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

14 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

• (1). Al Mindeel T, Spentzou E, Eftekhari M. Energy, thermal comfort, and indoor air quality: multi-objective optimization review. Renew Sustain Energy Rev. 2024;202:114682.
• (2). Li L, He Y, Zhang H, Fung JC, Lau AK. Enhancing IAQ, thermal comfort, and energy efficiency through an adaptive multi-objective particle swarm optimizer-grey wolf optimization algorithm for smart environmental control. Build Environ. 2023;235:110235.
• (3). Li L, Zhang Y, Fung JC, Qu H, Lau AK. A coupled computational fluid dynamics and back-propagation neural network-based particle swarm optimizer algorithm for predicting and optimizing indoor air quality. Build Environ. 2022;207:108533.
• (4). Guo F, Woo Ham S, Kim D, Moon HJ. Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building. Appl Energy. 2025;377:124467.
• (5). Cho JH, Moon JW. Integrated artificial neural network prediction model of indoor environmental quality in a school building. J Clean Prod. 2022;344:131083.
• (6). Yu KH, Chen YA, Jaimes E, Wu WC, Liao KK, Liao JC, et al. Optimization of thermal comfort, indoor quality, and energy-saving in campus classroom through deep Q learning. Case Stud Therm Eng. 2021;24:100842.
• (7). Lopez-Perez LA, Flores-Prieto JJ, Rios-Rojas C. Comfort temperature prediction according to an adaptive approach for educational buildings in tropical climate using artificial neural networks. Energy Build. 2021;251:111328.
• (8). Somu N, Sriram A, Kowli A, Ramamritham K. A hybrid deep transfer learning strategy for thermal comfort prediction in buildings. Build Environ. 2021;204:108133.
• (9). Brik B, Esseghir M, Merghem-Boulahia L, Snoussi H. An IoT-based deep learning approach to analyse indoor thermal comfort of disabled people. Build Environ. 2021;203:108056.
• (10). Martínez-Comesaña M, Eguia-Oller P, Martinez-Torres J, Febrero-Garrido L, Granada-Álvarez E. Optimisation of thermal comfort and indoor air quality estimations applied to in-use buildings combining NSGA-III and XGBoost. Sustain Cities Soc. 2022;80:103723.
• (11). Morresi N, Casaccia S, Sorcinelli M, Arnesano M, Uriarte A, Torrens-Galdiz JI, et al. Sensing physiological and environmental quantities to measure human thermal comfort through machine learning techniques. IEEE Sens J. 2021;21(10):12322–37.
• (12). Wang Z, Calautit J, Tien PW, Wei S, Zhang W, Wu Y, et al. An occupant-centric control strategy for indoor thermal comfort, air quality, and energy management. Energy Build. 2023;285:112899.
• (13). Wang Z, Ma J, Gao Q, Bain C, Imoto S, Liò P, et al. Dual-stream multi-dependency graph neural network enables precise cancer survival analysis. Med Image Anal. 2024;97:103252.

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