Abstract
The topographical nature of the Sultanate of Oman makes the solar power system a viable and reliable option for bulk power production in the renewable energy market. Many desert areas of Oman experience high levels of solar radiation. This is suitable for photovoltaic (PV) systems as their efficiency mainly depends on solar radiation. However, in real-time applications, many environmental factors affect the efficiency of the solar panel and therefore its performance. In this article, the Multilayer Feed Forward Neural Network (MFFN) is proposed to track the solar PV system performance in order to replace or improve the performance of the solar PV system based on its current state. A backpropagation algorithm (BPA) is used to train the MFFN.
Keywords
Back propagation algorithm, Multi-layer Feed Forward network, Photovoltaic system, Renewable energy, Solar power system
Article Type
Special Issue Article
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite this Article
Kumaravel, G.; Kirthiga, S.; Al Shekaili, Mohammed Mahmood Hamed; and AL Othmani, Qais Hamed Saif Abdullah
(2024)
"A Solar Photovoltaic Performance Monitoring and Statistical Forecasting Model Using a Multi-Layer Feed-Forward Neural Network and Artificial Intelligence,"
Baghdad Science Journal: Vol. 21:
Iss.
5, Article 35.
DOI: https://doi.org/10.21123/bsj.2024.10736
