to tackle the significant issues in renewable energy forecasting, facilitating more resilient and sustainable energy. [ABSTRACT FROM AUTHOR]

Autonomous vehicles can transform the transportation sector by offering a safer and more effective means of travel. However, the success of self-driving cars depends on their ability to navigate complex road conditions, including the detection of potholes. Potholes pose a substantial risk to vehicles and passengers, leading to potential damage and safety hazards, making their detection a critical task for autonomous driving. In this work, we propose a robust and efficient solution for pothole detection using the "you look only once (YOLO) algorithm of version 8, the newest deep learning object detection algorithm." Our proposed system employs a deep learning methodology to identify real-time potholes, enabling autonomous vehicles to avoid potential hazards and minimise accident risk. We assess the effectiveness of our system using publicly available datasets and show that it outperforms existing state-of- the-art approaches interms of accuracy and efficiency. Additionally, we investigate different data augmentation methods to enhance the detection capabilities of our proposed system. Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. The results of our study are also compared with the results of YOLO V5. [ABSTRACT FROM AUTHOR]