| Issue |
E3S Web Conf.
Volume 581, 2024
Empowering Tomorrow: Clean Energy, Climate Action, and Responsible Production
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/e3sconf/202458101001 | |
| Published online | 21 October 2024 | |
Optimization of Solar Panel Efficiency using Genetic Algorithms
1 Department of EEE, GRIET, Hyderabad, Telangana, India
2 Department of MBA, KG Reddy College of Engineering and Technology, Chilkur(Vil), Moinabad(M), Ranga Reddy(Dist), Hyderabad, 500075,Telangana, India.
3 Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
4 Uttaranchal University, Dehradun - 248007, India
5 Lovely Professional University, Phagwara, Punjab, India,
6 Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103
7 Department of Electronics & Communication Engineering, GLA University, Mathura-281406 (U.P.)
8 Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq
* Corresponding Author: vijaya361@grietcollege.com
Maximizing the efficiency of solar panels is crucial for enhancing the viability of solar energy in both residential and commercial sectors. In this study, we employ Genetic Algorithms (GAs) to optimize various parameters affecting solar panel performance, such as tilt angle, azimuth angle, and environmental conditions like temperature and solar irradiance. We develop a model that simulates the efficiency of solar panels under varying conditions and apply GAs to find the optimal configuration. The results demonstrate a significant improvement in energy output, with optimized parameters yielding up to a 15% increase in solar panel efficiency. This research shows the potential of GAs in solving complex optimization problems in renewable energy systems.
Key words: Solar panel efficiency / Genetic Algorithms / Optimization / Renewable energy / Tilt angle / Azimuth angle
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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