摘要
本研究以北京某办公楼的地源热泵系统为例,分别采用EnergyPlus公式拟合模型、支持向量机算法和BP神经网络算法对其制冷工况和供热工况进行能耗建模。系统采集了两年的运行数据,包括热泵和水泵的电耗、地源侧和用户侧的进出水温度与流量。结果表明,EnergyPlus公式拟合模型、支持向量机算法和BP神经网络算法在制冷工况下的标准平均误差(NMBE)分别为-2.22%、-1.12%和0.43%,变异均方根误差(CVRMSE)分别为10.70%、9.45%和5.01%;供热工况下NMBE分别为-1.20%、-0.97%和0.03%,CVRMSE分别为11.06%、14.26%和7.91%。3个模型的精度都满足要求,但是EnergyPlus公式拟合模型仅适用于满足热平衡公式的35.3%的情况。基于机器学习的模型对整个运行期间预测都较准确,其中BP神经网络模型效果最好。
This paper presents a case study to model the ground source heat pump system of an office building in Beijing.There modeling methods are used,including EnergyPlus equation fitting model,support vector machine algorithm and BP neural network algorithm.The system has collected two years of operating data,including the power consumption of heat pumps and water pumps,and the temperatures and flow rates of supply and return water on the ground source side and the user side.The results show that the normalized mean bias errors(NMBE)of EnergyPlus equation fitting model,support vector machine algorithm and BP neural network algorithm under cooling conditions are-2.22%,-1.12%and 0.43%,respectively,and the coefficients of variation of the root mean square error(CVRMSE)are 10.70%,9.45%and 5.01%,respectively.The NMBE under heating conditions are-1.20%,-0.97%and 0.03%,and CVRMSE are 11.06%,14.26%and 7.91%,respectively for three models.The accuracy of three models all meets the requirements,but the EnergyPlus equation fitting model is only suitable for stabilized cases,which accounts for 35.3%.The machine learning based models can ensure accuracy during the entire operation period,and the BP neural network model has the best performance.
作者
杨楚豪
陈毅兴
袁玥
陈志华
李念平
彭琛
YANG Chuhao;CHENG Yixing;YUAN Yue;CHEN Zhihua;LI Nianpin;PENG Chen(School of civil engineering,Hunan University,Changsha 410082,China;Persagy Technology CO.,Ltd,Beijing 100096,China)
出处
《建筑科学》
CSCD
北大核心
2022年第4期97-104,168,共9页
Building Science
基金
湖南省自然科学基金优秀青年基金资助项目(2020JJ3008)
国家重点研发计划政府间国际科技创新合作/港澳台科技创新合作重点专项“净零能耗建筑适宜技术研究与集成示范”(2019YFE0100300)。