Pioneering Energy Efficiency in Steel Industries Through Weighted SHAP and PFI Methodologies

In the realm of industrial energy management, the steel industry stands as a significant consumer, necessitating innovative approaches to optimize energy usage. This paper presents a comprehensive study leveraging machine learning models to analyze energy consumption data from DAEWOO Steel Co. Ltd in Gwangyang, South Korea. Utilizing datasets encompassing daily, monthly, and annual electricity usage, this research deploys various Naive Bayes models (Gaussian, Multi-nomial’ Complement, Bernoulli) to predict and analyze energy consumption patterns. Key to this study is the use of Permutation Feature Importance (PFI) and SHapley Additive exPlanations (SHAP) for feature importance analysis. With a particular focus on an advanced extension of SHAP that enhances the interpretability of model predictions by introducing a weighting factor based on feature importance derived from the model itself, Weighted SHapley Additive exPlanations (Weighted SHAP). This extension allows for a more nuanced assessment of feature contributions to the energy consumption patterns. By aligning with such advanced data exchange protocols, the comparative analysis of these models, enriched with the sophisticated SHAP methodology, provides valuable insights, contributing to a more efficient and sustainable energy management strategy in the steel industry. This alignment ensures that the energy management solutions proposed can be seamlessly integrated, furthering the advancements in smart manufacturing and Industry 4.0.

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].