Open AccessPrediction of PV Solar Panel Output Characteristics Using a Multilayer Artificial Neural Network (MLANN)
Author(s) -
Abdulrahman Th. Mohammad,
Zuhair S. Al-Sagar,
Ali Nasser Hussain,
Majid Khudair Abbas Al-Tamimi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1105/1/012013
Subject(s) - artificial neural network , voltage , matlab , photovoltaic system , solar irradiance , power (physics) , test data , irradiance , computer science , simulation , electronic engineering , engineering , electrical engineering , meteorology , artificial intelligence , optics , physics , quantum mechanics , programming language , operating system
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.