基于LSTM与EMD结合的电厂循环冷却水系统运行状态预测研究

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关键词：循环冷却水系统；运行状态预测；遗传算法中图分类号：TM621 文献标志码：ADOI：10.19968/j.cnki.hnkj.1003-5168.2025.14.002文章编号：1003-5168（2025）14-0013-04

Research on Operation Status Prediction of the Circulating Cooling WaterSystem in PowerPlantsBased onthe Combinationof LSTM and EMD

LIU Feiyu （Intelligent Control Industry College，Henan Chemical Technician College，Kaifeng 4750Oo，China）

Abstract： [Purposes] To address the insufficient prediction accuracy of the operation status in power plant circulating cooling water system，a prediction method combining Genetic Algorithm-optimized Bidirectional Long Short-Term Memory neural networks （GA-BiLSTM） and Empirical Mode Decomposition （EMD）is proposed.[Methods]EMD is employed to decompose the original data into multiple Intrinsic Mode Function （IMF）components，thereby reducing data complexity.With the help of the Genetic Algorithm（GA），the hyperparameters of the Bidirectional Long Short-Term Memory neural network （BiLSTM） are optimized to improve the performance of the model. The decomposed IMF components are then individuallyfed into theoptimized GA-BiLSTM model for prediction，with final resultsobtained through reconstruction.[Findings] Experimental results demonstrate that all prediction error metrics of this model remain at low levels，with a 55% improvement in prediction accuracy compared to conventional models.[Conclusions] The prediction method based on the combination of LSTMand EMD can provide strong assurance for stable operation of the circulating cooling water system in power plants.

Keywords： circulating cooling water system; operation status prediction; Genetic Algorithm

0 引言

电厂循环冷却水系统在工业生产中占据关键地位，其稳定运行对保障设备正常运转、提高生产效率和降低能耗具有重要意义。（剩余4538字）