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Improving Module Temperature Prediction Models for Floating Photovoltaic Systems: Analytical Insights from Operational Data

Author

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  • Monica Nicola

    (Fraunhofer Institute for Solar Energy Systems, 79110 Freiburg, Germany)

  • Matthew Berwind

    (Fraunhofer Institute for Solar Energy Systems, 79110 Freiburg, Germany)

Abstract

Floating photovoltaic (FPV) systems are gaining popularity as a valuable means of harnessing solar energy on unused water surfaces. However, a significant gap persists in our comprehension of their thermal dynamics and the purported cooling benefits they provide. The lack of comprehensive monitoring data across different climatic regions and topographies aggravates this uncertainty. This paper reviews the applicability of established module temperature prediction models, originally developed for land-based PV systems, to FPVs. It then details the refinement of these models using FPV-specific data and their subsequent validation through large-scale, ongoing FPV projects. The result is a significant improvement in the accuracy of temperature predictions, as evidenced by the reduced Mean Absolute Error (MAE) and improved R-squared ( R 2 ) after parameter optimisation. This reduction means that the tailored models better reflect the distinct environmental influences and cooling processes characteristic of FPV systems. The results not only confirm the success of the proposed method in refining the accuracy of current models, but also indicate significant post-tuning changes in the parameters representing wind and convective effects. These adjustments highlight the increased responsiveness of FPVs to convective actions, especially when compared to ground-based systems, possibly due to the evaporative cooling effect of bodies of water. Through this research, we address a critical gap in our understanding of heat transfer in FPV systems and aim to enrich the knowledge surrounding the acknowledged cooling effect of FPVs.

Suggested Citation

  • Monica Nicola & Matthew Berwind, 2024. "Improving Module Temperature Prediction Models for Floating Photovoltaic Systems: Analytical Insights from Operational Data," Energies, MDPI, vol. 17(17), pages 1-17, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4289-:d:1465351
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    References listed on IDEAS

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    1. Socrates Kaplanis & Eleni Kaplani & John K. Kaldellis, 2023. "PV Temperature Prediction Incorporating the Effect of Humidity and Cooling Due to Seawater Flow and Evaporation on Modules Simulating Floating PV Conditions," Energies, MDPI, vol. 16(12), pages 1-19, June.
    2. Maarten Dörenkämper & Minne M. de Jong & Jan Kroon & Vilde Stueland Nysted & Josefine Selj & Torunn Kjeldstad, 2023. "Modeled and Measured Operating Temperatures of Floating PV Modules: A Comparison," Energies, MDPI, vol. 16(20), pages 1-18, October.
    3. KERAMIDAS Kimon & FOSSE Florian & DIAZ RINCON Andrea & DOWLING Paul & GARAFFA Rafael & ORDONEZ Jose & RUSS Peter & SCHADE Burkhard & SCHMITZ Andreas & SORIA RAMIREZ Antonio & VANDYCK Toon & WEITZEL Ma, 2022. "Global Energy and Climate Outlook 2022: Energy trade in a decarbonised world," JRC Research Reports JRC131864, Joint Research Centre.
    4. Mattei, M. & Notton, G. & Cristofari, C. & Muselli, M. & Poggi, P., 2006. "Calculation of the polycrystalline PV module temperature using a simple method of energy balance," Renewable Energy, Elsevier, vol. 31(4), pages 553-567.
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