Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Devaraj, Tintu Beena
Unknown Affiliation
Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Global solar energy estimation using improved greedy based genetic algorithm with deep convolutional neural network Devaraj, Tintu Beena; Kottoor, Charu Navaneeth Raveendran
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i1.pp633-641

Abstract

Demand for solar energy increases and it is required to manage the supply of energy effectively. Accurate detection on patterns of energy consumed assist in taking appropriate decisions on generating energy. Even though many traditional techniques have predicted the consumption rate, still improvement is needed in prediction accuracy. The pre-processing is performed initially for handling missing values. The feature selection is accomplished using improved greedy based genetic algorithm (GGA) to extract best features to enhance the performance of the model. Output from feature-selection is passed as input to the classification phase using proposed deep convolutional neural network (CNN) in which future solar energy patterns are classified and predicted timely basis power consumption and it optimize the model by minimize the error. The prediction accuracy is estimated through evaluation metrics such as mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) 0.423, 0.652, and 0.215, respectively. The outcomes achieved in terms of accuracy at 99.75, precision at 99.28, sensitivity, and recall at 100 revealed the efficiency of the proposed classification model. As a result, the proposed future prediction of solar energy was considered efficient since it achieved reduced error values than other prediction algorithms. It assists in maintaining stability in solar-energy based systems.
Co-Authors Kottoor, Charu Navaneeth Raveendran