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Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems

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  • Ben Ammar, Rim
  • Ben Ammar, Mohsen
  • Oualha, Abdelmajid

Abstract

The solar water pumping system is one of the brightest applications of solar energy for its environmental and economic advantages. It consists of a photovoltaic panel which converts solar energy into electrical energy to operate a DC or AC motor and a battery bank. The photovoltaic power fluctuation can affect the water pumping system performances. Thus, the photovoltaic power prediction is very important to ensure a balance between the produced energy and the pump requirements. The prediction of the generated power depends on solar irradiation and ambient temperature forecasting. The purpose of this study was to evaluate various methodologies for weather data estimation namely: the empirical models, the feed forward neural network and the adaptive neuro-fuzzy inference system. The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The predicted energy was used for the solar water pumping management algorithm.

Suggested Citation

  • Ben Ammar, Rim & Ben Ammar, Mohsen & Oualha, Abdelmajid, 2020. "Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems," Renewable Energy, Elsevier, vol. 153(C), pages 1016-1028.
    • Handle: RePEc:eee:renene:v:153:y:2020:i:c:p:1016-1028
    DOI: 10.1016/j.renene.2020.02.065
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    Cited by:

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    3. Bouazza Fekkak & Mustapha Merzouk & Abdallah Kouzou & Ralph Kennel & Mohamed Abdelrahem & Ahmed Zakane & Mostefa Mohamed-Seghir, 2021. "Comparative Study of Experimentally Measured and Calculated Solar Radiations for Two Sites in Algeria," Energies, MDPI, vol. 14(21), pages 1-25, November.
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    6. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.

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