Research Article
The Personalized Thermal Comfort Prediction Using an MH-LSTM Neural Network Method
| Author | Research scope | Target | Highlighted factors | Research methodology | F1 | F2 | F3 | F4 | F5 | F6 |
| Yao et al. [24] | Self-regulatory actions | Psychological factor | — | — | ○ | — | — | — | Survey and monitoring | Jowkar et al. [26] | Thermal exposure | — | — | ○ | — | — | ○ | Field study, classification, and data analysis | Xiong et al. [27] | Physiological parameter | Investigated gender differences | ○ | — | ○ | ○ | — | — | Experiment | Choi and Yeom [28] | Local skin temperatures | ○ | — | ○ | ○ | — | ○ | Monitoring | Chaudhuri et al. [29] | Machine learning | Predicted thermal state (PTS) | — | ○ | ○ | — | — | ○ | Machine learning | Cosma et al. [30] | Individual thermal preference model | ○ | ○ | ○ | ○ | — | ○ | Experiment | Katic et al. [31] | Artificial neural network algorithm | — | ○ | — | — | — | ○ | Data analysis |
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Note. F1 = skin temperature; F2 = artificial intelligence approaches; F3 = gender; F4 = sensor device; F5 = individual characteristics; F6 = prediction of thermal comfort.
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