The wide adoption of smart meters makes residential load data available and thus improves the understanding of the energy consumption behavior. Many existing studies have focused on smart-meter data analysis, but the drivers of energy consumption behaviors are not well understood. This paper aims to characterize and estimate users' load patterns based on their demographic and socioeconomic information. We adopt the symbolic aggregate approximation (SAX) method to process the load data and use the K-Means method to extract key load patterns. We develop a deep neural network (DNN) to analyze the relationship between users' load patterns and their demographic and socioeconomic features. Using real-world load data, we validate our framework and demonstrate the connections between load patterns and household demographic and socioeconomic features. We also take two regression models as benchmarks for comparisons.

中文翻译：

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通过家庭人口和社会经济因素表征住宅负荷模式

智能电表的广泛采用使住宅负载数据可用，从而提高了对能源消耗行为的理解。现有的许多研究都集中在智能电表数据分析上，但对能源消费行为的驱动因素尚不清楚。本文旨在根据用户的人口统计和社会经济信息来表征和估计用户的负载模式。我们采用符号聚合近似（SAX）方法处理负载数据，并使用 K-Means 方法提取关键负载模式。我们开发了一个深度神经网络 (DNN) 来分析用户的负载模式与其人口统计和社会经济特征之间的关系。使用真实世界的负载数据，我们验证了我们的框架并展示了负载模式与家庭人口统计和社会经济特征之间的联系。我们还将两个回归模型作为比较的基准。