With the increasingly widespread of advanced metering infrastructure, electric load clustering is becoming more essential for its great potential in analytics of consumers' energy consumption patterns and preference through data mining. Moreover, a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data, observe the characteristics of load clusters, and classify the components of the total load. This can give rise to the development of related techniques and research in the smart grid, such as demand-side response. This paper summarizes the basic concepts and the general process in electric load clustering. Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages. Afterwards, eight indices widely used to evaluate the validity of electric load clustering are described. Finally, vital applications are discussed thoroughly along with future trends including the tariff design, anomaly detection, load forecasting, data security and big data, etc.

中文翻译：

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智能电网中的电力负荷聚类：方法，应用和未来趋势

随着高级计量基础设施的日益普及，电力负载群集对其在通过数据挖掘分析消费者的能源消耗模式和偏好方面的巨大潜力变得越来越重要。此外，已经实践了各种电力负荷聚类技术来获得负荷数据的分布，观察负荷聚类的特征以及对总负荷的组成进行分类。这可以引起智能电网中相关技术和研究的发展，例如需求方响应。本文总结了电力负荷聚类的基本概念和一般过程。然后，对电力负载聚类中的几种相似性度量和五个主要类别进行了综合总结，并介绍了它们的优缺点。然后，描述了广泛用于评估电力负荷聚类有效性的八个指标。最后，对重要的应用程序以及未来的趋势进行了详尽的讨论，包括费率设计，异常检测，负载预测，数据安全性和大数据等。