A Novel Feature Selection Method Based on an Improved Beluga Whale Optimization Algorithm for Thermal Comfort Prediction
Received: 14 January 2026 | Revised: 22 February 2026, 13 March 2026, 20 March 2026, 22 March 2026, and 24 March 2026 | Accepted: 26 March 2026 | Online: 29 April 2026
Corresponding author: S. Josephine Theresa
Abstract
Heating, Ventilation, and Air Conditioning (HVAC) systems consume a significant portion of building energy and do not usually address individual thermal comfort requirements. To overcome this difficulty, the present study proposes a privacy-aware, deep learning-based thermal comfort prediction system using Federated Deep Learning (FDL). The preprocessing of the ASHRAE Global Thermal Comfort Database II was performed through outlier removal, Z-score normalization, categorical encoding, and class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The Cubic Transverse Mutation Beluga Whale Optimization (CTMBWO) algorithm was employed to effectively identify features without redundancy and enhance robustness. A federated deep neural network was trained on the chosen features without learning raw data. Chicken Swarm Optimization (CSO) was applied to optimize hyperparameters and maximize convergence and accuracy. Five-fold stratified cross-validation achieved a mean of 98.14 with low variance, outperforming baseline models. The proposed model facilitates energy-saving and privacy-conscious HVAC management in intelligent buildings.
Keywords:
Heating, Hentilation, Air Conditioning (HVAC), Machine Learning (ML), Deep Neural Network (DNN), building types, Federated Deep Learning (FDL)Downloads
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Copyright (c) 2026 C. Premila Rosy, Saravanan Ki, S. Josephine Theresa, D. Meenakshi, D. Geethamani
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