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Formulation and Data-Driven Optimization for Maximizing the Photovoltaic Power with Tilt Angle Adjustment

Author

Listed:
  • Wenrui Ye

    (Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Münür Sacit Herdem

    (Department of Mechanical Engineering, Adiyaman University, Adıyaman 02030, Turkey)

  • Joey Z. Li

    (Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Jatin Nathwani

    (Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • John Z. Wen

    (Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

Abstract

This paper reports on how the trade-off between the incident solar irradiance and conversion efficiency of a photovoltaic panel affects its power production. A neural network was developed through statistical analysis and a data-driven approach to accurately calculate the photovoltaic panel’s power output. Although the incident beam irradiance at a specified location directly relates to the tilt angle, the diffusion irradiance and energy conversion efficiency are nonlinearly dependent on a number of operating parameters, including cell temperature, wind speed, humidity, etc. A mathematical model was implemented to examine and cross-validate the physics of the neural network. Through simulation and comparison of the optimized results for different time horizons, it was found that hourly optimization can increase the energy generated from the photovoltaic panel by up to 42.07%. Additionally, compared to the base scenario, annually, monthly, and hourly optimization can result in 9.7%, 12.74%, and 24.78% more power, respectively. This study confirms the data-driven approach is an effective tool for optimizing solar power. It recommends adjusting the tilt angle of photovoltaic panels hourly, during the daily operation of maximizing the energy output and reducing solar costs.

Suggested Citation

  • Wenrui Ye & Münür Sacit Herdem & Joey Z. Li & Jatin Nathwani & John Z. Wen, 2022. "Formulation and Data-Driven Optimization for Maximizing the Photovoltaic Power with Tilt Angle Adjustment," Energies, MDPI, vol. 15(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8578-:d:974486
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    References listed on IDEAS

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    1. Ali S. Alghamdi, 2021. "Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio," Energies, MDPI, vol. 14(6), pages 1-18, March.
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