摘要
针对除霜控制方法的自适应性需求,本文提出一种制热能力衰减(Degradation of Heating Capacity,DHC)方法识别结霜状态,并基于全连接神经网络(Fully Connected Neural Network,FNN)分类模型开展除霜效果水平预测研究。结果表明:在空气源热泵的监测案例中,所提出的DHC方法能有效识别结霜状态,且在测试集中FNN分类模型对除霜效果水平识别的准确率达到91.43%。与原始除霜控制方法相比,整个供暖季的除霜频率、热量损失和功耗损失分别降低66.3%、1775 MJ和1829 MJ,同时季节性能参数SCOP提高8.6%。研究结果为ASHP系统的除霜控制方法在实际运行中的实施与优化提供了一条有效途径。
Based on the adaptive demand of the defrosting control method,the degradation of heating capacity(DHC)method was developed in this work to identify the frosty state,and the defrosting effect was evaluated adopting a fully connected neural network(FNN)classification model.Results indicated that in the monitoring case of the ASHP system,the proposed DHC method can effectively identify the frosty state,and the defrosting effect recognition accuracy achieved 91.3%for the trained FNN classification model in the testing data set.Compared with the original defrosting control method,the defrosting frequency,heating loss and power consumption were respectively reduced by 66.3%,1775 MJ and 1829 MJ,and the SCOP was increased by 8.6%throughout the heating season.The promising results in this work will provide an innovative approach for the implementation and optimization of the defrosting control strategy of the ASHP system in practical operation.
作者
郭焱华
邵双全
李浩
王智超
GUO Yanhua;SHAO Shuangquan;LI Hao;WANG Zhichao(School of Energy and Power Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;China Academy of Building Research,Beijing 100013,China)
出处
《工程热物理学报》
EI
CAS
CSCD
北大核心
2024年第3期628-634,共7页
Journal of Engineering Thermophysics
基金
国家自然科学基金(No.52076085)
华中科技大学学术前沿青年团队资助(No.2019QYTD10)。
关键词
空气源热泵
除霜
数据驱动
制热能力衰减
air source heat pump
defrosting
data-driven
degradation of heating capacity