Residential customers have traditionally not been treated as individual entities due to the high volatility in residential consumption patterns as well as a historic focus on aggregated loads from the utility and system feeder perspective. Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns, which can reveal important heterogeneity across different time scales, weather conditions, as well as within and across individual households. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability and the different constraints that may affect demand-response (DR) flexibility. We systematically evaluate the relationship between daily time-of-use patterns and their variability to external and internal influencing factors, including time scales of interest, meteorological conditions, and household characteristics by application of an improved version of the adaptive K-means clustering method to profile "household-days" of a summer peaking utility. We find that for this summer-peaking utility, outdoor temperature is the most important external driver of the load shape variability relative to seasonality and day-of-week. The top three consumption patterns represent approximately 50% of usage on the highest temperature days. The variability in summer load shapes across customers can be explained by the responsiveness of the households to outside temperature. Our results suggest that depending on the influencing factors, not all the consumption variability can be readily translated to consumption flexibility. Such information needs to be further explored in segmenting customers for better program targeting and tailoring to meet the needs of the rapidly evolving electricity grid.

中文翻译：

---

通过智能电表数据的每日负荷形状聚类研究住宅能源使用模式中的潜在变化动因

传统上，由于居民消费模式的高波动性以及从公用事业和系统馈线的角度来历来关注总负载，因此传统上不将居民客户视为个体实体。智能电表的大规模部署激发了越来越多的研究，以探索分类的日负荷模式，这可以揭示不同时间范围，天气条件以及单个家庭内部和各个家庭之间的重要异质性。本文旨在阐明耗电量模式表现出可变性的机制以及可能影响需求响应（DR）灵活性的不同约束。我们系统地评估每日使用时间模式及其对外部和内部影响因素的可变性之间的关系，包括感兴趣的时间尺度，气象条件和家庭特征，方法是应用改进的自适应K均值聚类方法来描述夏季高峰时段的“住户日”。我们发现，对于这个夏季高峰时段的公用事业而言，相对于季节和星期几而言，室外温度是影响负载形状变化的最重要外部因素。前三个消耗模式代表最高温度天使用量的大约50％。各个客户夏季负荷形状的变化可以通过家庭对外部温度的响应来解释。我们的结果表明，根据影响因素，并非所有的消费差异都可以轻易地转化为消费弹性。