城市家庭水、能消费占城市总用水量和用能量的37%和18%以上。城市家庭水-能消费关联密切，具有显著的家庭尺度时间变化特征和城市尺度空间分布特征。深入认识城市家庭水-能耦合系统特征是实现精细化城市水-能协同管理的重要前提。首先，利用智能传感器监测手段，对样本家庭的水、能消费活动进行连续长时间监测，获取微观家庭逐小时水、能消费量数据。第二，利用大数据分析方法识别居民家庭消费活动，运用物质流分析思想构建水流、能流过程，识别水-能耦合关系节点和特征、水-能消费时变特征、消费活动与家庭属性之间的相关性。第三，融合遥感影像、兴趣点、人口普查等多源数据，提取住宅建筑信息，估算城市家庭空间分布。最后，将微观家庭消费活动及水-能消费合成至城市家庭消费活动及水-能消费量，并分析居民消费活动、家庭空间分布等不确定因素对结果的影响。研究结果可为实现精细化城市水-能协同管理提供高时空精度的数据支撑和重要的研究基础。

Urban households contribute over 37% and 18% of urban water and energy consumptions, respectively. The water and energy consumptions of households are highly correlated, as evident from the significant temporal variation at the household level and spatial variation at the city level. In-depth understanding of the household water-energy nexus is crucial to the achievement of the refined urban water-energy collaborative management. Firstly, the information on household hourly water and energy consumptions will be collected through continuous long-term monitoring with smart sensors. Secondly, consumption activities of household will be recognized using big data processing method, and the flows of water and energy will be developed with the concept of material flow, in order to identify the critical points and characteristics of water-energy nexus, spatiotemporal characteristics of water-energy consumption and relationships between consumption activities and household attributes. Thirdly, the information on urban residential buildings and spatial distribution of households will be extracted based on multi-source data, including remote sensing data, points of interests data, and population census data. Lastly, the disaggregate household-level consumption activities and water-energy consumptions will be aggregated at the city-level. Furthermore, the potential influences of household consumption activities and geographical distribution of households on the outcomes will be examined. The results can provide consolidate support for refined urban water-energy collaborative management.