Prediction of PV Solar Panel Output Characteristics Using a Multilayer Artificial Neural Network (MLANN)

In this paper, PV solar collector was tested experimentally from 1st July to 31th August 2018 between 7:00 am and 6:00 pm under the weather conditions of Iraq. The PV output power was calculated by using the measured data of voltage and current obtained from experiments. To predict the PV solar outp...

Full description

Saved in:
Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2021-06, Vol.1105 (1), p.12013
Main Authors: Mohammad, Abdulrahman Th, Al-Sagar, Zuhair S., Hussain, Ali Nasser, Al-Tamimi, Majid Khudair Abbas
Format: Article
Language:English
Subjects:
ANN
Artificial neural networks
Back propagation networks
Electric potential
Error Back-Propagation
Irradiance
Model testing
Multilayers
Neural networks
output Power
Photovoltaic cells
PV solar
Solar panels
Voltage
Weather
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, PV solar collector was tested experimentally from 1st July to 31th August 2018 between 7:00 am and 6:00 pm under the weather conditions of Iraq. The PV output power was calculated by using the measured data of voltage and current obtained from experiments. To predict the PV solar output characteristics, four structures of a multilayer artificial neural network MLANN with Error Back-Propagation EBP were designed in MATLAB software. The MLANN structures have two inputs (temperature and irradiance) and three outputs (voltage, current and power). From experiment tests, a dataset of 434 hourly points was collected to investigate the structures of MLANN model. A 70% of the data used for training stage and 30% was distributed between the testing and validating stages. From test stage, the average of output value was taken for 14 numbers of data to compare with experimental values. The MLANN results show that the structures 2-4-4-1, 2-1-1-1 and 2-5-5-1 were the optimum testing model in the voltage, current and power output respectively with high accuracy and good agreement with experimental results.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1105/1/012013