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All Journal International Journal of Robotics and Control Systems Control Systems and Optimization Letters
Karthick, K.
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Predictive Modeling of Energy Consumption in the Steel Industry Using CatBoost Regression: A Data-Driven Approach for Sustainable Energy Management Karthick, K.; Dharmaprakash, R.; Sathya, S.
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1234

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.
Comprehensive Overview of Optimization Techniques in Machine Learning Training Karthick, K.
Control Systems and Optimization Letters Vol 2, No 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i1.69

Abstract

This article offers a comprehensive overview of optimization techniques employed in training machine learning (ML) models. Machine learning, a subset of artificial intelligence, employs statistical methods to enable systems to learn and improve from experience without explicit programming. The paper delineates the significance of optimization in ML, emphasizing its role in adjusting model parameters to minimize loss functions, thereby ensuring efficient model training and improved generalization. The discussion encompasses various optimization methods, including Gradient Descent Variants, Adaptive Learning Rate Methods, Second-Order Optimization Methods, Regularization Methods, Constraint-based Methods, and Bayesian Optimization. Each section elucidates the principles, applications, and benefits of these techniques, highlighting their relevance in addressing challenges such as overfitting, scalability, and computational efficiency. The article aims to guide researchers, practitioners, and enthusiasts in navigating the complex landscape of optimization techniques tailored for diverse machine learning algorithms and applications.
Co-Authors Dharmaprakash, R. Sathya, S.