Neural Network Self-Organizing Maps Model for Partitioning PV Solar Power

被引：0
|
作者：
Munshi, Amr [1 ]
机构：
[1] Umm Al Qura Univ, Dept Comp Engn, Mecca, Saudi Arabia
来源：
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 2022年 / 22卷 / 05期
关键词：
Ant colony clustering; electric grid; photovoltaic; solar panel;
D O I：
10.22937/IJCSNS.2022.22.5.1
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The growth in global population and industrialization has led to an increasing demand for electricity. Accordingly, the electricity providers need to increase the electricity generation. Due to the economical and environmental concerns associated with the generation of electricity from fossil fuels. Alternative power recourses that can potentially mitigate the economical and environmental are of interest. Renewable energy resources are promising recourses that can participate in producing power. Among renewable power resources, solar energy is an abundant resource and is currently a field of research interest. Photovoltaic solar power is a promising renewable energy resource. The power output of PV systems is mainly affected by the solar irradiation and ambient temperature. this paper investigates the utilization of machine learning unsupervised neural network techniques that potentially improves the reliability of PV solar power systems during integration into the electrical grid.
引用
收藏
页码：1 / 4
页数：4
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