Abstract
With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.
Highlights
There are two common methods to establish a thermal model in a space: the distributed parameter method and the lumped parameter method
This paper aims to use the Random Vector Functional Link Neural Network (RVFLNN) to quickly establish a thermal environment model for a metro station
A thermal environment modeling method based on the RVFLNN was proposed for metro stations
Summary
There are two common methods to establish a thermal model in a space: the distributed parameter method and the lumped parameter method. Studies in the literature [1,2] use the distributed parameter method to establish a heat transfer model, and to achieve the distribution and prediction of object temperature. This method has the disadvantages of high computational cost and slow solution process. The lumped parameter model was established for the thermal node network method. The ANN possesses the advantage of strong nonlinear fitting ability. It has become the main research direction when building a heat transfer model. The ANN method has been used to establish the nonlinear heat transfer model
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
