E3S Web of Conferences (Jan 2019)

Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant

  • Lu Kuan,
  • Gao Song,
  • Xiangkun Pang,
  • lingkai Zhu,
  • Meng Xiangrong,
  • Sun Wenxue

DOI
https://doi.org/10.1051/e3sconf/201913601012
Journal volume & issue
Vol. 136
p. 01012

Abstract

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A multi-layer LSTM (Long short-term memory) model is proposed for condenser vacuum degree prediction of power plants. Firstly, Min-max normalization is used to pre-process the input data. Then, the model proposes the two-layer LSTM architecture to identify the time series pattern effectively. ADAM(Adaptive moment)optimizer is selected to find the optimum parameters for the model during training. Under the proposed forecasting framework, experiments illustrates that the two-layer LSTM model can give a more accurate forecast to the condenser vacuum degree compared with other simple RNN (Recurrent Neural Network) and one-layer LSTM model.