Analysis of the Effects of Cell Temperature on the Predictability of the Solar Photovoltaic Power Production

Authors

  • Sameer Al-Dahidi Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan http://orcid.org/0000-0002-7745-7784
  • Salah Al-Nazer
  • Osama Ayadi
  • Shuruq Shawish
  • Nahed Omran

Abstract

The use of intermittent power supplies, such as solar energy, has posed a complex conundrum when it comes to the prediction of the next days' supply. There have been several approaches developed to predict the power production using Machine Learning methods, such as Artificial Neural Networks (ANNs). In this work, we propose the use of weather variables, such as ambient temperature, solar irradiation, and wind speed, collected from a weather station of a Photovoltaic (PV) system located in Amman, Jordan. The objective is to substitute the aforementioned ambient temperature with the more realistic PV cell temperature with a desire of achieving better prediction results. To this aim, ten physics-based models have been investigated to determine the cell temperature, and those models have been validated using measured PV cell temperatures by computing the Root Mean Square Error (RMSE). Then, the model with the lowest RMSE has been adopted in training a data-driven prediction model. The proposed prediction model is to use an ANN compared to the well-known benchmark model from the literature, i.e., Multiple Linear Regression (MLR). The results obtained, using standard performance metrics, have displayed the importance of considering the cell temperature when predicting the PV power output.  

Keywords: Renewable Energy, Photovoltaic, Prediction, Cell temperature, Multiple Linear Regression, Artificial Neural Networks.

JEL Classifications: C53, Q47.

DOI: https://doi.org/10.32479/ijeep.9533

Downloads

Download data is not yet available.

Author Biography

Sameer Al-Dahidi, Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan

Sameer Al-Dahidi was born in Kuwait in 1986. He received the B.Sc. degree (with honors) in Electrical Engineering from The Hashemite University, Zarqa, Jordan, in 2008, the M.Sc. degree in Nuclear Energy (Operations Specialty) from Ecole Centrale Paris and Université Paris-Sud 11, Paris, France in 2012, and the Ph.D. degree (with honors) in Energetic and Nuclear Science and Technology from Politecnico di Milano, Milan, Italy in 2016.He is currently an Assistant Professor at the Mechanical and Maintenance Engineering Department, School of Applied Technical Sciences at the German Jordanian University, Amman, Jordan since February 2018. His current research interests include the development of analytics and models for Prognostics and Health Management (PHM), operation, maintenance and Reliability, Availability, Maintainability, and Safety (RAMS) analysis of engineering systems, and the development of Artificial Intelligence (AI)-based methods for renewable energy production prediction. In addition, he has interests in renewable energy systems and mechanical engineering fields such as thermal sciences, Heating, Ventilation and Air-Conditioning (HVAC), and others. Dr. Al-Dahidi has published several articles in high quality Journals, such as Applied Soft Computing, Expert Systems with Applications, Advances in Mechanical Engineering, International Journal of Prognostics and Health Management, Reliability Engineering & System Safety, Energies, and Entropy.

Downloads

Published

2020-08-10

How to Cite

Al-Dahidi, S., Al-Nazer, S., Ayadi, O., Shawish, S., & Omran, N. (2020). Analysis of the Effects of Cell Temperature on the Predictability of the Solar Photovoltaic Power Production. International Journal of Energy Economics and Policy, 10(5), 208–219. Retrieved from https://www.econjournals.com/index.php/ijeep/article/view/9533

Issue

Section

Articles