Author: Pranay Chimmani (Purdue University) - The manufacturing sector faces mounting pressure to optimize energy consumption, reduce costs, and meet sustainability goals. Traditional energy management relies heavily on manual audits, data modeling, and analysis, which often lack adaptability to dynamic industrial environments. Recent advances in Artificial Intelligence (AI), particularly Large Language Models (LLMs) present a transformative opportunity to enhance energy management by enabling real time, data-driven, and context-aware decision-making. This paper explores how Artificial Intelligence (AI), combined with Large Language Models (LLMs), can redefine energy management in manufacturing by positioning AI as a next-generation energy consultant. AI-powered predictive analytics can aid in uncovering inefficiencies, forecast energy demand, and optimize load balancing across industrial processes. LLMs can leverage Retrieval-Augmented Generation (RAG) to interpret complex instructed data such as technical documents, integrate heterogeneous data sources, and generate actionable insights tailored to different stakeholders. By leveraging natural language, LLMs can act as consultants to translate sophisticated energy models into intuitive recommendations, improving decision support for operations. Personalized recommendations are something which traditional methods often lack, LLMs integrated with Digital Twins can simulate different energy scenarios to assess trade-offs and recommend sustainable practices tailored to specific manufacturing contexts. The use of AI as an energy consultant also opens pathways for continuous monitoring and optimization, moving beyond static recommendations to dynamic systems. Ultimately, AI augmented energy consulting empowers manufacturers to not only achieve compliance and efficiency targets but also drive innovation in sustainable operations, positioning them competitively in a global competitive market
