| Summary: | Photovoltaic (PV) technology converts solar energy into electricity. It is a sustainable and abundant energy source that offers efficient, clean, and reliable power generation. Numerous researchers have focused on optimizing PV systems to maximize their output. Neural networks, computational models capable of predicting trends, play a significant role in this field. This study introduces the Dolphin Echolocation Algorithm (DEA) for predicting the output power of grid-connected photovoltaic (GCPV) systems and compares different input models to identify the most favorable outcome. The optimization accuracy was evaluated using root mean square error (RMSE) and regression (R value). The primary objectives of this project are to identify the best output results by comparing four input models using the DEA. The obtained results show that the proposed DEA outperforms the basic artificial neural network (ANN) in terms of output performance. © 2024 IEEE.
|
|---|