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Long-term performance analysis and power prediction of PV technology in the State of Qatar

Author

Listed:
  • Touati, Farid
  • Chowdhury, Noor Alam
  • Benhmed, Kamel
  • San Pedro Gonzales, Antonio J.R.
  • Al-Hitmi, Mohammed A.
  • Benammar, Mohieddine
  • Gastli, Adel
  • Ben-Brahim, Lazhar

Abstract

“Solar photovoltaic (PV) energy in GCC”- the term seems convincing to many solar PV industries due to high solar exposure in GCC region. However, long-term effects such as dust accumulation and seasonal variation are major drawbacks for solar PV energy. This research aims to investigate PV performance for two years in the harsh environment of Qatar. For data collection, a wireless system has been developed to record critical parameters such as solar irradiance, relative humidity, ambient temperature, PV module temperature, dust, wind speed, and output PV power. Results show that due to panel dusting for eight months, the PV output power decreased by 50%. Also, owing to lower ambient temperatures, clearer sky and cleaner panels due to occasional rainfall, the PV panels show higher output power in Winter than in Summer season. Besides, within one-month, a cloudy condition in Winter causes 20% drop in average output power. Therefore, a strategic plan is needed to build and manage efficiently a PV solar plant in harsh environments such as of Qatar. Energy management requires prediction of energy yield. To this end, using machine-learning, a mathematical model has been established which can predict the output power from PV panels under different environmental conditions.

Suggested Citation

  • Touati, Farid & Chowdhury, Noor Alam & Benhmed, Kamel & San Pedro Gonzales, Antonio J.R. & Al-Hitmi, Mohammed A. & Benammar, Mohieddine & Gastli, Adel & Ben-Brahim, Lazhar, 2017. "Long-term performance analysis and power prediction of PV technology in the State of Qatar," Renewable Energy, Elsevier, vol. 113(C), pages 952-965.
    • Handle: RePEc:eee:renene:v:113:y:2017:i:c:p:952-965
    DOI: 10.1016/j.renene.2017.06.078
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    11. Amith Khandakar & Muhammad E. H. Chowdhury & Monzure- Khoda Kazi & Kamel Benhmed & Farid Touati & Mohammed Al-Hitmi & Antonio Jr S. P. Gonzales, 2019. "Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar," Energies, MDPI, vol. 12(14), pages 1-19, July.
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