Space heating with gas as the primary fuel is a dominant contributor of residential energy consumption in UK and hence important for achieving UK's 2050 carbon targets. Whilst most cities in the UK have reduced their emissions per capita, there is a large variation of domestic gas consumption within cities. Past research suggests that the variations in gas consumption are as much as function of the quality of built environment, as it is of wider socio-economic-demographic features of households. Their combined influence on variations of residential heating consumption is however not well understood. This paper proposes a novel two-layer clustering framework to address this gap. The proposed framework constitutes of Gaussian Mixture Models and Hierarchical clustering and is illustrated through the analysis of residential gas consumption across London. Results show eight clusters of London Lower Super Output Areas (LSOAs) explained along 18 dimensions that include built-environment, social-economic and demographic information. These 8 clusters are used as variables to further cluster four groups of local authorities. The distinct gas consumption related properties of the resulting clusters explain the sources of variations in gas consumption across clusters and indicate possible directions for future fine-tuning of local policies.

以天然气为主要燃料的空间供暖是英国住宅能源消耗的主要贡献者，因此对于实现英国的2050年碳排放目标至关重要。尽管英国大多数城市均降低了人均排放量，但城市内部的国内天然气消耗量却存在很大差异。过去的研究表明，燃气消耗的变化与建筑环境质量的影响一样大，因为它具有更广泛的家庭社会经济人口统计学特征。然而，它们对住宅供热消耗变化的综合影响尚不清楚。本文提出了一种新颖的两层群集框架来解决这一差距。拟议的框架由高斯混合模型和层次聚类构成，并通过分析伦敦的住宅用气量进行了说明。结果显示，伦敦低端超级产出区（LSOA）的八个集群沿18个维度进行了解释，包括建筑环境，社会经济和人口统计学信息。这8个群集被用作变量，以进一步群集四组地方当局。所产生的集群的与天然气消耗相关的不同特性解释了集群之间天然气消耗变化的原因，并为未来对地方政策进行微调指明了可能的方向。这8个群集被用作变量，以进一步群集四组地方当局。所产生的集群的与天然气消耗相关的不同特性解释了集群之间天然气消耗变化的原因，并为未来对地方政策进行微调指明了可能的方向。这8个群集被用作变量，以进一步群集四组地方当局。所产生的集群的与天然气消耗相关的不同特性解释了集群之间天然气消耗变化的原因，并为未来对地方政策进行微调指明了可能的方向。