IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v216y2023ics0960148123010005.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123010005
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119086?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Pillai, P. K. C. & Agarwal, R. C., 1981. "Factors influencing solar energy collector efficiency," Applied Energy, Elsevier, vol. 8(3), pages 205-213, July.
    4. 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).
    5. 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.
    6. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Rongquan & Bu, Siqi & Li, Gangqiang, 2024. "Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach," Applied Energy, Elsevier, vol. 367(C).
    2. Şenol, Halil & Çolak, Emre & Oda, Volkan, 2024. "Forecasting of biogas potential using artificial neural networks and time series models for Türkiye to 2035," Energy, Elsevier, vol. 302(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gil, Juan D. & Topa, A. & Álvarez, J.D. & Torres, J.L. & Pérez, M., 2022. "A review from design to control of solar systems for supplying heat in industrial process applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    3. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
    4. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    5. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    6. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clément & Rathelot, Roland, 2024. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," Journal of Development Economics, Elsevier, vol. 169(C).
    7. Albert Stuart Reece & Gary Kenneth Hulse, 2022. "European Epidemiological Patterns of Cannabis- and Substance-Related Congenital Neurological Anomalies: Geospatiotemporal and Causal Inferential Study," IJERPH, MDPI, vol. 20(1), pages 1-35, December.
    8. Foutzopoulos, Giorgos & Pandis, Nikolaos & Tsagris, Michail, 2024. "Predicting full retirement attainment of NBA players," MPRA Paper 121540, University Library of Munich, Germany.
    9. Jiang, X.S. & Jing, Z.X. & Li, Y.Z. & Wu, Q.H. & Tang, W.H., 2014. "Modelling and operation optimization of an integrated energy based direct district water-heating system," Energy, Elsevier, vol. 64(C), pages 375-388.
    10. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    11. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    12. Marchetto, Elisa & Da Re, Daniele & Tordoni, Enrico & Bazzichetto, Manuele & Zannini, Piero & Celebrin, Simone & Chieffallo, Ludovico & Malavasi, Marco & Rocchini, Duccio, 2023. "Testing the effect of sample prevalence and sampling methods on probability- and favourability-based SDMs," Ecological Modelling, Elsevier, vol. 477(C).
    13. Eeva-Katri Kumpula & Pauline Norris & Adam C Pomerleau, 2020. "Stocks of paracetamol products stored in urban New Zealand households: A cross-sectional study," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-11, June.
    14. Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.
    15. Jian Lu & Raheel Ahmad & Thomas Nguyen & Jeffrey Cifello & Humza Hemani & Jiangyuan Li & Jinguo Chen & Siyi Li & Jing Wang & Achouak Achour & Joseph Chen & Meagan Colie & Ana Lustig & Christopher Dunn, 2022. "Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    16. Timo Schulte & Tillmann Wurz & Oliver Groene & Sabine Bohnet-Joschko, 2023. "Big Data Analytics to Reduce Preventable Hospitalizations—Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions," IJERPH, MDPI, vol. 20(6), pages 1-16, March.
    17. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
    18. Fogliato Riccardo & Oliveira Natalia L. & Yurko Ronald, 2021. "TRAP: a predictive framework for the Assessment of Performance in Trail Running," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 129-143, June.
    19. Cruz, Talita & Schaeffer, Roberto & Lucena, André F.P. & Melo, Sérgio & Dutra, Ricardo, 2020. "Solar water heating technical-economic potential in the household sector in Brazil," Renewable Energy, Elsevier, vol. 146(C), pages 1618-1639.
    20. Unterberger, Viktor & Lichtenegger, Klaus & Kaisermayer, Valentin & Gölles, Markus & Horn, Martin, 2021. "An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems," Applied Energy, Elsevier, vol. 293(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:216:y:2023:i:c:s0960148123010005. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.