Application of Artificial Intelligence for Energy Audit in Industry to Improve the Performance and Energy Efficiency

In manufacturing industries, effective energy management is crucial for lowering production costs and maintaining market competitiveness. Typically, this management is achieved through energy audits, with electrical energy audits specifically targeting the identification and analysis of electrical losses during manufacturing. Through energy audits, this study explores the application of artificial intelligence (AI) in industrial settings to enhance operational performance and energy efficiency. Traditional energy audit methods, while effective, often face limitations in scalability and real-time analysis. By utilizing machine learning algorithms, predictive modeling, and advanced data analytics, artificial intelligence (AI) presents a viable substitute that can yield more profound understanding of energy consumption trends. This research examines various AI-driven techniques and tools that facilitate the identification of inefficiencies, predict energy usage trends, and recommend actionable improvements. Through case studies and empirical data, the paper demonstrates how AIbased audits can optimize energy management practices, reduce operational costs, and contribute to sustainability goals. The findings suggest that integrating AI into energy audits can significantly transform industrial energy practices by providing more accurate, timely, and actionable data.