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

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 residential households.The t-distributed stochastic neighbor embedding(TSNE)dimensionality reduction method reduced multidimensional data to two dimensions and visualized the distribution of different urban residential households,demonstrating the scientific approach used in this study.[Results](1)The optimal subset of energy usage profile indicators for urban residential households in the Beijing-Tianjin-Hebei region includes eight indicators:household population,building area,house use pattern,number of appliances,air conditioning behavior,annual electricity consumption,annual gas consumption,and number of solar energy equipment.(2)The optimal number of clusters for the household energy dataset is four.(3)Using the optimal subset,the k-means clustering algorithm classifies urban residential households in the Beijing-Tianjin-Hebei region into energy utilization quality pursuit,energy-saving potential,energy utilization regularity,and energy conservation and environmental protection types.[Conclusions]By analyzing the characteristics of the four types of urban residential household energy usage profiles in the Beijing-Tianjin-Hebei region,we examine the energy-saving potential of households with energy utilization quality pursuit,energy-saving potential,and energy utilization regularity types and propose energy-saving recommendations.We provide new insights for relevant departments to understand the energy usage characteristics of urban residential households in the Beijing-Tianjin-Hebei region and develop accurate energy conservation strategies based on household types.