| Title |
Estimation of Electrical Loads Patterns by Usage in the Urban Railway Station by RNN |
| DOI |
http://doi.org/10.5370/KIEE.2018.67.11.1536 |
| Keywords |
Urban railway station ; Load pattern ; Deep learning ; Recursive neural networks ; Gated recurrent unit |
| 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. |