Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

Abstract EN

This paper presents a solar power modelling method using artificial neural networks (ANNs).

Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power.

Both neural networks have four inputs and one output.

The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power.

The data used in this paper started from January 1, 2006, until December 31, 2010.

The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks.

A mathematical equation is used to estimate the generated power.

At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.