Dr H Shaheen

Enhancing thermal comfort and indoor air quality through energy optimization with neural network

• Authors Details :  
• Mane,  
• S.,  
• Palaniswamy,  
• D.,  
• Shaheen

Journal title : Journal of Thermal Analysis and Calorimetry

Publisher : Springer Science and Business Media LLC

Online ISSN : 1588-2926

15 Views Original Article

Indoor thermal comfort and air quality are essential for occupant well-being, while simultaneously optimizing energy consumption in buildings. Achieving a balance between these factors presents a significant challenge, as indoor environments are dynamic and energy demands fluctuate. By modifying ventilation rates in response to real-time data, demand-controlled ventilation systems can reduce energy consumption and enhance indoor comfort and air quality. However, optimizing these systems with advanced predictive models remains a complex task. To address this challenge, this publication proposes a Dual-Stream Multi-Dependency Graph Neural Network (DMGNN)-based energy-efficient ventilation management technique that maximizes indoor air quality and thermal comfort. The suggested method seeks to enhance thermal comfort and air quality by maximizing heating, ventilation, and air conditioning (HVAC) operations while reducing energy consumption. Initially data are collected from an Indoor Air Quality Monitoring Dataset. The DMGNN is employed to capture the complex dependencies between environmental factors such as temperature, humidity, and CO2 concentrations, considering both temporal and spatial relationships. Implementing the proposed system and evaluating it through simulations in various building environments demonstrates notable improvements in thermal comfort, indoor air quality, and energy economy. The suggested system’s performance is contrasted with that of other current methods, showing superior energy efficiency and optimization of both indoor air quality and occupant comfort. This study presents an innovative, scalable framework for smart building management, promoting sustainable energy solutions.

Article DOI & Crossmark Data

DOI : https://doi.org/10.1007/s10973-025-14882-6

Article Subject Details

• Computer Science
• Computer Science Applications
• Mechanical Engineering

Article Keywords Details

• and air conditioning (HVAC)
• ventilation
• Dual-Stream Multi-Dependency Graph Neural Network (DMGNN)
• Heating
• Smart building management
• Sustainable energy solutions

Article File

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Article References

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