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Hourly PV production estimation by means of an exportable multiple linear regression model

Author

Listed:
  • Trigo-González, Mauricio
  • Batlles, F.J.
  • Alonso-Montesinos, Joaquín
  • Ferrada, Pablo
  • del Sagrado, J.
  • Martínez-Durbán, M.
  • Cortés, Marcelo
  • Portillo, Carlos
  • Marzo, Aitor

Abstract

The current state of photovoltaic (PV) electricity integration is demanding several strategies that control the optimal performance of PV plants. Cleaning the PV plant, controlling PV production or the estimation of the electricity generation, are some relevant items related to the PV systems. In general, the soiling, the clouds and another climatological factorsare involved in the final PV production. For knowing the performance of a PV system, it is necessity to model the PV plant behavior according to these relevant variables. In this work, a Multiple Linear Regression (MLR) model has been presented to determine the hourly PV production by using the Performance Ratio (PR) factor, according to different technologies: Cadmium Telluride (CdTe) and multicrystallinesilicon (mc-Si). In this sense, data from several PV plants were studied in different Chile regions: San Pedro de Atacama and Antofagasta. With this study, it has been determined that the model can be extrapolated to different climatological emplacements, where generally, the root mean square error (RMSE) presents values lower than 16% in all cases, having the best result the CdTetechnology.

Suggested Citation

  • Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    • Handle: RePEc:eee:renene:v:135:y:2019:i:c:p:303-312
    DOI: 10.1016/j.renene.2018.12.014
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    4. Gowtham Vedulla & Anbazhagan Geetha & Ramalingam Senthil, 2022. "Review of Strategies to Mitigate Dust Deposition on Solar Photovoltaic Systems," Energies, MDPI, vol. 16(1), pages 1-28, December.
    5. Fuster-Palop, Enrique & Prades-Gil, Carlos & Masip, X. & Viana-Fons, Joan D. & Payá, Jorge, 2021. "Innovative regression-based methodology to assess the techno-economic performance of photovoltaic installations in urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    6. Vishnu Suresh & Przemyslaw Janik & Jacek Rezmer & Zbigniew Leonowicz, 2020. "Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm," Energies, MDPI, vol. 13(3), pages 1-15, February.
    7. Tamer, Tolga & Gürsel Dino, Ipek & Meral Akgül, Cagla, 2022. "Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    8. Trigo-González, Mauricio & Cortés-Carmona, Marcelo & Marzo, Aitor & Alonso-Montesinos, Joaquín & Martínez-Durbán, Mercedes & López, Gabriel & Portillo, Carlos & Batlles, Francisco Javier, 2023. "Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain," Renewable Energy, Elsevier, vol. 206(C), pages 251-262.
    9. Abunima, Hamza & Park, Woan-Ho & Glick, Mark B. & Kim, Yun-Su, 2022. "Two-Stage stochastic optimization for operating a Renewable-Based Microgrid," Applied Energy, Elsevier, vol. 325(C).
    10. Mateo, C. & Hernández-Fenollosa, M.A. & Montero, Á. & Seguí-Chilet, S., 2022. "Ageing and seasonal effects on amorphous silicon photovoltaic modules in a Mediterranean climate," Renewable Energy, Elsevier, vol. 186(C), pages 74-88.

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