工业工程2024，Vol.27Issue(6)：26-37,12.DOI:10.3969/j.issn.1007-7375.240012
基于CNN-GA-XGBoost负荷预测的中央空调冷水机组数字孪生系统研究
A Digital Twin System for Central Air Conditioning Chiller Units Based on CNN-GA-XGBoost Load Forecasting
摘要
Abstract
Central air conditioning chiller systems often operate with partial load to meet the cooling demand at building terminals,leading to high energy consumption.Load forecasting for central air conditioning chiller units is beneficial for energy-saving renovations to reach the optimal load conditions.A prediction model for the digital twin of chiller units is proposed based on convolutional neural networks(CNN),genetic algorithm(GA),and extreme gradient boosting(XGBoost)to cope with the complexities of interactions and multi-variables in a chilled water system.First,the CNN-GA-XGBoost model is trained with historical data.Subsequently,the trained model is integrated into the digital twin system through an application programming interface(API)for real-time predictions.Finally,the predicted results are visualized within the digital twin system.Results demonstrate a decision coefficient of evaluated indicators of 0.995 by the proposed method,with an mean absolute percentage error(MAPE)of 0.82 and a root mean square error(RMSE)of 2.22.The digital twin prediction model effectively bridges physical entities and data-driven approaches,enabling accurate predictions of building air conditioning loads.Furthermore,the proposed prediction method demonstrates higher accuracy and better generalization compared with other models.关键词
卷积神经网络/数字孪生/负荷预测/冷水系统/遗传算法/超参数优化Key words
convolutional neural network/digital twin/load prediction/chilled water system/genetic algorithm/hyperparameter optimization分类
信息技术与安全科学引用本文复制引用
翁卫兵,李鹏冲,彭晨,万安平..基于CNN-GA-XGBoost负荷预测的中央空调冷水机组数字孪生系统研究[J].工业工程,2024,27(6):26-37,12.基金项目
国家自然科学基金资助项目(52372420) （52372420）
