制冷技术2026，Vol.46Issue(2)：8-15,8.DOI:10.3969/j.issn.2095-4468.2026.02.102
基于长短期记忆神经网络的冷水机组能耗预测与用能异常分析
Energy Consumption Prediction and Abnormal Energy Usage Analysis of Chiller Units Based on Long Short-Term Memory Neural Network
摘要
Abstract
Based on long short-term memory(LSTM)neural networks,a collaborative optimization method for energy consumption forecasting and anomaly analysis is proposed.By collecting real-time operational data from a chiller unit in an electronics factory,and utilizing the Random Forest algorithm to select 12 key features strongly correlated with energy consumption,an LSTM forecasting model is developed.Dynamic thresholds are then set by comparing the predicted values with the actual values to identify potential energy anomalies.The experimental results show that the model's mean absolute error on the test set is 0.91,the root mean square error is 1.30,and the coefficient of determination reaches 0.97.The prediction accuracy of the model exceeds that of recurrent neural network and gated recurrent unit models.With the upper threshold set at 4.73 and the lower threshold at-1.07,532 abnormal data points are identified.The research indicates that this method,by integrating energy consumption prediction with abnormal energy usage analysis,can effectively identify abnormal energy consumption,providing a new technical path for the intelligent operation and maintenance and energy conservation management of chiller units.关键词
冷水机组/长短期记忆神经网络/能耗预测/用能异常分析Key words
Chiller/Long short-term memory neural network/Energy consumption prediction/Analysis of abnormal energy consumption分类
化学化工引用本文复制引用
曹睿祺,杨闯,陈焕新,叶明树,林才成..基于长短期记忆神经网络的冷水机组能耗预测与用能异常分析[J].制冷技术,2026,46(2):8-15,8.基金项目
国家自然科学基金(No.51876070). （No.51876070）
