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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Rajavinu, N.
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Control & Systems Engineering Electrical & Electronics Engineering

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Machine learning applications for predicting system production in renewable energy Somasundaram, Deepa; Muthukumar, R.; Rajavinu, N.; Ramaiyan, Kalaivani; Kavitha, P.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1925-1933

Abstract

Renewable energy systems play pivotal role in addressing the global challenge of sustainable energy production. Efficiently harnessing energy from renewable sources requires accurate prediction models to optimize system production. This paper delves into the realm of predictive modeling, focusing on the utilization of machine learning techniques to forecast system production in renewable energy systems. The investigation incorporates a range of factors such as wind speed, sunshine, air pressure, radiation, air temperature, and relative air humidity, alongside temporal data ('Date-Hour (NMT)'). These factors undergo rigorous curation and preprocessing to ensure the reliability and quality of the predictive model. Various machine learning algorithms, including linear regression, decision tree, random forest, and support vector machine (SVM), are employed to examine the relationships between these factors and system production. The findings are assessed using metrics such as mean squared error, mean absolute error, and R-squared. Through comparative analysis, the study illuminates the strengths and limitations of each algorithm, providing valuable insights into their suitability for renewable energy forecasting. This paper adds to renewable energy research by examining how machine learning predicts system production. The insights are valuable for researchers, practitioners, and policymakers in sustainable energy development.
Comparative analysis of optimization techniques for optimal EV charging station placement Somasundaram, Deepa; Prakash, G.; Rajavinu, N.; Lakshmi, D.; Kavitha, P.; Devaraj, V.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2860-2867

Abstract

The optimal placement of electric vehicle (EV) charging stations plays a crucial role in improving accessibility, reducing travel distances, and minimizing infrastructure costs in smart urban planning. This study presents a comparative analysis of traditional optimization techniques-such as linear programming (LP), particle swarm optimization (PSO), k-means clustering, and greedy heuristic methods-alongside a machine learning-based approach using genetic algorithms (GA). A machine learning framework is implemented to simulate EV charging demand, optimize station deployment, and incorporate real-world constraints like cost, grid capacity, and user travel penalties. The results demonstrate that GA achieves superior performance in balancing cost-efficiency and user convenience, outperforming traditional techniques in solution quality under dynamic demand conditions. PSO and LP provide faster convergence but are less adaptive to changing parameters. The study highlights the potential of integrating machine learning into infrastructure planning and provides actionable insights for urban planners and policymakers in developing resilient and intelligent EV charging networks.
Co-Authors Devaraj, V. Kavitha, P. Lakshmi, D. Muthukumar, R. Prakash, G. Ramaiyan, Kalaivani Somasundaram, Deepa