Predictive Modeling of Energy Consumption in the Steel Industry Using CatBoost Regression: A Data-Driven Approach for Sustainable Energy Management

(1) * K. Karthick Mail (GMR Institute of Technology, India)
(2) R. Dharmaprakash Mail (Panimalar Engineering College, India)
(3) S. Sathya Mail (S. A. Engineering College, India)
*corresponding author

Abstract


This article presents a machine learning model for predicting energy consumption in the steel industry, which aids in energy management, cost reduction, environmental regulation compliance, informed decision-making for future energy investments, and contributes to sustainability. The dataset used for the prediction model comprises 11 attributes and 35,040 instances. The CatBoost prediction algorithm was employed for energy consumption prediction, and hyperparameter optimization was performed using GridSearchCV with 5-fold cross-validation. The developed model has undergone a comparative analysis based on both Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics, demonstrating its promise for accurate energy consumption prediction on both the training and test sets. The proposed model accurately predicts energy consumption for different load types, achieving impressive results on both the training set (RMSE=0.382, R2=0.999, MAPE=1.139) and the test set (RMSE=1.073, R2=0.998, MAPE=1.142). These findings highlight the potential of CatBoost as a valuable tool for energy management and conservation, enabling organizations to make informed decisions, optimize resource allocation, and promote sustainability.

Keywords


Energy Consumption; Data Analysis; Predictive Modeling; Machine Learning in Steel Industry; Energy Optimization

   

DOI

https://doi.org/10.31763/ijrcs.v4i1.1234
      

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10.31763/ijrcs.v4i1.1234 Abstract views : 1203 | PDF views : 532

   

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