Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks

Abstract

Solar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—genetic algorithm, particle swarm optimization, and artificial bee colony—to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony.