应用数据挖掘的高校教学建筑空调使用及其能耗分析

doi:10.3785/j.issn.1008-973X.2020.09.003

浙江大学学报(工学版)

2020, Vol. 54

Issue (9): 1677-1689 DOI: 10.3785/j.issn.1008-973X.2020.09.003

土木与交通工程

应用数据挖掘的高校教学建筑空调使用及其能耗分析

李鑫悦1(

),陈淑琴1,*(

),李鸿亮2,3,楼云霄2,李佳鹤4

1. 浙江大学建筑工程学院，浙江杭州 310058
2. 浙江中易和节能技术有限公司，浙江杭州 310052
3. 浙江大学控制科学与工程学院，浙江杭州 310027
4. 浙江蓝卓工业互联网信息技术有限公司，浙江杭州 310053

Analysis of air-conditioning usage and energy consumption in campus teaching buildings with data mining

Xin-yue LI1(

),Shu-qin CHEN1,*(

),Hong-liang LI2,3,Yun-xiao LOU2,Jia-he LI4

1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. Zhejiang Excenergy Energy-saving Technology Co. Ltd, Hangzhou 310052, China
3. College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
4. Zhejiang Bluetron Industry Internet Information Technology Co. Ltd, 310053, China

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摘要：

以浙江省某高校为研究对象，根据其节能监管平台在2016年11月—2019年2月的空调实时运行数据，利用聚类方法，全年共得到6种典型空调使用模式以及4种空调能耗模式. 利用基于监督学习的决策树、随机森林算法，对空调使用与能耗的关系进行解耦，明确不同空调使用情况导致的不同能耗水平，并使用交叉验证的方法比较多种机器学习算法的精度. 分析结果表明：空调使用时长均直接影响着日空调能耗，且在制冷工况下教室规模和空调使用强度也对能耗有着明显的影响. 研究结果可为高校教学建筑的节能管理及其能耗模拟提供支持.

关键词： 教学建筑; 数据挖掘; 空调使用行为; 空调能耗; 能耗影响解耦

Abstract:

This study was based on the real-time operation data of the air-conditioning (AC) system which were collected by the energy consumption monitoring platform in a university located in Zhejiang province from November 2016 to February 2019. With the clustering analysis, six typical AC usage patterns and four energy consumption patterns were proposed for a whole year. Two supervised machine learning methods, namely the decision tree and random forest, were used to decouple the relation of AC usage and its energy consumption, and to figure out the different energy consumption levels under different AC usage conditions. The cross-validation method was used to compare the accuracy of various machine learning algorithms. Results show that the usage hour directly influences the energy consumption. The area scale of classrooms and AC use intensity have the significant effect on energy consumption in cooling scenario. The results of this study are beneficial to energy-saving management and the simulation of energy consumption for the teaching buildings in colleges and universities.

Key words: teaching buildings data mining air-conditioner usage air-conditioning energy consumption decoupling influences of energy consumption

收稿日期: 2019-09-11 出版日期: 2020-09-22

CLC:

TU 201.5

基金资助: 国家重点研发计划资助项目（2018YFC0704400）

通讯作者: 陈淑琴 E-mail: lixinyue@zju.edu.cn;hn_csq@126.com

作者简介: 李鑫悦（1995—），男，硕士生，从事智能建筑研究. orcid.org/0000-0002-8973-8698. E-mail： lixinyue@zju.edu.cn

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引用本文:

李鑫悦,陈淑琴,李鸿亮,楼云霄,李佳鹤. 应用数据挖掘的高校教学建筑空调使用及其能耗分析[J]. 浙江大学学报(工学版), 2020, 54(9): 1677-1689.

Xin-yue LI,Shu-qin CHEN,Hong-liang LI,Yun-xiao LOU,Jia-he LI. Analysis of air-conditioning usage and energy consumption in campus teaching buildings with data mining. Journal of ZheJiang University (Engineering Science), 2020, 54(9): 1677-1689.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.09.003 或 http://www.zjujournals.com/eng/CN/Y2020/V54/I9/1677

表 1 浙江某高校的校教室统计表

图 1 不同规模教室的空调使用时长分布

图 2 2016-2019年各月室外平均温度及各月制冷、制热占比均值

表 2 全年空调使用时间段划分

表 3 浙江某高校的作息时间表

图 3 空调使用与能耗分析研究框架

表 4 用于能耗解耦效果评价的混淆矩阵示意

图 4 各典型空调使用模式的逐时空调使用率

表 5 空调典型使用模式特征

表 6 典型空调使用模式在各季节的占比

图 5 不同容量教室的空调能耗分布

图 6 不同容量教室的用能强度分布

表 7 制冷、制热工况下典型能耗模式的特征值

图 7 各月室外平均温度与空调设定温度分布

图 8 制冷工况下各使用模式的能耗模式分布

图 9 制热工况各使用模式下能耗模式分布

图 10 制冷工况下的能耗解耦决策树模型

图 11 制热工况下能耗解耦决策树模型

表 8 制冷工况下决策树模型的混淆矩阵

表 9 制热工况下决策树模型的混淆矩阵

表 10 制冷工况下随机森林模型的混淆矩阵

表 11 制热工况下随机森林模型的混淆矩阵

表 12 随机森林模型的制冷、制热工况统计指标？

表 13 制冷、制热工况下各变量的基尼系数

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