Output Power Prediction of a Photovoltaic Module Through Artificial Neural Network

• Published in: IEEE access Vol. 10; pp. 116160 - 116166
• Main Authors: Khan, Muhammad Aseer, Khan, Muhammad Arqum, Ali, Husan, Ashraf, Bilal, Khan, Shahbaz, Baig, Dur-E-Zehra, Wadood, Abdul, Khurshaid, Tahir
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alternative energy sources; Artificial neural network (ANN); Artificial neural networks; Datasets; Energy demand; environmental; Environmental factors; Heuristic methods; Humidity; Immune system; Irradiance; Mathematical models; Neural networks; Neurons; photovoltaic (PV) system; Photovoltaic cells; Photovoltaics; Power distribution; Predictive models; Regression analysis; renewable energy (RE); Renewable energy sources; Silicon; Solar energy; Statistical analysis; Wind speed
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3216384

With the increase in energy demand, renewable energy has become a need of almost every country. Solar Energy is an important constituent of it and contributes a large portion in it. Forecasting the output power of a Photovoltaic (PV) system has always been a challenging problem in the power sector from the last few decades. The output power of a PV system depends upon several environmental factors such as irradiance (G), temperature (T), humidity (H), wind speed (W), provided the tilt angle is kept constant, among which the vital role is played by irradiance. Researchers have utilized several techniques to accurately predict the output power of PV module but every method has various pros and cons. In this paper, an experimental measurement dataset of 28296 samples with all the environmental parameters mentioned above are taken as the inputs and power as its output, of a Poly-Silicon (Poly-Si) PV module, is trained through Artificial Neural Network (ANN), to predict the output power accurately. The proposed ANN contains a layer size of 15 and training algorithm used is Levenberg-Marquardt. A detailed analysis and preprocessing of the data is carried out through Pearson's correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. The data division is done randomly with 70% dataset used for training, 15% dataset used for each validation and testing. The statistical results show that ANN accurately predicted the power output of PV module. The regression analysis values acquired are 98% and the MSE of all the three phases is 0.0604.