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Estimation of Electrical Loads Patterns by Usage in the Urban Railway Station by RNN

RNN을 활용한 도시철도 역사 부하 패턴 추정

  • Park, Jong-young (Smart Electrical & Signaling Division, Korea Railroad Research Institute)
  • Received : 2018.09.28
  • Accepted : 2018.10.30
  • Published : 2018.11.01

Abstract

For effective electricity consumption in urban railway station such as peak load shaving, it is important to know each electrical load pattern by various usage. The total electricity consumption in the urban railway substation is already measured in Korea, but the electricity consumption for each usage is not measured. The author proposed the deep learning method to estimate the electrical load pattern for each usage in the urban railway substation with public data such as weather data. GRU (gated recurrent unit), a variation on the LSTM (long short-term memory), was used, which aims to solve the vanishing gradient problem of standard a RNN (recursive neural networks). The optimal model was found and the estimation results with that were assessed.

Keywords

References

  1. Han-Su Kim and Oh-Kyu Kwon, "Power demand forecasting in the DC urban railway substation", Trans. of KIEE, Vol. 63, No. 11, pp. 1608-1614, Nov. 2014.
  2. Jae-Haeng Heo, Seungkwon Shin, Jong-young Park, and Hyeongig Kim, "Study on the optimal operation of ESS considering urban railway load characteristic", Trans. of KIEE, Vol. 64, No. 10, pp. 1508-1516, Oct. 2015.
  3. Jong-young Park, Jae-Haeng Heo, Seungkwon Shin, and Hyungchul Kim, "Economic evaluation of ESS in urban railway substation for peak load shaving based on net present value", Journ. of Electr. Eng. Technol., Vol. 12, No. 2, pp. 981-987, Mar. 2017. https://doi.org/10.5370/JEET.2017.12.2.981
  4. Jong-young Park, Jae-Haeng Heo, Hyeongig Kim, Hyungchul Kim, and Seungkwon Shin, "Economic evaluation of ESS applying to demand response management in urban railway system", Trans. of KIEE, Vol. 66, No. 1, pp. 222-228, Jan. 2017.
  5. Hosung Jung, Hyungchul Kim, Seoungkwon Shin, Kiyong Yoon, Jae-moon Kim, and Yang-su Kim, "Installation of power monitoring system for load pattern analysis on DC urban transit system", ISGC&E 2013, July 2013.
  6. Hansang Lee, Seungmin Jung, Hosung Jung, Hyungchul Kim, and Gilsoo Jang, "Power management for electric railway system to reduce the railway operating cost", 2012 KIEE fall conf., pp. 411-413, 2012.
  7. Thanasis G. Barbounis, John B. Theocharis, Minas C. Alexiadis, and Petros S. Dokopoulos, "Long-term wind speed and power forecasting using local recurrent neural network models", IEEE Trans. Energy Conver., Vol. 21, No. 1, pp. 273-284, March 2006. https://doi.org/10.1109/TEC.2005.847954
  8. Zhichao Shi, Hao Liang, and Venkata Dinavahi, "Direct interval forecast of uncertain wind power based on recurrent neural networks", IEEE Trans. Sustain. Energy, Vol. 9, No. 3, pp. 1177-1187, July 2018. https://doi.org/10.1109/TSTE.2017.2774195
  9. Weicong Kong, Zhao Yang Dong, David J. Hill, Fengji Luo, and Yan Xu, "Short-term residential load forecasting based on resident behaviour learning," IEEE Trans. Power Syst., Vol. 33, No. 1, pp. 1087-1088, Jan. 2018. https://doi.org/10.1109/TPWRS.2017.2688178
  10. Dong-Ha Shin and Chang-Bok Kim, "A study on deep learning input pattern for summer power demand prediction", Journ. of KIIT. Vol. 14, No. 11, pp. 127-134, Nov. 2016.
  11. Jun-Ho Park, Dong-Ha Shin, and Chang-Bok Kim, "Deep learning model for electric power demand prediction using special day separation and prediction elements extension", Journ. of Advanc. Navigat. Technol., Vol. 21, No. 4, pp. 365-370, Aug. 2017.
  12. Jong-young Park, "Analysis of electrical loads in the urban railway station by big data analysis", Trans. of KIEE, Vol. 67, No. 3, pp. 460-466, Mar. 2018.
  13. Kyunghyun Cho, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", Proc. of EMNLP 2014.
  14. "Understanding LSTM Networks", http://colah.github.io/posts/2015-08-Understanding-LSTMs/(accessed Sep. 11. 2018)