Prediction of Solar Irradiance Based on Artificial Neural Networks

Waleed I Hameed,Raed A Abd-Alhameed,Mohammed S Al-Radhi,Baha A Sawadi,Safa J Al-Kamil,Ameer L Saleh,Yasir I A Al-Yasir

doi:10.3390/inventions4030045

Abstract

Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.

Highlights

Among different types of energy sources, photovoltaic (PV) energy is one of the most important renewable energy sources [1]
Module, the input–output data set was extracted using MATLAB Simulink. These data sets were utilized to train the parameters of the artificial neural networks (ANN) using the backpropagation algorithm
The trained neural network (NN) was used to predict the solar irradiance by measuring the open-circuit voltage and the short-circuit current of the PV module

Summary

Introduction

Among different types of energy sources, photovoltaic (PV) energy is one of the most important renewable energy sources [1]. A precise measurement of the module temperature (T) and incident solar irradiance (G) are required to get better performance of PV systems with maximum power point tracking (MPPT). Photo of of aa pyranometer pyranometer [6] Taking these disadvantages into account, there is an urgent and essential need for building a Taking these disadvantages into account, there is an urgent and essential need for building a system to predict the instant value of solar irradiance incident on surfaces. This paper proposes an ANN to predict solar irradiance based on real measurements of I–V curve parameters for a PV module. These essential parameters change with irradiance and PV cell curve parameters for a PV module.

Artificial

Irradiance

Simulation and Measurement Results

Cumulative

Conclusions