Since the semi-closed and semi-opened spaces such as high-speed railway station have the indoor thermal comfort level difficult to predict, a Deep Forest (DF)-based deep learning method was proposed to realize the scientific prediction of thermal comfort level. Firstly, the heat exchange environment of high-speed railway station was modeled based on field survey and Energy Plus platform. Secondly, 8 influence factors, such as passenger density, operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners, were presented, and 424 operating conditions were designed to obtain massive data. Finally, DF was used to obtain the relationship between thermal comfort and influence factors in order to predict the indoor thermal comfort level of high-speed rail station. Deep Neural Network (DNN) and Support Vector Machine (SVM) were provided as comparison algorithms for the verification. Experimental results show that, among the three models, DF performs best in terms of the prediction accuracy and weighted- F ₁, and has the best prediction accuracy of 99.76% and the worst of 98.11%. Therefore, DF can effectively predict the indoor thermal comfort level of high-speed railway stations.

As many factors affect the thermal comfort of semi-enclosed buildings such as high-speed railway stations in a complex way and there exists contradiction between thermal comfort and energy consumption, a comprehensive prediction method for thermal comfort and energy consumption of high-speed railway station based on machine learning was proposed. Firstly, with sensor data capturing and Energy Plus platform, the indoor and outdoor status, the control units like multi-evaporator air conditioners and heat exchangers as well as the thermal energy transmission environment of high-speed railway station were modeled. Secondly, eight factors influencing the thermal comfort of high-speed railway station, such as the operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners, the operating number of heat exchangers, passenger density, outdoor temperature, indoor temperature, indoor humidity, and indoor carbon dioxide concentration, were proposed, 424 model operating conditions and 3 714 240 instances were designed. Finally, in order to effectively predict indoor thermal comfort and energy consumption of high-speed railway station, six machine learning methods, which are deep neural network, support vector regression, decision tree regression, linear regression, ridge regression and Bayesian ridge regression, were designed. Experimental results show that decision tree regression has the best prediction performance in a short time with average mean squared error of 0.002 2. The obtained research results can directly provide actively predicted environmental parameters and realize real-time decision-making for the temperature control strategy in the next stage.

To solve the problem that Markov model has poor prediction accuracy and sparse matching in location prediction, a mobile user location prediction method based on Adaboost-Markov model was proposed. Firstly, the original trajectory data was preprocessed by a trajectory division method based on angle offset and distance offset to extract feature points, and density clustering algorithm was used to cluster the feature points into interest regions of the user, then the original trajectory data was discretized into a trajectory sequence composed of interest regions. Secondly, according to the matching degree of prefix trajectory sequence and historical trajectory pattern tree, the model order k was adaptively determined. Finally, Adaboost algorithm was used to assign the corresponding weight coefficients according to the importance degree of 1 to k order Markov models to form a multi-order fusion Markov model, realizing the prediction of future interest regions of the mobile user. The experimental results on a large-scale real user trajectory dataset show that the average prediction accuracy of Adaboost-Markov model is improved by 20.83%, 11.3%, and 5.38% respectively compared with the first-order Markov model, the second-order Markov model, and the multi-order fusion Markov model with average weight coefficient, and the proposed model has good universality and multi-step prediction performance.