某地铁站空调负荷逆向分解方法与验证  

Reverse decomposition method and verification of air conditioning load for an underground railway station

作　　者：王小飞[1] 刚文杰[2] 肖紫薇 徐新华[2] Wang Xiaofei;Gang Wenjie;Xiao Ziwei;Xu Xinhua(China Railway Siyuan Survey and Design Group Co.,Ltd.,Wuhan;Huazhong University of Science and Technology,Wuhan)

机构地区：[1]中铁第四勘察设计院集团有限公司,武汉 [2]华中科技大学,武汉

出　　处：《暖通空调》2022年第3期157-161,共5页Heating Ventilating & Air Conditioning

摘　　要：提出采用随机森林算法对某地铁站大系统空调负荷进行分解,以便获取各子项负荷,为地铁站空调系统负荷计算、设计及运营提供一定参考。根据对冷负荷分项负荷特征的分析,选择室外参数、总冷负荷及时间对随机森林模型进行训练。训练集均方根误差为0~5.1 kW,平均误差为0~7.2%;测试集均方根误差RMSE为0~15.7 kW,平均误差为0~22.6%。4项分项负荷中,灯光设备负荷分解精度最高,人员负荷与新风负荷分解误差相对较大。The random forest method is proposed to decompose the cooling load of the air conditioning system in an underground railway station in order to obtain the subitem cooling load,and provide some reference for the cooling load calculation,system design and system operation of the HVAC system.Based on the analysis of the characteristics of these subitem cooling loads,outdoor parameters,total cooling load and time are selected for model training.The results show that the RMSEs of the training set are 0 to 5.1 kW with average error of 0 to 7.2%.The RMSEs of the test set are 0 to 15.7 kW with average error of 0 to 22.6%.Among these four subitem cooling loads,the lighting equipment load has the highest accuracy in decomposition,while the occupant load and outdoor air load have relatively large errors.

关 键 词：空调系统 负荷分解 随机森林 非侵入式负荷 均方根误差 

分 类 号：TU962[建筑科学]