Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems

Hakan Tutumlu

doi:10.35860/iarej.1934313

Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems

Abstract

Heat Recovery Ventilation (HRV) systems reduce building energy demand by thermally coupling exhaust and fresh outdoor air streams through a plate heat exchanger core, without auxiliary heating. This study presents a machine-learning (ML) framework for predicting and optimizing the passive performance of the Argemsan K-203 cross-flow plate heat exchanger under variable airflow conditions. Outdoor fresh air at 7.8oC/81%RH was drawn through the HRV core and supplied to the occupied space, while indoor exhaust air at 21.1oC /55%RH was simultaneously discharged outside through the same core-the built-in 1500W electrical heater was intentionally deactivated throughout. Experiments were conducted over 60 minutes at 13 measurement states spanning u_t∈{2, 4, 6} m/s and u_e∈{2, 4, 6, 7.2} m/s. Three regression models - Random Forest (RF), Gradient Boosting (GB), and Ridge Regression-were evaluated via Leave-One-Out Cross Validation (LOO-CV). RF achieved R2 =0.931, MAE=0.261oC for fresh-air outlet temperature (T4); Gradient Boosting achieved R2=0.880 for sensible efficiency (ηT). Sensible efficiency ranged from 48.2% to 78.8% (mean 58.9%) and a humidity-ratio–based latent effectiveness of 35-59% (mean 44%). Recovered heat power spanned 245-713W, yielding 236-685 kWh/year of annual energy savings and 48-138 (natural gas) or 100-290 (grid) kgCO2/year avoidance per unit. Response-surface analysis identifies u_t=2 m/s with u_e ≥6 m/s as the optimal operating band.

Keywords

References

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Details

Primary Language

English

Subjects

Energy Generation, Conversion and Storage (Excl. Chemical and Electrical)

Journal Section

Research Article

Authors

Hakan Tutumlu ^*
0000-0003-3884-7015
Türkiye

Publication Date

August 25, 2026

Submission Date

April 20, 2026

Acceptance Date

August 22, 2026

Published in Issue

Year 2026 Volume: 10 Number: 2

DOI

https://doi.org/10.35860/iarej.1934313

IZ

https://izlik.org/JA33LZ88WY

Cite

RIS / Bibtex

APA

Tutumlu, H. (2026). Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems. International Advanced Researches and Engineering Journal, 10(2), 143-149. https://doi.org/10.35860/iarej.1934313

AMA

1.Tutumlu H. Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems. Int. Adv. Res. Eng. J. 2026;10(2):143-149. doi:10.35860/iarej.1934313

Chicago

Tutumlu, Hakan. 2026. “Artificial Intelligence-Assisted Performance Prediction and Operating-Point Identification for Passive Heat Recovery Ventilation Systems”. International Advanced Researches and Engineering Journal 10 (2): 143-49. https://doi.org/10.35860/iarej.1934313.

EndNote

Tutumlu H (August 1, 2026) Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems. International Advanced Researches and Engineering Journal 10 2 143–149.

IEEE

[1]H. Tutumlu, “Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems”, Int. Adv. Res. Eng. J., vol. 10, no. 2, pp. 143–149, Aug. 2026, doi: 10.35860/iarej.1934313.

ISNAD

Tutumlu, Hakan. “Artificial Intelligence-Assisted Performance Prediction and Operating-Point Identification for Passive Heat Recovery Ventilation Systems”. International Advanced Researches and Engineering Journal 10/2 (August 1, 2026): 143-149. https://doi.org/10.35860/iarej.1934313.

JAMA

1.Tutumlu H. Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems. Int. Adv. Res. Eng. J. 2026;10:143–149.

MLA

Tutumlu, Hakan. “Artificial Intelligence-Assisted Performance Prediction and Operating-Point Identification for Passive Heat Recovery Ventilation Systems”. International Advanced Researches and Engineering Journal, vol. 10, no. 2, Aug. 2026, pp. 143-9, doi:10.35860/iarej.1934313.

Vancouver

1.Hakan Tutumlu. Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems. Int. Adv. Res. Eng. J. 2026 Aug. 1;10(2):143-9. doi:10.35860/iarej.1934313