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http://dx.doi.org/10.7471/ikeee.2019.23.1.334

Development of Photovoltaic Output Power Prediction System using OR-AND Structured Fuzzy Neural Networks  

Kim, Haemaro (Dept. of Electrical Engineering, Dong-Eui University)
Han, Chang-Wook (Dept. of Electrical Engineering, Dong-Eui University)
Lee, Don-Kyu (Dept. of Electrical Engineering, Dong-Eui University)
Publication Information
Journal of IKEEE / v.23, no.1, 2019 , pp. 334-337 More about this Journal
Abstract
In response to the increasing demand for energy, research and development of next-generation energy is actively carried out around the world to replace fossil fuels. Among them, the specific gravity of solar power generation systems using infinity and pollution-free solar energy is increasing. However, solar power generation is so different from solar energy that it is difficult to provide stable power and the power production itself depends on the solar energy by region. To solve these problems in this paper, we have collected meteorological data such as actual regional solar irradiance, precipitation, temperature and humidity, and proposed a solar power output prediction system using logic-based fuzzy Neural Network.
Keywords
logic-based fuzzy neural networks; genetic algorithm; photovoltaic system; prediction; OR-AND;
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