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

Song Gao,Zhu Lingkai,Xiangrong Meng,T.Y Fang,Kuan Lu,Wenxue Sun,Pang Xiangkun,V Khaletski

doi:10.1051/e3sconf/201913601012

Abstract

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.

Highlights

The condenser is the cold source of thermodynamic cycles in power plants, and its performance directly affects the unit's peak load regulation capability, operation safety and thermal efficiency
Ge Xiaoxia et al [1] proposed a prediction model of condenser vacuum based on Drosophila algorithm optimization generalized regression neural network
Li Jianqiang et al [2] constructed a steam condenser vacuum prediction model based on the PSO-SVR model

Summary

Introduction

The condenser is the cold source of thermodynamic cycles in power plants, and its performance directly affects the unit's peak load regulation capability, operation safety and thermal efficiency. Ge Xiaoxia et al [1] proposed a prediction model of condenser vacuum based on Drosophila algorithm optimization generalized regression neural network. LSTM [6] is a deep learning model with a recurrent network structure It has made a series of achievements in natural language processing [7], image recognition [8] and other fields, and its prediction accuracy and reliability have been significantly improved. A multi-layer LSTM based condenser vacuum degree prediction model for power plants is proposed. In the process of training, the model adopts ADAM method to find the optimum parameters Last, this model is applied a power plant in Shandong Province from July 1 to 26, 2017 for vacuum degree prediction. Compared with the baseline results from simple RNN network model and single-layer LSTM model, the prediction effect of this method has been significantly improved

Long short-term Memory

Data Preprocessing

Forecasting Process

Dataset and experimental results

Conclusion