## 4 Discussion
Compared with other thermal dissatisfaction rate prediction methods, such as the adaptive dissatisfaction rate prediction model based on residential air conditioning turn-on behavior in China [34], which considers residents' adaptive behavior, extracts the data of air conditioning turn-on from the original data through the air conditioning turn-on judgment algorithm, adopts the Monte Carlo sampling method for transformation, obtains the data set of the percentage of residents' air conditioning turn-on behavior in a specific indoor and outdoor environment, and establishes a nonlinear regression model, the final R\({}^{2}\) of this model is 0.833, while the R\({}^{2}\) of the model proposed in this paper is 0.864. In contrast to the literature [35], which considered the thermal dissatisfaction rate caused by local thermal sensation, 16 subjects were selected for an experimental study in a climate chamber to assess the effect of thermal sensation of whole-body thermal conditions on the thermal dissatisfaction rate, and the predicted thermal environment dissatisfaction rate with vertical air temperature difference had a predicted model MAE of 0.2739, while the MAE of the model proposed in this paper was 0.033, which was reduced by 0.2409. In line with the main idea of the literature [36], a human thermal comfort prediction model was established and applied to the rational setting of indoor temperature and humidity to maintain the occupants' thermal comfort at a reasonable level. The difference is that the literature used indoor air temperature as the main index for studying thermal comfort in demand response (DR) and evaluated human thermal comfort using the _PMV-PPD_ model, where _PMV_ was used to determine the minimum and maximum acceptable indoor air temperature, and then evaluated the changing set point temperature using different control strategies, focusing on the rational setting of indoor temperature and humidity. And our study focused on combining group universal thermal comfort characteristics and individualized differences to modify the _PMV-PPD_ model in real time to obtain thermal comfort values closer to the actual ones and further designed strategies for setting values of indoor environmental parameters to verify the effectiveness of the prediction model, focusing on the prediction of human thermal comfort. In the future, this study refers to the advantages of literature to further compare the reasonableness of indoor temperature setting values under different control strategies and also to further improve the advantages of this paper. Furthermore, in this study, the predicted thermal discomfort rate is applied to the intelligent setting of indoor temperature and humidity, and combined with reinforcement learning methods, the real-time predicted thermal discomfort rate is utilized for online learning of indoor temperature and humidity. The findings indicate that when indoor occupants feel hot, compared to the _PPD_ model, the indoor temperature setting learned by the _BPD_ model is lower, resulting in higher human comfort and a thermal discomfort rate closer to the BPD prediction. Among the different reinforcement learning methods [37; 38], such as Q-learning, SARSA, and the DDPG algorithm performs better,achieving a reasonable indoor temperature and humidity setting while satisfying both human comfort and energy efficiency. There are two limitations to this study. First, the sample size is limited by the number of subjects. Future studies can expand the sample size by collecting environmental data from sensor monitoring, questionnaires, and video-recorded body language expressions of thermal discomfort. Second, this study is an experimental study based on a real office scenario. Future studies can investigate the number and distribution of buildings and collect data from different building types to enhance the persuasiveness of the data and the validity of the model.