Artificial neural network weights-based new formula for solar power plant energy prediction - Salim, Muhammed Sabri; Sabri, Naseer; Dheyab, Ali Abdul Rahman (Edelweiss Applied Science and Technology, 2024)
Renewable energy, particularly solar power, is crucial for national development, but forecasting its electrical power output remains a challenge. Environmental parameters like irradiance, temperature, and wind speed impact photovoltaic systems' power. This research presents a unique approach using artificial neural network’s weights to compute the output power of a photovoltaic system across various operating situations. The study utilized an experimental dataset of 28296 samples to train an artificial neural network (ANN), with the output power of a photovoltaic station serving as the target parameter and irradiance, temperature, and wind speed as the input parameters. Next, utilize the ANN's weights to create a distinct model for predicting the production of electricity. The new formula's results were more accurate than the meteorological service's local measurement data for weather prediction, which showed mean square error, an average absolute percentage deviation, and linear correlation of 0.0592, 0.984%, and 0.9688, respectively. The acquired formula makes these results accessible and usable even in the absence of the relevant ANN software.