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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Robotics and Control Systems
Dharmaprakash, R.
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Control & Systems Engineering Electrical & Electronics Engineering

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Investigation on implementing the swarm nano grid system for effective utilization of solar-powered agro-industries Karthikeyan, N.; Nanthagopal, S.; Dharmaprakash, R.; Ravikumar, R.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp1128-1136

Abstract

The agro-industry is the backbone of the global economy, even in the twenty-first century. The agro-industry would not be what it is now without irrigation. The production and use of renewable energy in this sector of the agricultural economy have also expanded rapidly in recent years. Base-load power production and conversions dominate the literature. This research examines user concerns. The swarm nano grid system fixes this. The pulse width modulation (PWM) sinusoidal inverter converted stable DC power from the bidirectional DC-DC converter into sinusoidal AC voltage for the irrigation pump induction motor. Solar panels, batteries, and converters are costly, but they pay off. The nano grid distributes excess power generated during low demand to local loads. This technique works well when the load can be disconnected from the power grid. MATLAB is used to keep an eye on the reliability and efficiency of the induction motor. In the first simulation, solar power generation is modeled using the MATLAB Simulink software in two distinct modes. A PWM sinusoidal inverter that is driven by solar energy is what provides power to the 5.67 kW induction submersible motor. The simulation result provides a conceptual model of how induction motors powered by renewable energy sources function in practice.
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.
Co-Authors Karthick, K. Karthikeyan, N. Nanthagopal, S. Ravikumar, R. Sathya, S.