[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.
LIU Xinnan
,
SONG Laihao
,
JI Yingbo
. Establishing energy usage profiles of urban residential households: A case study of the Beijing-Tianjin-Hebei region[J]. Journal of Tsinghua University(Science and Technology), 2024
, 64(8)
: 1482
-1491
.
DOI: 10.16511/j.cnki.qhdxxb.2024.22.027
[1] 中国建筑节能协会,重庆大学城乡建设与发展研究院.中国建筑能耗与碳排放研究报告(2022年)[J].建筑, 2023(2):57-69. China Association of Building Energy Efficiency; Institute of Urban-Rural Construction and Development, Chongqing University. Research report on building energy consumption and carbon emissions in China (2022)[J]. Construction and Architecture, 2023(2):57-69.(in Chinese)
[2] 王春艳,张景翔,龙洁,等.基于面板数据回归模型的家庭水-能消费时空特征与影响因素[J].清华大学学报(自然科学版), 2022, 62(3):614-626. WANG C Y, ZHANG J X, LONG J, et al. Panel data regression model for identifying the spatiotemporal characteristics and key factors influencing household water-energy consumption[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(3):614-626.(in Chinese)
[3] KUANG B, SCHELLY C, OU G, et al. Data-driven analysis of influential factors on residential energy end-use in the US[J]. Journal of Building Engineering, 2023, 75:106947.
[4] IRAGANABOINA N C, ELURU N. An examination of factors affecting residential energy consumption using a multiple discrete continuous approach[J]. Energy and Buildings, 2021, 240:110394.
[5] LEE S J, SONG S Y. Time-series analysis of the effects of building and household features on residential end-use energy[J]. Applied Energy, 2022, 312:118722.
[6] HU S, YAN D, GUO S Y, et al. A survey on energy consumption and energy usage behavior of households and residential building in urban China[J]. Energy and Buildings, 2017, 148:366-378.
[7] ABRAHAMSE W, STEG L. How do socio-demographic and psychological factors relate to households' direct and indirect energy use and savings?[J]. Journal of Economic Psychology, 2009, 30(5):711-720.
[8] 周成栋,刘猛,李子桥,等.家庭结构对住宅空调使用时间的影响及关联性分析[J].土木与环境工程学报(中英文), 2024, 46(2):188-196. ZHOU C D, LIU M, LI Z Q, et al. The influence of family structure on operation time of residential air-conditioning and correlation analysis[J]. Journal of Civil and Environmental Engineering, 2024, 46(2):188-196.(in Chinese)
[9] HAO P, YIN S M, WANG D, et al. Exploring the influencing factors of urban residential electricity consumption in China[J]. Energy for Sustainable Development, 2023, 72:278-289.
[10] BEDIR M, HASSELAAR E, ITARD L. Determinants of electricity consumption in Dutch dwellings[J]. Energy and Buildings, 2013, 58:194-207.
[11] 王丞.我国绿色建筑和低碳建筑评价体系的发展比较及优化建议[J].建筑科学, 2023, 39(2):235-244. WANG C. Comparison of the development for green building and low-carbon building evaluation systems in China and optimization suggestions[J]. Building Science, 2023, 39(2):235-244.(in Chinese)
[12] 徐芳,应洁茹.国内外用户画像研究综述[J].图书馆学研究, 2020(12):7-16. XU F, YING J R. A review of user profile research at home and abroad[J]. Researches on Library Science, 2020(12):7-16.(in Chinese)
[13] 杨敏,李宏伟,任怡凤,等.基于旅客异质性画像的公铁联程出行方案推荐方法[J].清华大学学报(自然科学版), 2022, 62(7):1220-1227. YANG M, LI H W, REN Y F, et al. Road-rail intermodal travel recommendations based on a passenger heterogeneity profile[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(7):1220-1227.(in Chinese)
[14] 江亿,彭琛,胡姗.中国建筑能耗的分类[J].建设科技, 2015(14):22-26. JIANG Y, PENG C, HU S. Classification of building energy consumption in China[J]. Construction Science and Technology, 2015(14):22-26.(in Chinese)
[15] 清华大学建筑节能研究中心.中国建筑节能年度发展研究报告2021[M].北京:中国建筑工业出版社, 2021. Building Energy Conservation Research Center, Tsinghua University. 2021 Annual report on China building energy efficiency[M]. Beijing:China Architecture&Building Press, 2021.(in Chinese)
[16] TETE K H S, SORO Y M, SIDIBÉ S S, et al. Urban domestic electricity consumption in relation to households'lifestyles and energy behaviours in Burkina Faso:Findings from a large-scale, city-wide household survey[J]. Energy and Buildings, 2023, 285:112914.
[17] 杨俊闯,赵超. K-Means聚类算法研究综述[J].计算机工程与应用, 2019, 55(23):7-14, 63. YANG J C, ZHAO C. Survey on K-Means clustering algorithm[J]. Computer Engineering and Applications, 2019, 55(23):7-14, 63.(in Chinese)
[18] 杨善林,李永森,胡笑旋,等. k-means算法中的k值优化问题研究[J].系统工程理论与实践, 2006(2):97-101. YANG S L, LI Y S, HU X X, et al. Optimization study on k value of k-means algorithm[J]. Systems Engineering-Theory&Practice, 2006(2):97-101.(in Chinese)
[19] OFETOTSE E L, ESSAH E A, YAO R M. Evaluating the determinants of household electricity consumption using cluster analysis[J]. Journal of Building Engineering, 2021, 43:102487.
