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July 1, 2026Journal of King Saud University - ScienceOpen Access

A study on efficiency estimation of water based PV/T systems with machine learning methods

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Authors

MDMerve DemirciRÖRahim Aytuğ Özer

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Overview

Randomized trial evaluates efficiency prediction in PV/T systems, suggesting improved accuracy with ML methods.

Key Points

  • This research aims to enhance the precision of efficiency predictions for water-based PV/T systems using machine learning techniques.
  • Utilized a dataset from literature for machine learning models including SVR, LR, and ANN.
  • Performed preprocessing steps such as min-max normalization and z-score normalization prior to analysis.
  • Analyzed input parameters such as mass flow rate and ambient temperature.
  • Achieved the best prediction performance using SVR with RMSE of 0.225 and R² of 0.98018.
  • Min-max normalization during preprocessing resulted in optimal model accuracy.
  • Identified PV/T surface area as the most influential parameter affecting efficiency.

Cite This Study

Demirci et al. (2026) studied this question.

synapsesocial.com/papers/6a44ae015cd2549c8bc435afhttps://doi.org/10.25259/jksus_1980_2025
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