Random Forest model to predict solar water heating system performance

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

Renewable Energy, 2023, vol. 216, issue C

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.

Keywords: Solar thermal energy systems; Artificial intelligence; Random Forest; Solar water heating (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:216:y:2023:i:c:s0960148123010005

DOI: 10.1016/j.renene.2023.119086

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