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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Pushparaj, Sivakumar
Unknown Affiliation
Control & Systems Engineering Electrical & Electronics Engineering

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Simulation of reactive flow over a parabolic vertical plate using MATLAB Pushparaj, Sivakumar; Ramalingam, Balaji; Adhimoolam, Ramesh; Mohan Reddy, P. Venkata; Srinivasan, Andal; Rajamanickam, Muthucumaraswamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1673-1682

Abstract

This article examines how fluid flows around an infinitely large, parabolic-shaped vertical plate, which is heated at an exponentially accelerating rate and undergoes a chemical reaction with the fluid. The plate’s temperature increases at an exponential rate, adding complexity to the heat transfer process. Additionally, the fluid undergoes a chemical reaction in this environment, impacting both the flow and concentration of chemical species. The article includes graphs that show how different parameters such as the rate of temperature increase, strength of thermal radiation, and reaction rate, effect the flow, heat, and concentration profiles. This graphical analysis provides a visual understanding of how each parameter influences the behavior of the fluid.
Predictive machine learning for smart grid demand response and efficiency optimization Vinitha, J. C.; Sumithra, J.; Suganya, M. J.; Dhas, P. Aileen Sonia; Ramalingam, Balaji; Pushparaj, Sivakumar
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i3.pp1628-1636

Abstract

This paper explores the evolution of smart grids (SGs) and how they enable consumers to schedule household appliances based on demand response programs (DRs) provided by distribution system operators (DSOs). This study looks at and compares four distinct regression models: linear regression, random forest regressor, gradient boosting regressor, and support vector regressor. This is being done because more and more people are using machine learning (ML) methods to make this process better. The models are trained and tested using a dataset that includes a variety of parameters, such as humidity, temperature, and the amount of power used by appliances. Mean squared error (MSE) and R-squared values are two important performance measures that are used to judge these models and see how well they can make predictions. These results reveal that the gradient boosting regressor was the most accurate model for figuring out how much energy smart homes use. This algorithm could be a great tool for better managing energy use because it can figure out the complicated connections between the things that are input and the amount of energy that appliances use. This study makes a big difference in the creation of strong regression models by emphasizing how important it is to be accurate when making predictions. This, in turn, helps to enhance energy sustainability and economic stability in smart home environments.
Co-Authors Adhimoolam, Ramesh Dhas, P. Aileen Sonia Mohan Reddy, P. Venkata Rajamanickam, Muthucumaraswamy Ramalingam, Balaji Srinivasan, Andal Suganya, M. J. Sumithra, J. Vinitha, J. C.