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

被引：0

作者：

Kumaravel, G. ^{[1
]}

Kirthiga, S. ^{[2
]}

Shekaili, Mohammed Mahmood Hamed Al ^{[1
]}

Othmani, Qais Hamed Saif Abdullah A. L. ^{[1
]}

机构：

[1] Univ Technol & Appl Sci, Engn Dept, Ibri, Oman

[2] Un design steel & welding LLC, Elect Div, Dubai, U Arab Emirates

来源：

BAGHDAD SCIENCE JOURNAL | 2024年 / 21卷 / 05期

关键词：

Back propagation algorithm; Multi-layer Feed Forward network; Photovoltaic system; Renewable energy; Solar power system;

D O I：

10.21123/bsj.2024.10736

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：1868 / 1877

页数：10

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