Application of Machine Learning Techniques for Predicting Heating Coil Performance in Building Heating Ventilation and Air Conditioning Systems

Adam Nassif, Pasidu Dharmasena and Nabil Nassif ()
Additional contact information
Adam Nassif: Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH 45220, USA
Pasidu Dharmasena: Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH 45220, USA
Nabil Nassif: Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH 45220, USA

Energies, 2025, vol. 18, issue 9, 1-30

Abstract: Heating systems in a building’s mechanical infrastructure account for a significant share of global building energy consumption, underscoring the need for improved efficiency. This study evaluates 31 predictive models—including neural networks, gradient boosting (XGBoost), bagging, and multiple linear regression (MLR) as a baseline—to estimate heating-coil performance. Experiments were conducted on a water-based air-handling unit (AHU), and the dataset was cleaned to eliminate illogical and missing values before training and validation. Among the evaluated models, neural networks, gradient boosting, and bagging demonstrated superior accuracy across various error metrics. Bagging offered the best balance between outlier robustness and pattern recognition, while neural networks showed strong capability in capturing complex relationships. An input-importance analysis further identified key variables influencing model predictions. Future work should focus on refining these modeling techniques and expanding their application to other HVAC components to improve adaptability and efficiency.

Keywords: HVAC systems; machine learning; neural network; gradient boosting; bagging; XGBoost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/9/2314/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/9/2314/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:9:p:2314-:d:1647263

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().