摘要
本文以冷水机组系统为研究对象,通过对历史运行数据的数据处理和基于设备经验模型的回归拟合,建立系统的能耗预测模型。为了进一步验证模型的可用性,本文将该能耗预测模型应用于基于粒子群优化(PSO)算法的优化控制策略,并使用该策略对冷水机组系统进行优化控制。结果表明,能耗预测模型的平均绝对百分比误差(MAPE)为1.54%,相关性系数(R^(2))为0.968。与常规控制策略对比,基于能耗预测模型的优化控制策略在典型日节能8.31%。
A multiple chiller system is taken as the research object.Through data processing of actual operation data and regression fitting based on equipment empirical model,the prediction model for system energy consumption is established.In order to further verify the usability of the model,the energy consumption prediction model to an optimized control strategy based on particle swarm optimization(PSO)algorithm is applied and the strategy to control the chiller system is used in this paper.The results show that the mean absolute percentage error(MAPE)of the prediction model is 1.54%and the correlation coefficient(R2)is 0.968.Compared with the conventional control strategy,the optimal control strategy based on the energy consumption prediction model can save 8.31%on a typical day.
作者
胡蓝青
晋欣桥
杜志敏
HU Lanqing;JIN Xinqiao;DU Zhimin(Institute of Refrigeration and Cryogenics,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《制冷技术》
2022年第3期13-20,共8页
Chinese Journal of Refrigeration Technology
关键词
冷水机组
运行数据
能耗模型
控制策略
Chiller system
Operating data
Energy consumption model
Control strategy
作者简介
晋欣桥(1965-),男,教授,博士。研究方向:建筑空调系统仿真和先进控制方法。联系地址:上海市闵行区东川路800号上海交通大学制冷与低温工程研究所,邮编200240。联系电话:021-34206774,E-mail:xqjin@sjtu.edu.cn。
