## 1 Introduction  
People now have higher expectations for the level of comfort in their living spaces due to the rapid economic development and improvement in material living standards [1]. Due to the numerous individual variances present, determining the group's thermal comfort in public building areas with random human movement can be difficult [2, 3, 4]. The thermal dissatisfaction rate has emerged as a research hotspot for comfort and energy conservation as a key indicator to assess the group's thermal comfort. The requirements for human comfort and building energy conservation can be efficiently satisfied by using the thermal dissatisfaction rate in the intelligent setting of an inside thermal environment [5]. In accordance with ASHRAE [6], an indoor environment that provides thermal comfort to its occupants makes at least 80% of its occupants feel comfortable. The _PMV-PPD_ model developed by Professor [PERSON] [7] is currently widely used internationally to assess human thermal comfort. It was based on the heat balance theory and calculated the predicted mean vote (_PMV_) and predicted percentage of dissatisfaction (_PPD_) by taking six factors into account including indoor air temperature, mean radiation temperature, air flowrate, relative humidity, metabolic rate, and thermal resistance of clothing. The _PMV_ measures the average thermal sensation of the population to the thermal environment, and the _PPD_ reflects the percentage of people who feel too cold or too hot. On the other hand, the _PMV-PPD_ model cannot satisfy individualized needs as it simply represents the population's average level of thermal comfort. Some researchers have developed thermal comfort prediction models utilizing machine learning techniques in order to increase the precision of thermal discomfort prediction by taking into account individualized thermal comfort variances. Using Markov chain Monte Carlo methods for parameter estimation and model validation, [PERSON] [8] developed a prediction model of the thermal environment dissatisfaction rate based on the Bayesian theory, taking into account individual variances in a human thermal sense. The results showed that the model could better reflect the actual thermal comfort of occupants but failed to fully consider the inter and intra-individual thermal sensation. In order to predict individualized thermal comfort while taking into account inter- and intra-individual thermal sensation, [PERSON] et al. [9] used the Gaussian process regression (GPR) algorithm. To reduce the risk of overfitting and increase the efficiency and accuracy of prediction, they chose constrained LASSO regression as a feature selection method. However, because the impact of weather forecast data was not taken into account, the system may not provide timely and effective temperature management and comfort enhancement methods. In order to effectively deal with complex nonlinear relationships, [PERSON] et al. [10] combined weather forecast data with indoor sensor data and used artificial neural networks to adaptively learn and adjust the model. The research only used artificial neural networks as a machine learning algorithm and did not compare it to other algorithms. Four machine learning algorithms--artificial neural network, random forest, support vector machine, and linear discriminant analysis--were compared by [PERSON] et al. [11] and found to have high accuracy and reliability in predicting thermal comfort while taking into account all meteorological data and environmental factors. However, all of those mentioned machine learning techniques use offline collections of subject parameters to develop thermal comfort prediction models for certain situations, which are then used for the prediction of a given person's thermal comfort in a particular environment [12]. Real-time online human physiological data collection minimizes the restrictions of particular areas and populations and provides a new way to predict thermal comfort [13].