Next Article in Journal
Evaluating Typical Algorithms of Combinatorial Optimization to Solve Continuous-Time Based Scheduling Problem
Next Article in Special Issue
BELMKN: Bayesian Extreme Learning Machines Kohonen Network
Previous Article in Journal
An Approach for Setting Parameters for Two-Degree-of-Freedom PID Controllers
Previous Article in Special Issue
An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming
Open AccessArticle

Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network

School of Electronic Information Engineering, North China University of Technology , Beijing 100144, China
Beijing Urban Construction Design & Development Group Co. Ltd., Beijing 100088, China
School of Aviation Science and Engineering, Beihang University (BUAA), Beijing 100191, China
Author to whom correspondence should be addressed.
Algorithms 2018, 11(4), 49;
Received: 9 March 2018 / Revised: 10 April 2018 / Accepted: 11 April 2018 / Published: 17 April 2018
(This article belongs to the Special Issue Advanced Artificial Neural Networks)
PDF [11548 KB, uploaded 3 May 2018]


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. View Full-Text
Keywords: RVFLNN; thermal environment; temperature prediction; metro station RVFLNN; thermal environment; temperature prediction; metro station

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Tian, Q.; Zhao, W.; Wei, Y.; Pang, L. Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network. Algorithms 2018, 11, 49.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top