An Estimation Model on Electricity Consumption of New Metro Stations
Electricity consumption of metro stations increases sharply with expansion of a metro network and this has been a growing cause for concern. Based on relevant historical data from existing metro stations, this paper proposes a support vector regression (SVR) model to estimate daily electricity consu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/3423659 |
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| _version_ | 1832561288938520576 |
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| author | Zhao Yu Yun Bai Qian Fu Yao Chen Baohua Mao |
| author_facet | Zhao Yu Yun Bai Qian Fu Yao Chen Baohua Mao |
| author_sort | Zhao Yu |
| collection | DOAJ |
| description | Electricity consumption of metro stations increases sharply with expansion of a metro network and this has been a growing cause for concern. Based on relevant historical data from existing metro stations, this paper proposes a support vector regression (SVR) model to estimate daily electricity consumption of a newly constructed metro station. The model considers some major factors influencing the electricity consumption of metro station in terms of both the interior design scheme of a station (e.g., layout of the station and allocation of facilities) and external factors (e.g., passenger volume, air temperature and relative humidity). A genetic algorithm with five-fold cross-validation is used to optimize the hyper-parameters of the SVR model in order to improve its accuracy in estimating the electricity consumption of a metro station (ECMS). With the optimized hyper-parameters, results from case studies on the Beijing Subway showed that the estimating accuracy of the proposed SVR model could reach up to 95% and the correlation coefficient was 0.89. It was demonstrated that the proposed model could outperform the traditional methods which use a back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations. |
| format | Article |
| id | doaj-art-7220bf2d809b400b9da176099d939f33 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7220bf2d809b400b9da176099d939f332025-02-03T01:25:26ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/34236593423659An Estimation Model on Electricity Consumption of New Metro StationsZhao Yu0Yun Bai1Qian Fu2Yao Chen3Baohua Mao4Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, No. 3, Shangyuancun, Haidian District, Beijing 100044, ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, No. 3, Shangyuancun, Haidian District, Beijing 100044, ChinaBirmingham Centre for Railway Research and Education, School of Engineering, University of Birmingham, Birmingham B15 2TT, UKKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, No. 3, Shangyuancun, Haidian District, Beijing 100044, ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, No. 3, Shangyuancun, Haidian District, Beijing 100044, ChinaElectricity consumption of metro stations increases sharply with expansion of a metro network and this has been a growing cause for concern. Based on relevant historical data from existing metro stations, this paper proposes a support vector regression (SVR) model to estimate daily electricity consumption of a newly constructed metro station. The model considers some major factors influencing the electricity consumption of metro station in terms of both the interior design scheme of a station (e.g., layout of the station and allocation of facilities) and external factors (e.g., passenger volume, air temperature and relative humidity). A genetic algorithm with five-fold cross-validation is used to optimize the hyper-parameters of the SVR model in order to improve its accuracy in estimating the electricity consumption of a metro station (ECMS). With the optimized hyper-parameters, results from case studies on the Beijing Subway showed that the estimating accuracy of the proposed SVR model could reach up to 95% and the correlation coefficient was 0.89. It was demonstrated that the proposed model could outperform the traditional methods which use a back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations.http://dx.doi.org/10.1155/2020/3423659 |
| spellingShingle | Zhao Yu Yun Bai Qian Fu Yao Chen Baohua Mao An Estimation Model on Electricity Consumption of New Metro Stations Journal of Advanced Transportation |
| title | An Estimation Model on Electricity Consumption of New Metro Stations |
| title_full | An Estimation Model on Electricity Consumption of New Metro Stations |
| title_fullStr | An Estimation Model on Electricity Consumption of New Metro Stations |
| title_full_unstemmed | An Estimation Model on Electricity Consumption of New Metro Stations |
| title_short | An Estimation Model on Electricity Consumption of New Metro Stations |
| title_sort | estimation model on electricity consumption of new metro stations |
| url | http://dx.doi.org/10.1155/2020/3423659 |
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