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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
M. Bhoopathi
Chennai Institute of Technology
Control & Systems Engineering Electrical & Electronics Engineering

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Comparative analysis of multi-output machine learning models for solar irradiance and wind speed forecasting: A case study in Tamil Nadu, India S. Selvi; N. Shanti; Lakshmi Dhandapani; M. Bhoopathi; T. Sathish Kumar; P. Kavitha
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp786-796

Abstract

The growing share of wind and solar energy has created challenges in electrical networks, mainly due to intermittency, fluctuations, and uncertainty. These issues affect power system stability, grid operations, and the balance between supply and demand. To address this, accurate prediction of solar irradiance and wind speed is critical for integrating renewable energy into power systems. In this study, we propose a multi-output machine learning approach to predict both global horizontal irradiance (GHI) and wind speed simultaneously. The study uses historical meteorological data obtained from the National Solar Radiation Database (NSRDB) for Tamil Nadu, India. Six regression algorithms: linear regression, gradient boosting, random Forest, extreme gradient boosting (XGB), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) are tested under identical conditions. Model hyperparameters were tuned using GridSearchCV and Bayesian optimization to ensure robust performance. Before modeling, a comprehensive statistical analysis, including input feature distribution and correlation analysis, was conducted. Model accuracy was evaluated using RMSE, MAE, and R² metrics on both training and testing datasets. The results showed that ensemble tree-based methods outperformed the baseline linear model. Among them, CatBoost produced the best results for GHI prediction, while random forest delivered the most reliable wind speed forecasts, demonstrating strong predictive capability for renewable energy applications.
Accurate RUL prediction of EV batteries using random forest and ensemble learning frameworks Ponkumar Ganesapandiyan; P. Hemachandu; N. Rajavinu; M. Bhoopathi; P. Kavitha; R. Kalaivani
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2291-2300

Abstract

Precise remaining useful life (RUL) estimation for lithium-ion batteries is essential for improving the safety, reliability, and maintenance of electric vehicles (EVs). This study proposes a random forest (RF)-based ensemble learning framework using the publicly available Hawaii Natural Energy Institute (HNEI) dataset containing 15,064 charge-discharge cycles. Seven degradation-related features, including cycle index, discharge time, voltage decrement, maximum discharge voltage, minimum charging voltage, time at 4.15 V, and constant-current charging duration, are extracted to characterize battery aging. The proposed RF model is compared with linear regression (LR), long short-term memory (LSTM), and attention-LSTM models using MAE, root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²). RF demonstrates superior prediction performance, achieving MAE of 5.20, RMSE of 6.83, MAPE of 1.33%, and R² of 0.997. Parity and residual analyses further confirm its strong predictive consistency. The proposed approach provides an accurate, computationally efficient, and interpretable solution for BMS applications, enabling effective battery health monitoring, predictive maintenance, charging optimization, and timely replacement.
A privacy-preserving IoT-machine learning framework for optimized and secure demand-side management in smart grids S. Pushpa; Jonnadula Narasimharao; K. L. Kishore; T. Sathish Kumar; M. Bhoopathi; R. Kalaivani
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2004-2012

Abstract

This paper presents an integrated internet of things (IoT) and machine learning-based framework for secure and efficient demand-side management (DSM) in modern smart grids. The proposed approach combines long short-term memory (LSTM) networks for accurate load forecasting, federated learning (FL) for decentralized privacy-preserving model training, and blockchain technology for secure and tamper-proof communication. In addition, a digital twin (DT)-assisted architecture is incorporated to enable predictive decision-making and system-level optimization. The framework explicitly considers renewable energy integration, electric vehicle (EV) charging loads, distributed energy storage, and power electronic converter constraints. Simulation results demonstrate a reduction in forecasting error by 15-25%, a 25% decrease in daily energy cost, and a 23.7% reduction in peak demand. The proposed system achieves improved voltage stability with reduced deviation and enhances overall efficiency up to 88%. Furthermore, cybersecurity performance is validated with an anomaly detection AUC of 0.95 and reduced data transmission through FL by 87%. The results confirm that the proposed framework provides a scalable, secure, and intelligent solution for next-generation smart grid applications.
Co-Authors Jonnadula Narasimharao K. L. Kishore Lakshmi Dhandapani N. Rajavinu N. Shanti P. Hemachandu P. Kavitha Ponkumar Ganesapandiyan R. Kalaivani S. Pushpa S. Selvi T. Sathish Kumar T. Sathish Kumar