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Random Forest model to predict solar water heating system performance

Author

Listed:
  • Lillo-Bravo, I.
  • Vera-Medina, J.
  • Fernandez-Peruchena, C.
  • Perez-Aparicio, E.
  • Lopez-Alvarez, J.A.
  • Delgado-Sanchez, J.M.

Abstract

This research proposes a Random Forest RF model to replace the experimental tests required by the ISO 9459–5:2007 for predicting the annual energy supplied and the solar fraction covered by a thermosiphon solar water heating system (TSWHS) for the same locations and daily load volumes that this standard. 38 TSWHS have been tested according to the procedures outlined in the standard ISO 9459-5 and two more have been selected from the Solar Keymark database to get the training and testing data set. From these, data from 36 of the TSWHS were used for RF model training, while data from the remaining four TSWHS were used for its testing. To assess the performance of the RF model, three statistical indicators were calculated: mean absolute percentage error (MAPE), mean absolute error (MAE) and the determination coefficient (R-square). Results show MAPE between 2.94% and 5.86% for the annual energy supplied and the solar fraction and R-Square between 0.995 and 0.998 for the annual energy supplied and between 0.973 and 0.976 for the solar fraction for all locations and daily load volume. Consequently, the RF model could be used successfully to replace the experimental tests required by the Standard.

Suggested Citation

  • Lillo-Bravo, I. & Vera-Medina, J. & Fernandez-Peruchena, C. & Perez-Aparicio, E. & Lopez-Alvarez, J.A. & Delgado-Sanchez, J.M., 2023. "Random Forest model to predict solar water heating system performance," Renewable Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s0960148123010005
    DOI: 10.1016/j.renene.2023.119086
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    References listed on IDEAS

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    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Vieira, Abel S. & Stewart, Rodney A. & Lamberts, Roberto & Beal, Cara D., 2018. "Residential solar water heaters in Brisbane, Australia: Key performance parameters and indicators," Renewable Energy, Elsevier, vol. 116(PA), pages 120-132.
    3. Marco Quartulli & Amaia Gil & Ane Miren Florez-Tapia & Pablo Cereijo & Elixabete Ayerbe & Igor G. Olaizola, 2021. "Ensemble Surrogate Models for Fast LIB Performance Predictions," Energies, MDPI, vol. 14(14), pages 1-17, July.
    4. Correa-Jullian, Camila & Cardemil, José Miguel & López Droguett, Enrique & Behzad, Masoud, 2020. "Assessment of Deep Learning techniques for Prognosis of solar thermal systems," Renewable Energy, Elsevier, vol. 145(C), pages 2178-2191.
    5. Vera-Medina, J. & Fernandez-Peruchena, C. & Guasumba, J. & Lillo-Bravo, I., 2021. "Performance analysis of factory-made thermosiphon solar water heating systems," Renewable Energy, Elsevier, vol. 164(C), pages 1215-1229.
    6. Pillai, P. K. C. & Agarwal, R. C., 1981. "Factors influencing solar energy collector efficiency," Applied Energy, Elsevier, vol. 8(3), pages 205-213, July.
    7. Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).
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