Large Language Model-Driven Innovations in Power Engineering Design and Their Influence on System Efficiency

This research looks at the shortcomings of previous approaches to power engineering design that do not address the emerging issues of increased energy systems' complexity and the need for faster data processing. The principal issue is design cycles and maintenance, which affects overall system effectiveness and durability. The solution includes large language models (LLMs) to process large volumes of data and improve designs and AI for decisionmaking. Compared to the traditional approaches, the LLM-driven approaches enhance the accuracy of predictive maintenance, effective use of resources, and the rate of decision-making. Its practicability is proved through existing practices and assessments of organizational cases, which present higher system productivity, less time on failure, and cost optimality. The studies presented here show that LLMs are crucial in dealing with urgent issues in the energy industry.