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Estimation of electrical power consumption in subway station design by intelligent approach

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  • Leung, Philip C.M.
  • Lee, Eric W.M.

Abstract

According to the records of Hong Kong rail operator, MTR Corporation, the weekly electrical consumption of each railway station ranges from 18MWh to 230MWh. Since the electrical consumption of stations is a major factor in the planning of infrastructure, a good prediction of the electrical consumption will greatly assist in the design of the station infrastructure. This study develops an intelligent approach to predict the energy consumption of railway stations. Multi-layered Perceptron (MLP) is adopted to mimic the non-linear correlation between energy consumption, the spatial design of the station, meteorological factors and also the usage of the 19 stations selected. Coefficient of correlation is obtained between the MLP predicted results and the actual collected data to evaluate the performance of the prediction. We apply statistical approach to assess the performance of the developed MLP model. It shows that minimum coefficient of correlation is 0.96 with a 95% confidence level which is considered sufficient for engineering application. This approach is also adopted to predict the profile of the weekly electrical consumption of a selected station. The predicted profile reasonably agrees with that of the actual consumption. This study develops a useful tool to estimate the electrical power consumption of new MTR stations.

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  • Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    • Handle: RePEc:eee:appene:v:101:y:2013:i:c:p:634-643
    DOI: 10.1016/j.apenergy.2012.07.017
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    1. Rehdanz, Katrin, 2007. "Determinants of residential space heating expenditures in Germany," Energy Economics, Elsevier, vol. 29(2), pages 167-182, March.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    4. Basbous, Tammam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2012. "A new hybrid pneumatic combustion engine to improve fuel consumption of wind–Diesel power system for non-interconnected areas," Applied Energy, Elsevier, vol. 96(C), pages 459-476.
    5. Gutiérrez, R. & Gutiérrez-Sánchez, R. & Nafidi, A., 2006. "Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors," Applied Energy, Elsevier, vol. 83(10), pages 1139-1151, October.
    6. Kilpatrick, R.A.R. & Banfill, P.F.G. & Jenkins, D.P., 2011. "Methodology for characterising domestic electrical demand by usage categories," Applied Energy, Elsevier, vol. 88(3), pages 612-621, March.
    7. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    8. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    9. Uri, Noel D., 1978. "Forecasting peak system load using a combined time series and econometric model," Applied Energy, Elsevier, vol. 4(3), pages 219-227, July.
    10. Deeble, V.C. & Probert, S.D., 1986. "Straight-line correlations for annual energy-consumption predictions?," Applied Energy, Elsevier, vol. 25(1), pages 23-39.
    11. Kaldellis, J.K. & Kavadias, K.A. & Filios, A.E., 2009. "A new computational algorithm for the calculation of maximum wind energy penetration in autonomous electrical generation systems," Applied Energy, Elsevier, vol. 86(7-8), pages 1011-1023, July.
    12. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    13. Kadoshin, Shiro & Nishiyama, Takashi & Ito, Toshihide, 2000. "The trend in current and near future energy consumption from a statistical perspective," Applied Energy, Elsevier, vol. 67(4), pages 407-417, December.
    14. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    15. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
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