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Application of clustering technique to electricity customer classification for load forecasting

机译:聚类技术在电力用户分类负荷预测中的应用

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With the development of smart grid and opening-up progress of the electricity market, Customer Relationship Management (CRM) plays a more and more important role in the power electric industry. Conducting medium and long term consumption pattern analysis of major customers can help the electricity providers grasp the changing trends of the future consumption, and thus better formulate the dedicated tariff offers and provide professional services according to various consumer demands. However, it's computationally costly and impossible to conduct such analysis for every single customer. To overcome this complexity, this paper aims to provide an effective solution to group customers into certain number of categories with similar electrical behavior by utilizing clustering techniques. By combining distance and correlation, a novel clustering validity indicator is proposed to evaluate the effectiveness of clustering procedure, which is subsequently helpful for choosing algorithms and the optimum number of clusters. Eventually, a case study has been conducted with electricity market data including various electricity customers from different industrial fields. Two frequently-used clustering algorithms have been employed to illustrate the feasibility of the proposed approach.
机译:随着智能电网的发展和电力市场的开放进程,客户关系管理(CRM)在电力行业中扮演着越来越重要的角色。进行主要客户的中长期消费模式分析,可以帮助电力供应商掌握未来消费的变化趋势,从而更好地制定专用电价,并根据消费者的各种需求提供专业服务。但是,对每个客户进行这样的分析在计算上是昂贵的,并且是不可能的。为了克服这种复杂性,本文旨在提供一种有效的解决方案,通过利用聚类技术将客户分为具有类似电气行为的一定数量的类别。通过将距离和相关性相结合,提出了一种新的聚类有效性指标来评价聚类过程的有效性,这对于选择算法和优化聚类数量有帮助。最终,利用电力市场数据进行了案例研究,其中包括来自不同工业领域的各种电力客户。两种常用的聚类算法已被用来说明所提出方法的可行性。

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