制冷技术2025，Vol.45Issue(1)：55-60,6.DOI:10.3969/j.issn.2095-4468.2025.01.202
基于长短期记忆神经网络的工厂冷水机组短期功率预测
Short-Term Power Prediction of Plant Chiller Based on Long Short-Term Memory Neural Network
摘要
Abstract
A prediction model based on long short-term memory(LSTM)is proposed to realize the short-term prediction of the chiller in a farad electronic plant,and its accuracy is verified by comparing with the multiple linear regression model.In order to further improve the prediction performance of the model,the network structure is optimized to obtain the optimal prediction model.The results show that when the number of hidden layers of LSTM model is 2 and the number of neurons in the hidden layer is 120,the prediction accuracy of the model is the highest,with RMSE(root mean square error)of 5.644 and R2(determination coefficient)of 0.921,which indicates that LSTM model can predict the power of the chiller well.关键词
冷水机组/长短期记忆网络/功率预测/决定系数Key words
Chiller/Long short-term memory/Power prediction/Determination coefficient分类
通用工业技术引用本文复制引用
王宜卿,叶明树,任义成,陈焕新,程亨达..基于长短期记忆神经网络的工厂冷水机组短期功率预测[J].制冷技术,2025,45(1):55-60,6.基金项目
国家自然科学基金(No.51876070). （No.51876070）
