{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T21:29:52Z","timestamp":1761514192022,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,17]],"date-time":"2018-04-17T00:00:00Z","timestamp":1523923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>With the improvement of China\u2019s 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.<\/jats:p>","DOI":"10.3390\/a11040049","type":"journal-article","created":{"date-parts":[[2018,4,18]],"date-time":"2018-04-18T03:51:13Z","timestamp":1524023473000},"page":"49","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Qing","family":"Tian","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, North China University of Technology , Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, North China University of Technology , Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Wei","sequence":"additional","affiliation":[{"name":"Beijing Urban Construction Design &amp; Development Group Co. Ltd., Beijing 100088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Aviation Science and Engineering, Beihang University (BUAA), Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,17]]},"reference":[{"key":"ref_1","first-page":"1080","article-title":"Simulation and forecast of the grinding temperature based on finite element and neural network","volume":"2013","author":"Ma","year":"2013","journal-title":"J. Electron. Meas. Instrum."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1016\/j.cja.2016.03.011","article-title":"Cutting tool temperature prediction method using analytical model for end milling","volume":"29","author":"Wu","year":"2016","journal-title":"Chin. J. Aeronaut."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.solener.2016.06.060","article-title":"Dynamic thermal performance prediction model for the flat-plate solar collectors based on the two-node lumped heat capacitance method","volume":"135","author":"Deng","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.applthermaleng.2017.03.108","article-title":"On the treatment of plane fusion front in lumped parameter thermal models with convection","volume":"120","author":"Skrzypek","year":"2017","journal-title":"Appl. Therm. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.enbuild.2014.05.001","article-title":"An improved lumped parameter method for building thermal modeling","volume":"79","author":"Underwood","year":"2014","journal-title":"Energy Build."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1016\/j.applthermaleng.2005.11.008","article-title":"Computation of thermal comfort inside a passenger car compartment","volume":"26","author":"Mezrhab","year":"2006","journal-title":"Appl. Therm. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.applthermaleng.2015.06.002","article-title":"Electric vehicle air conditioning system performance prediction based on artificial neural network","volume":"89","author":"Tian","year":"2015","journal-title":"Appl. Therm. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ejps.2016.03.010","article-title":"Implementation of an artificial neural network as a PAT tool for the prediction of temperature distribution within a pharmaceutical fluidized bed granulator","volume":"88","author":"Korteby","year":"2016","journal-title":"Eur. J. Pharm. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.energy.2013.08.027","article-title":"Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner","volume":"61","author":"Adewole","year":"2013","journal-title":"Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.applthermaleng.2015.07.036","article-title":"Artificial Neural Network model for predicting wall temperature of supercritical boilers","volume":"90","author":"Dhanuskodi","year":"2015","journal-title":"Appl. Therm. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.asoc.2015.09.034","article-title":"Prediction of cutting temperature in orthogonal maching of AISI 316L using artificial neural network","volume":"38","author":"Kara","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.nucet.2017.05.008","article-title":"Prediction of the moderator temperature field in a heavy water reactor based on a cellular neural network","volume":"3","author":"Starkov","year":"2017","journal-title":"Nucl. Energy Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.aei.2016.05.001","article-title":"Tree-Structure Ensemble General Regression Neural Networks applied to predict the molten steel temperature in Ladle Furnace","volume":"30","author":"Wang","year":"2016","journal-title":"Adv. Eng. Inf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/72.737490","article-title":"The ensemble approach to neural-network learning and generalization","volume":"10","author":"Lgelnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_15","unstructured":"Rao, C.R., and Mitra, S.K. (1971). Generalized Inverse of Matrices and Its Applications, Wiley."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neucom.2011.12.046","article-title":"Evolutionary extreme learning machine ensembles with size control","volume":"102","author":"Wang","year":"2013","journal-title":"Neurocomputting"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1109\/72.471375","article-title":"Stochastic choice of basis functions in adaptive function approximation and the functional-link net","volume":"6","author":"Igelnik","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.ins.2015.01.007","article-title":"Distributed learning for Random Vector Functional-Link networks","volume":"301","author":"Scardapane","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/0925-2312(94)90053-1","article-title":"Learning and generalization characteristics of the random vector functional-link net","volume":"6","author":"Pao","year":"1994","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ins.2013.12.016","article-title":"Fast decorrelated neural network ensembles with random weights","volume":"264","author":"Alhamdoosh","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115","DOI":"10.18280\/ijht.330116","article-title":"The Local Media Radiant Temperature for the Calculation of Comfort in Areas Characterized by Radiant Surfaces","volume":"33","author":"Cannistraro","year":"2015","journal-title":"Int. J. Heat Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/23080477.2015.11665651","article-title":"Smart Control of air Climatization System in Function on the Valuers of the Mean Local Radiant Temperature","volume":"3","author":"Cannistraro","year":"2015","journal-title":"Smart Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"S589","DOI":"10.18280\/ijht.34S255","article-title":"Evaluation of the Sound Emissions and Climate Acoustic in Proximity of ome Railway Station","volume":"34","author":"Cannistraro","year":"2016","journal-title":"Int. J. Heat Technol."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/11\/4\/49\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:00:59Z","timestamp":1760194859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/11\/4\/49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,17]]},"references-count":23,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["a11040049"],"URL":"https:\/\/doi.org\/10.3390\/a11040049","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2018,4,17]]}}}