I. Introduction
The steel industry is a cornerstone of modern industrial-ization, known for its intensive energy consumption. As a significant consumer of energy, the industry faces ongoing challenges in managing and optimizing its energy usage [1]. The relevance of energy optimization in this sector is underscored by a combination of environmental, economic, and technological factors [2]. This paper presents a novel approach to addressing these challenges through the application of machine learning models to analyze energy consumption data from DAEWOO Steel Co. Ltd in Gwangyang, South Korea [3].