전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제67권11호
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  • Pages.1536-1541
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  • 2018
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  • 1975-8359(pISSN)
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  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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RNN을 활용한 도시철도 역사 부하 패턴 추정

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

  • Park, Jong-young (Smart Electrical & Signaling Division, Korea Railroad Research Institute)
  • 투고 : 2018.09.28
  • 심사 : 2018.10.30
  • 발행 : 2018.11.01
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초록

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.

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참고문헌

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