An Estimation Model on Electricity Consumption of New Metro Stations

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.

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

“…Five-fold-cross-validation with the genetic algorithm was used to optimize the hyper-parameters of the SVR to improve its performance. 9 In addition to the aforementioned input parameters, wind speed and direction, day, and time information have also been used in the estimation of electricity consumption. 10 Parametric and nonparametric AutoRegressive (AR, NPAR), Smooth Transition AutoRegressive (STAR), and AutoRegressive…”

“…In this model, similar to the previous model, meteorological data such as air temperature and relative humidity were used as input data that affected consumption. Five‐fold‐cross‐validation with the genetic algorithm was used to optimize the hyper‐parameters of the SVR to improve its performance 9 . In addition to the aforementioned input parameters, wind speed and direction, day, and time information have also been used in the estimation of electricity consumption 10 .…”

Predicting the traction power of metropolitan railway lines using different machine learning models

“…Finally, it is important to note that studies using ML approaches other than ANN for predicting railways energy consumption are rather scarce. In fact, we only found the work of Yu et al [34] who estimated the daily electricity consumption of a newly constructed metro station, rather than on a train itself, using a Support Vector Machine model.…”