Comparative assessment of machine learning approaches to predict building annual cooling load intensity in urban environments

Attarhay Tehrani, Alireza; Sobhaninia, Saeideh; Bertini, Aurora; Attia, Shady; Tekler, Zeynep Duygu; Manshour, Shiva; Amaripadath, Deepak

doi:10.1080/17512549.2026.2686610

Article (Scientific journals)

Comparative assessment of machine learning approaches to predict building annual cooling load intensity in urban environments

Attarhay Tehrani, Alireza; Sobhaninia, Saeideh; Bertini, Aurora et al.

2026 • In Advances in Building Energy Research, p. 1-38

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10.1080/17512549.2026.2686610

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Keywords :

Energy consumption; energy management; machine learning; optimization; cooling load

Abstract :

[en] Reducing energy consumption has become a critical priority in sustainable urban development, with accurate prediction of building cooling loads essential for efficient energy management systems. However, challenges such as inconsistent data and model generalizability restrict the seamless integration of energy modeling into urban planning. As such, this study introduces a novel hybrid modeling framework to forecast annual cooling load intensity in Phoenix, Arizona. The proposed hybrid approach integrates Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) in a stacking ensemble and uses a GA to optimize hyperparameters and the meta-learner. The results demonstrate that this optimized framework achieved a better accuracy with a root mean square error (RMSE) of 1.09 kWh/m², a coefficient of determination (R²) of 0.99, a mean absolute error (MAE) of 0.26 kWh/m², and a mean square error (MSE) of 1.19 kWh/m². Additionally, SHAP interpretability analysis indicates the window-to-wall ratio as the most influential driver of cooling load inPhoenix, Arizona. The model’s robustness was further validated on 100 unseen buildings in Scottsdale, Arizona, demonstrating strong generalizability. These findings highlight the potential of the proposed framework to enhance building energy forecasting and to provide actionable insights that support urban energy management.

Disciplines :

Architecture

Author, co-author :

Attarhay Tehrani, Alireza ; Islamic Azad University

Sobhaninia, Saeideh ; University of Washington

Bertini, Aurora ; Université de Liège - ULiège > Département ArGEnCo > Techniques de construction des bâtiments ; UC Louvain

Attia, Shady ; Université de Liège - ULiège > Département ArGEnCo > Techniques de construction des bâtiments

Tekler, Zeynep Duygu ; University of Oxford

Manshour, Shiva ; University of Nevada

Amaripadath, Deepak ; Université de Liège - ULiège > Urban and Environmental Engineering ; Arizona State University ; Arizona State University

Language :

English

Title :

Comparative assessment of machine learning approaches to predict building annual cooling load intensity in urban environments

Publication date :

06 July 2026

Journal title :

Advances in Building Energy Research

ISSN :

1751-2549

eISSN :

1756-2201

Publisher :

Informa UK Limited

Pages :

1-38

Peer reviewed :

Peer Reviewed verified by ORBi

Development Goals :

11. Sustainable cities and communities
7. Affordable and clean energy

Additional URL :

https://www.tandfonline.com/doi/pdf/10.1080/17512549.2026.2686610

Available on ORBi :

since 06 July 2026

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