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

Sameer Al-Dahidi, Salah Al-Nazer, Osama Ayadi, Shuruq Shawish and Nahed Omran
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Sameer Al-Dahidi: Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, Jordan
Salah Al-Nazer: Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, Jordan
Osama Ayadi: Department of Mechanical Engineering, Faculty of Engineering, The University of Jordan, Amman, Jordan,
Shuruq Shawish: Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, Jordan
Nahed Omran: Renewable Energy Center, Applied Science Private University, Amman, Jordan.

International Journal of Energy Economics and Policy, 2020, vol. 10, issue 5, 208-219

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. (search for similar items in EconPapers)
JEL-codes: C53 Q47 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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