Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
Machine Learning-Based Approach to Optimizing the Performance of a Solar Laptop Charger
Asogwa Tochukwu Chijindu
24 October 2025 · Vol. 1, Issue 2, pp. 49-54
DOI: 10.65150/EP-gjetr/V1E2/2025-02
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What is this article about?
The increasing reliance on portable electronic devices and the growing demand for sustainable energy solutions have underscored the need for efficient solar-powered charging systems. This study presents a machine learning-based approach to optimizing the performance of a solar laptop charger by employing a Long Short-Term Memory (LSTM) neural network.
Article at a glance
- Published
- 24 October 2025
- Volume / issue / pages
- Vol. 1, Issue 2, pp. 49-54
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
The increasing reliance on portable electronic devices and the growing demand for sustainable energy solutions have underscored the need for efficient solar-powered charging systems. This study presents a machine learning-based approach to optimizing the performance of a solar laptop charger by employing a Long Short-Term Memory (LSTM) neural network. The proposed system aims to predict optimal charging voltages in real-time, adapting to fluctuations in solar irradiance and ambient temperature to enhance energy conversion efficiency. Historical solar data comprising environmental and electrical parameters was collected, pre-processed, and used to train and test the LSTM model. Simulation results demonstrated that the model accurately forecasted charging voltages, achieving a high coefficient of determination (R² = 0.976), with low prediction error rates (MAE = 0.227, RMSE = 0.285). Though the system was not physically implemented, the simulation results confirm the effectiveness of the LSTM model as an intelligent alternative to conventional Maximum Power Point Tracking (MPPT) techniques. This study highlights the potential of machine learning in enhancing the performance and adaptability of solar energy systems, offering a scalable and software-driven solution for sustainable energy applications.
Keywords: Solar Charger Optimization, LSTM, Charging Voltage Prediction, Deep Learning, Renewable Energy
Cite this article
Asogwa Tochukwu Chijindu (2025). Machine Learning-Based Approach to Optimizing the Performance of a Solar Laptop Charger. Global Journal of Engineering and Technology Research, 1(2), 49-54. https://doi.org/10.65150/EP-gjetr/V1E2/2025-02
@article{Asogwa2025,
title = {Machine Learning-Based Approach to Optimizing the Performance of a Solar Laptop Charger},
author = {Asogwa Tochukwu Chijindu},
journal = {Global Journal of Engineering and Technology Research},
year = {2025},
volume = {1},
number = {2},
pages = {49-54},
doi = {10.65150/EP-gjetr/V1E2/2025-02},
url = {https://doi.org/10.65150/EP-gjetr/V1E2/2025-02}
}Questions about this article
Who wrote this article?
Asogwa Tochukwu Chijindu.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 1, Issue 2, pages 49-54, published by EP Journals Group on 24 October 2025.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V1E2/2025-02 (https://doi.org/10.65150/EP-gjetr/V1E2/2025-02).
How do I cite this article?
APA: Asogwa Tochukwu Chijindu (2025). Machine Learning-Based Approach to Optimizing the Performance of a Solar Laptop Charger. Global Journal of Engineering and Technology Research, 1(2), 49-54. https://doi.org/10.65150/EP-gjetr/V1E2/2025-02
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