城镇住宅家庭用能画像构建——以京津冀地区为例  

Establishing energy usage profiles of urban residential households: A case study of the Beijing-Tianjin-Hebei region

作　　者：刘心男 宋来昊 纪颖波 LIU Xinnan;SONG Laihao;JI Yingbo(School of Civil Engineering,North China University of Technology,Beijing 100144,China)

机构地区：[1]北方工业大学土木工程学院,北京100144

出　　处：《清华大学学报（自然科学版）》2024年第8期1482-1491,共10页Journal of Tsinghua University(Science and Technology)

基　　金：国家重点研发计划(2021YFF0602000);北方工业大学有组织科研项目(110051360023XN278-02)。

摘　　要：为精准引导城镇住宅家庭节能,该文基于用户画像理论从多维度视角构建了城镇住宅家庭用能画像模型。以京津冀地区城镇住宅家庭为研究对象,基于文献研究从家庭属性、建筑特征、家电设备、用能行为、能源消耗以及可再生能源使用6个维度构建了家庭用能画像标签体系,采用调查问卷及半结构化访谈方法获取351份家庭用能数据。基于标签平均轮廓系数,采用向后特征选择法确定画像标签最优子集,采用k-means算法对城镇住宅家庭聚类分析。结果表明:城镇住宅家庭用能画像标签最优子集包括家庭人口、建筑面积、房屋使用方式、家电设备数量、空调行为、年用电量、年用气量和太阳能设备8个标签;可将京津冀地区城镇住宅家庭划分为用能品质型、节能潜力型、用能规律型和节能环保型4类。该研究结果可为制定家庭节能精准引导策略提供理论参考。[Objective]With improving living standards,energy consumption in urban residential households has continuously increased.In 2020,the energy consumption during the operational phase of buildings accounted for 21.3%of the total energy consumption in China,with the urban residential energy consumption accounting for 38.7%of the energy consumption during the operational phase of buildings.Energy usage in urban residential households is overly complex and differs considerably among households.For sustainable energy saving and emission reduction,it is important to monitor energy conservation in urban residential households by accurately analyzing and identifying user characteristics of different households.Therefore,we develop an urban residential household energy usage profile model using a multidimensional perspective of user profiling theory.[Methods]Herein,we focused on urban residential households in the Beijing-Tianjin-Hebei region by establishing a labeling system for household energy usage profiling in six dimensions:household attributes,building features,household appliances,energy usage behaviors,energy consumption,and use of renewable energy;this labeling system included 18 indicators.A total of 351 valid household energy usage datasets were collected through surveys and semistructured interviews.A comprehensive search method was used to calculate the silhouette coefficients of the 18 indicators using different numerical combinations.Backward feature selection was used to filter the indicators based on their average silhouette coefficients.This process was terminated when the insignificance of indicators led to inconsistent results across different indicator sets.Consequently,the silhouette coefficient of the household energy dataset clustering was>0.5.The remaining indicators represented the optimal subset of the household energy usage profile indicators.Finally,the optimal number of clusters k was determined using the elbow method principle.The k-means algorithm was applied to cluster analysis of urban res

关 键 词：城镇住宅家庭 家庭用能画像 K-MEANS聚类 特征选择 

分 类 号：TU241.5[建筑科学—建筑设计及理论]