Enhancing Efficiency and Performance of Photovoltaic Systems through Machine Learning Integration

DOI

10.2991/978-94-6463-496-9_12How to use a DOI?

Keywords

Enhancing Efficiency; Performance; Photovoltaic Systems; Artificial Neural Network; Machine Integration

Abstract

This research examines the possibility of incorporating Machine Learning, a data-driven approach that learns from experience, into photovoltaic (PV) systems to significantly improve their efficiency and performance. Machine Learning offers a powerful and efficient way to solve complex problems by learning patterns and relationships from data. In this study, the authors explore the application of this technique in optimizing critical parameters within PV systems, such as module orientation, tilt angle, and power management. The research presents a comprehensive analysis of Machine Learning’s performance when applied to various photovoltaic systems in varying environmental conditions and load demands. The results demonstrate that the integration of Machine Learning leads to substantial improvements in system efficiency, energy output, and overall performance. This is achieved by optimizing the PV system’s parameters to maximize energy generation while minimizing energy losses and maintaining stability under fluctuating load conditions. Additionally, the paper discusses the advantages of using Machine Learning over traditional optimization techniques, such as its ability to manage problem optimization issues that are nonlinear and non-convex in nature, effectively. The authors also highlight the potential for further research in this area, including the exploration of other data-driven optimization techniques and the development of advanced control strategies for PV systems.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Khaled Belhouchet
AU  - Abderrahim Zemmit
PY  - 2024
DA  - 2024/08/31
TI  - Enhancing Efficiency and Performance of Photovoltaic Systems through Machine Learning Integration
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 144
EP  - 155
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_12
DO  - 10.2991/978-94-6463-496-9_12
ID  - Belhouchet2024
ER  -