Bayesian Optimized of CNN-M-LSTM for Thermal Comfort Prediction and Load Forecasting in Commercial Buildings

Bayesian Optimized of CNN-M-LSTM for Thermal Comfort Prediction and Load Forecasting in Commercial Buildings

Heating, ventilation, and air conditioning (HVAC) systems account for 60% of the energy consumption in commercial buildings. Each year, millions of dollars are spent on electricity bills by commercial building operators. To address this energy consumption challenge, a predictive model named Bayesian...

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Bibliographic Details
Main Authors: Chi Nghiep Le, Stefan Stojcevski, Tan Ngoc Dinh, Arangarajan Vinayagam, Alex Stojcevski, Jaideep Chandran
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Designs
Subjects:
load forecasting
thermal comfort
commercial building
renewable energy
control engineering
deep learning
Online Access:https://www.mdpi.com/2411-9660/9/3/69
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