Authors

Collina Emmanuel M

Department of Computer Engineering, University of San Carlos, Philippines

Pantanar Elizabeth S

Department of Computer Engineering, University of San Carlos, Philippines

Lescano Mark Matthew

Department of Electrical and Electronics Engineering, University of San Carlos, Philippines

Abstract

The importance of renewable energy is underscored by the existing energy deficit, which poses significant challenges to the sustainable development of the human population. In this research article, the authors have developed a renewable energy resource optimizer using a simple random forest classifier, achieving an impressive accuracy of 95% during the testing phase. The study analyzes CSV data that includes features such as latitude, longitude, elevation, wind speed, solar radiation, temperature, humidity, existing energy consumption, and available area to predict energy generation and modify consumption accordingly. This research project is specifically focused on predictive analysis to enhance energy optimization. It offers valuable insights into the future potential and limitations of renewable resources. Additionally, the authors have designed a frontend for improved data visualization, incorporating a map component to identify areas where the predicted energy can be effectively utilized.

Keywords

CNN Random forest Machine learning Sustainable development AI Renewable energy

Citation of this Article

Collina Emmanuel M, Pantanar Elizabeth S, & Lescano Mark Matthew. (2025). Review of Energy Generation Optimization Techniques Using AI Models. Current Journal of Engineering and Science Research. 2(2), 17-21. Article DOI: https://doi.org/10.47001/CJESR/2025.202004

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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