Abstract
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.
Highlights
A metro system plays an important role among urban mass transit systems and has a number of advantages over other public transportation modes in metropolitan areas, such as having more reliable services, being able to transport much larger volume of passengers, and being more environmentally friendly
According to statistical data derived from the Beijing Subway, the electricity consumption of a metro station (ECMS) has taken up approximately half of the total electricity consumption of an entire metro system
To reduce the ECMS is of great significance to cut down the whole electricity consumption of a metro system [1]
Summary
A metro system plays an important role among urban mass transit systems and has a number of advantages over other public transportation modes in metropolitan areas, such as having more reliable services, being able to transport much larger volume of passengers, and being more environmentally friendly. Ahn et al [13] built a linear regression model to assess existing subway stations performance and predict energy consumption levels for future expansion. These models would be less likely to achieve a high accuracy in predicting the ECMS, as they may fail to capture the underlying nonlinearity in the relationship between the ECMS and its influencing factors. A SVR-based model is developed to predict the daily ECMS, considering both the internal (e.g., layout of a station) and external factors with respect to operating a station. Input variables e area of concourse ( 1) e area of platform ( 2) e area of plant room ( 3) e area of staff accommodation room ( 4) e quantity of VT facilities ( 5) e height of VT facilities ( 6) Average temperature ( 7) Relative humidity ( 8) e total number of passengers ( 9)
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