%0 Journal Article
%T 基于LSTM模型的电厂传感器点位预测<br>Prediction of Power Plant Sensor Point Based on LSTM Model
%A 田霄阳
%A 张明西
%A 王博闻
%A 符云杰
%A 周飞
%A 刘洲
%A 胡高斌
%A 姚怡豪
%J Modeling and Simulation
%P 506-513
%@ 2324-870X
%D 2024
%I Hans Publishing
%R 10.12677/MOS.2024.131049
%X 随着大数据的发展，国内众多电厂也开始智能化改进。传感器点位的实时数值是反应发电机组当前健康状况的重要指标，具有较强的跟踪意义，研究并预测短时间内传感器点位的实时数值变化有助于电厂监盘人员了解发电机组当前的健康状况，能够提前应对意外情况。通过对传感器点位数值的时间序列数据建模分析，使用LSTM模型，对后续的几次传感器点位数值进行短期预测，取得了较好的拟合预测结果，充分说明时间序列模型应用在传感器点位数值的短期预测的可行性。随着电厂智能化的深入，传感器点位预测还应进一步提高准确度，帮助电厂监盘人员更好地应对发电机组异常状况。<br />
With the development of big data, many power plants in China have also begun to improve their in-telligence. The real-time value of sensor points is an important indicator reflecting the current health status of the generator set, which has strong tracking significance. Studying and predicting the real-time value changes of sensor points in a short period of time can help power plant moni-toring personnel understand the current health status of the generator set and respond to unex-pected situations in advance. By modeling and analyzing the time series data of sensor point values, the LSTM model was used to make short-term predictions for subsequent sensor point values, and good fitting prediction results were obtained, fully demonstrating the feasibility of applying the time series model to short-term prediction of sensor point values. With the deepening of intelli-gence in power plants, the accuracy of sensor point prediction should be further improved to help power plant monitoring personnel better cope with abnormal conditions of generator units.
%K 时间序列，传感器点位数值，LSTM模型，短期预测<br>Time Series
%K Sensor Point Value
%K LSTM Model
%K Short-Term Prediction
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=79633