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ThermCont: A machine Learning enabled Thermal Comfort Control Tool in a real time
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.buildenv.2016.06.014openalex-snapshot
Thermal comfort and occupant adaptive behaviour in Japanese university buildings with free running and cooling mode offices during summer
inferredcites →https://doi.org/10.1016/j.egypro.2015.11.656openalex-snapshot
Thermal Comfort Evaluation Within Non-residential Environments: Development of Artificial Neural Network by Using the Adaptive Approach Data
inferredcites →https://doi.org/10.1016/j.enbuild.2007.02.009openalex-snapshot
Climate change, thermal comfort and energy: Meeting the design challenges of the 21st century
inferredcites →https://doi.org/10.1016/j.enbuild.2014.04.052openalex-snapshot
Utilizing artificial neural network to predict energy consumption and thermal comfort level: An indoor swimming pool case study
inferredcites →https://doi.org/10.1016/j.enbuild.2015.07.047openalex-snapshot
A review of human thermal comfort in the built environment
inferredcites →https://doi.org/10.1016/j.jenvp.2015.01.007openalex-snapshot
Tracking the human-building interaction: A longitudinal field study of occupant behavior in air-conditioned offices
inferredcites →https://doi.org/10.1016/s0003-6870(72)80074-7openalex-snapshot
Thermal comfort: Analysis and applications in environmental engineering
inferredcites →https://doi.org/10.1108/00438020210441849openalex-snapshot
Thermal environment effects on construction workers’ productivity
inferredcites →https://doi.org/10.1109/66.762881openalex-snapshot
Multiple linear regression analysis of the overlay accuracy model
inferredcites →https://doi.org/10.1109/coase.2015.7294164openalex-snapshot
Predicting individual thermal comfort using machine learning algorithms
inferredcites →https://doi.org/10.1109/icsgsc.2017.8038552openalex-snapshot
Machine learning based prediction of thermal comfort in buildings of equatorial Singapore
inferredcites →https://doi.org/10.1109/ijcnn.2012.6252365openalex-snapshot
Neural network PMV estimation for model-based predictive control of HVAC systems
inferredcites →https://doi.org/10.1177/1420326x11425967openalex-snapshot
Investigation of Gender Differences in Sleeping Comfort at Different Environmental Temperatures
inferredcites →https://doi.org/10.1177/1420326x14539693openalex-snapshot
Prediction of the thermal comfort indices using improved support vector machine classifiers and nonlinear kernel functions
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s44189-023-00020-0openalex-snapshot
Evaluation of thermal comfort in air-conditioned rooms based on structure/control-related parameters and data-mining method
inferred← citeshttps://doi.org/10.1016/j.buildenv.2021.108056openalex-snapshot
An IoT-based deep learning approach to analyse indoor thermal comfort of disabled people
inferred← citeshttps://doi.org/10.1016/j.enbuild.2021.111692openalex-snapshot
A thermal control methodology based on a machine learning forecasting model for indoor heating
inferred← citeshttps://doi.org/10.1049/enc2.70000openalex-snapshot
A Stackelberg game‐based model for low‐carbon scheduling of commercial building loads considering lifecycle unit carbon‐emission factors
inferred← citeshttps://doi.org/10.1063/5.0240460openalex-snapshot
Geo-specific development of thermal comfort prediction models: A machine learning approach using the ASHRAE dataset
inferred← citeshttps://doi.org/10.1080/10447318.2022.2124359openalex-snapshot
“Intelligent Tutoring System in Education for Disabled Learners Using Human–Computer Interaction and Augmented Reality”
inferred← citeshttps://doi.org/10.1109/nanomed59780.2023.10404963openalex-snapshot
Accuracy Enhancement of Micro-PMV Sensor System for Human Thermal Comfort Measurements through Machine Learning
inferred← citeshttps://doi.org/10.1109/pimrc48278.2020.9217157openalex-snapshot
An IoT Environment for Estimating Occupants’ Thermal Comfort
inferred← citeshttps://doi.org/10.1109/swc65939.2025.00303openalex-snapshot
Adaptive Thermal Comfort Modeling Using Machine Learning: A Generalizable, Low-Sensor Approach for Diverse Climates and Buildings
inferred← citeshttps://doi.org/10.1117/1.jei.31.6.061818openalex-snapshot
Accurate semantic segmentation of RGB-D images for indoor navigation
inferred← citeshttps://doi.org/10.24018/ejece.2021.5.4.346openalex-snapshot
Smart System for Thermal Comfort Prediction on Residential Buildings Using Data-Driven Model with Random Forest Classifier
inferred← citeshttps://doi.org/10.3390/en15030808openalex-snapshot
Providing Convenient Indoor Thermal Comfort in Real-Time Based on Energy-Efficiency IoT Network
inferred← citeshttps://doi.org/10.3390/su18073381openalex-snapshot
Skipping Energy Simulation with S-TCML: A Surrogate Machine Learning Sustainable Framework for Real-Time Thermal Comfort Evaluation in Office Buildings
inferred← citeshttps://doi.org/10.37190/ord220401openalex-snapshot
Supervisory optimal control using machine learning for building thermal comfort
inferred← citeshttps://doi.org/10.37256/scb.1120265408openalex-snapshot
Enhancing Thermal Comfort Through Leading-Edge Design, Monitoring, and Optimization Technologies: A Review
inferred← citeshttps://doi.org/10.70675/91637969z4070z4816z8ce3zbfe93506a6f7openalex-snapshot
Thermal Comfort Control with Energy Constraints in Smart Buildings
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