Energy Pipeline

Energy pipelines, encompassing both physical infrastructure and data processing workflows, are undergoing significant advancements driven by AI and machine learning. Research focuses on improving pipeline safety through intelligent sensing and anomaly detection, often employing deep learning architectures like convolutional neural networks and transformers for real-time threat assessment and predictive maintenance. Furthermore, automated machine learning (AutoML) is streamlining the development of complex analytical pipelines for various applications, from optimizing large-scale model training to enhancing time series forecasting accuracy in diverse fields like agriculture and energy management. These improvements contribute to increased efficiency, reliability, and safety across numerous sectors.