| Autor: |
Memić, Adin, Dedović, Maja Muftić, Dautbašić, Nedis, Kapo, Medina |
| Zdroj: |
B&H Electrical Engineering; Dec2024, Vol. 18 Issue 2, p1-7, 7p |
| Abstrakt: |
This paper investigates the potential application of neural networks for predicting electricity production in hybrid systems combining photovoltaic (PV) panels and wind turbines. The research focuses on identifying key factors affecting the efficiency and reliability of these systems, including weather variability, PV panel temperature control, solar irradiation, and panel contamination by dust and other pollutants. Artificial neural network (ANN) models are used to predict power output, incorporating robust data filtering and parameter optimization techniques. Through case studies from Germany, the significant role of stochastic weather patterns on energy production is demonstrated, highlighting the need for accurate modeling and strategic management. The findings emphasize that accurate modeling and prediction are crucial for optimizing the operation and reliability of hybrid systems, facilitating a reduced dependency on fossil fuels and promoting sustainable power accessibility in remote areas. By applying a Feed Forward Back Propagation Network (FFBPN), this research demonstrates improved prediction accuracy of power outputs, which is crucial for effective integration and management of renewable sources in the power grid. The study supports ongoing refinement of predictive models and system integration strategies to fully harness the potential of hybrid renewable energy systems. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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