A Novel Condenser Vacuum Degree Prediction Model Based on LSTM and MemN2N

Abstract

Condenser vacuum degree prediction of power plants is a challenge task in power system security field. Most existing studies are based on shallow machine learning algorithms, which fail to leverage historical data comprehensively, resulting inaccuracy and unreliable predictions. Therefore, using a serialization model like Recurrent Neural Network to capture time-series information from historical data is necessary. However, these serialization model alone has inherent defects in dealing with long-distance dependence, which may cause historical information forgetting problem. This paper proposes a new prediction model combining LSTM and End-To-End Memory Network (MemN2N). We use LSTM to mine the long-distance dependency information in historical data, and introduce the encoding historical information into the memory pool of MemN2N. MemN2N allows better preservation of historical information for serialization model LSTM, and can make accurate and reliable predictions through soft attention mechanism. Through the experiments on real data from the power plant show that, compared with other prediction models, the model proposed in this paper achieves higher prediction accuracy and has great engineering value.