Personal thermal comfort prediction model based on naive Bayesian incremental learning algorithm

The establishment of personal thermal comfort model through machine learning requires the acquisition of a large amount of data before modeling, which is not in line with the actual application scenarios. Moreover, it costs too much to update the model to adapt to the change of personal thermal comfort preference with seasons and other factors. Therefore, based on the naive Bayesian algorithm, establishes and updates the thermal comfort model using the incremental learning method, in which the prior probability and the posterior probability of the model is modified. Through experimental data verification, after learning 26 samples averagely by this method, the predicted results can be substantial agreement with the true thermal preference (Kappa coefficient is 0.6994). Moreover, compared with the existing update methods, the establishment and update method proposed in this paper saves 17% of the time cost and more than 90% of the space cost. Thus, it is more suitable for deployment to lightweight intelligent terminals with fewer computing and storing resources.