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The Influence of Seasonal Cloud Cover, Ambient Temperature and Seasonal Variations in Daylight Hours on the Optimal PV Panel Tilt Angle in the United States

Author

Listed:
  • Essa Alhamer

    (Department of Mechanical and Aerospace Engineering, University of Dayton, Dayton, OH 45469, USA)

  • Addison Grigsby

    (Department of Mechanical and Aerospace Engineering, University of Dayton, Dayton, OH 45469, USA)

  • Rydge Mulford

    (Department of Mechanical and Aerospace Engineering, University of Dayton, Dayton, OH 45469, USA)

Abstract

A variety of variables influence the optimal tilt angle of a PV panel, including the characteristics of the panel, the local seasonal weather variations, the number of daylight hours the panel is exposed to and the ambient temperature of the surroundings. In this study, the optimal PV tilt angle and maximum energy output of PV arrays was calculated for every county in the United States and compared against the practice of setting the PV tilt angle to be equivalent to the latitude angle of the PV geographic location. A PVWatts API, implemented through Python, was used in conjunction with the SciPy optimization package to find the optimal tilt angle for each county using a direct line search algorithm. Most counties (95.8%) showed a difference between the location latitude and the optimal tilt of more than one degree. Many counties showed a deviation of 2–6° lower than the location latitude. The variation of daylight hours had the largest influence on tilt angle and seasonal cloud cover and ambient temperature had varying levels of influence. Generally, winter cloud cover decreased the optimal tilt angle whereas high summer temperatures increased the tilt angle.

Suggested Citation

  • Essa Alhamer & Addison Grigsby & Rydge Mulford, 2022. "The Influence of Seasonal Cloud Cover, Ambient Temperature and Seasonal Variations in Daylight Hours on the Optimal PV Panel Tilt Angle in the United States," Energies, MDPI, vol. 15(20), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7516-:d:940139
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    References listed on IDEAS

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    Cited by:

    1. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.

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