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Electronic Demand Data Prediction using Bidirectional Long Short Term Memory Networks

  • Journal of Software Assessment and Valuation
  • Abbr : JSAV
  • 2018, 14(1), pp.33-40
  • Publisher : Korea Software Assessment and Valuation Society
  • Research Area : Engineering > Computer Science

Ko Sangjun 1 Ho-Yeong Yun 2 Dong-Myung Shin 1

1엘에스웨어(주)
2연세대학교

Candidate

ABSTRACT

Power demand prediction is used to detect anomalies such as machine malfunction and power off when the measured power amount deviates from the predicted power amount by a certain range in the industrial sector. Power data is difficult to predict because it is time series data with strong seasonality and trend. In this paper, a power prediction model based on bi-directional LSTM algorithm, which is a deep learning based algorithm and the most popular algorithm in recent years, has been established along with LSTM. The model was learned, evaluated, and verified using four years of power data.

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