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

Author

Listed:
  • 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.)

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.

Suggested Citation

  • Sameer Al-Dahidi & Salah Al-Nazer & Osama Ayadi & Shuruq Shawish & Nahed Omran, 2020. "Analysis of the Effects of Cell Temperature on the Predictability of the Solar Photovoltaic Power Production," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 208-219.
  • Handle: RePEc:eco:journ2:2020-05-24
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    References listed on IDEAS

    as
    1. Ba, Moustapha & Ramenah, Harry & Tanougast, Camel, 2018. "Forseeing energy photovoltaic output determination by a statistical model using real module temperature in the north east of France," Renewable Energy, Elsevier, vol. 119(C), pages 935-948.
    2. Brunet, Carole & Savadogo, Oumarou & Baptiste, Pierre & Bouchard, Michel A., 2018. "Shedding some light on photovoltaic solar energy in Africa – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 325-342.
    3. Adnan Al-Bashir & Mohamed Al-Dweri & Ahmad Al-Ghandoor & Bashar Hammad & Wael Al-Kouz, 2020. "Analysis of Effects of Solar Irradiance, Cell Temperature and Wind Speed on Photovoltaic Systems Performance," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 353-359.
    4. Sameer Al-Dahidi & Osama Ayadi & Jehad Adeeb & Mohammad Alrbai & Bashar R. Qawasmeh, 2018. "Extreme Learning Machines for Solar Photovoltaic Power Predictions," Energies, MDPI, vol. 11(10), pages 1-18, October.
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    Cited by:

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    More about this item

    Keywords

    Renewable Energy; Photovoltaic; Prediction; Cell temperature; Multiple Linear Regression; Artificial Neural Networks.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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