A Solar Photovoltaic Performance Monitoring and Statistical Forecasting Model Using a Multi-Layer Feed-Forward Neural Network and Artificial Intelligence

Main Article Content

G. Kumaravel
https://orcid.org/0000-0002-0927-5815
S. Kirthiga
Mohammed Mahmood Hamed Al Shekaili
Qais Hamed Saif Abdullah AL Othmani

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.

Article Details

How to Cite
1.
A Solar Photovoltaic Performance Monitoring and Statistical Forecasting Model Using a Multi-Layer Feed-Forward Neural Network and Artificial Intelligence. Baghdad Sci.J [Internet]. 2024 May 25 [cited 2024 Jun. 17];21(5(SI):1868. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10736
Section
Special Issue - (ICCDA) International Conference on Computing and Data Analytics

How to Cite

1.
A Solar Photovoltaic Performance Monitoring and Statistical Forecasting Model Using a Multi-Layer Feed-Forward Neural Network and Artificial Intelligence. Baghdad Sci.J [Internet]. 2024 May 25 [cited 2024 Jun. 17];21(5(SI):1868. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10736

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