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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Energy Res. | doi: 10.3389/fenrg.2019.00130

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

  • 1School of Applied Technical Sciences, Department of Mechanical and Maintenance Engineering, German jordanian university, Jordan
  • 2Faculty of Engineering, Mechanical Engineering Department, University of Jordan, Jordan
  • 3Renewable Energy Center, Applied Science Private University, Jordan
  • 4National School of Applied Sciences, Abdelmalek Essaadi University, Morocco

The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate one day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kWac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15%, 1%, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) performance metrics, respectively, compared to the Persistence prediction model of literature.

Keywords: Solar photovaltaic, Power prediction, artificial neural netwok, learning algorithms, training datasets, Persistence

Received: 17 Jul 2019; Accepted: 30 Oct 2019.

Copyright: © 2019 Al-Dahidi, Ayadi, Adeeb and Louzazni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Sameer Al-Dahidi, German jordanian university, School of Applied Technical Sciences, Department of Mechanical and Maintenance Engineering, Amman, Jordan, sameer.aldahidi@gju.edu.jo