In Germany and many other countries the energy market has been subject to significant changes. Instead of only a few large-scale producers that serve aggregated consumers, a shift towards regenerative energy sources is taking place. Energy systems are increasingly being made more flexible by decentralised producers and storage facilities, i.e. many consumers are also producers. The aggregation of producers form another type of power plants: a virtual power plant. On the basis of aggregated production and consumption, virtual power plants try to make decisions under the conditions of the electricity market or the grid condition. They are influenced by many different aspects. These include the current feed-in, weather data, or the demands of the consumers. Clearly, a virtual power plant is focusing on developing strategies to influence and optimise these factors. To accomplish this, many data sets can and should be analysed in order to interpret and create forecasts for energy systems. Time series based analytics are therefore of particular interest for virtual power plants. Classifying the different time series according to generators, consumers or customer types simplifies processes. In this way, scalable solutions for forecasts can be found. However, one has to first find the according clusters efficiently. This paper presents a method for determining clusters of time series. Models are adapted and model-based clustered using ARIMA parameters and an individual quality measure. In this way, the analysis of generic time series can be simplified and additional statements can be made with the help of graphical evaluations. To facilitate large scale virtual power plants, the presented clustering workflow is prepared to be applied on big data capable platforms, e.g. time series stored in Apache Cassandra, analysed through an Apache Spark execution framework. The procedure is shown here using the example of the Day-Ahead prices of the electricity market for 2018.

中文翻译：

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聚类时间序列应用于能源市场

在德国和许多其他国家，能源市场发生了重大变化。不仅只有少数为聚集的消费者提供服务的大型生产商，还朝着再生能源的方向转变。分散的生产者和存储设施使能源系统变得越来越灵活，即许多消费者也是生产者。生产者的聚集形成了另一类发电厂：虚拟发电厂。在总的生产和消耗的基础上，虚拟电厂尝试在电力市场或电网条件下做出决策。它们受到许多不同方面的影响。这些包括当前的馈电，天气数据或消费者的需求。显然，虚拟电厂正在集中精力开发影响和优化这些因素的策略。为此，可以并且应该分析许多数据集，以解释和创建能源系统的预测。因此，基于时间序列的分析对于虚拟电厂尤为重要。根据生成器，消费者或客户类型对不同的时间序列进行分类可以简化流程。这样，可以找到用于预测的可扩展解决方案。但是，必须首先有效地找到相应的群集。本文提出了一种确定时间序列聚类的方法。使用ARIMA参数和单独的质量度量对模型进行调整并基于模型进行聚类。通过这种方式，可以简化通用时间序列的分析，并且可以借助图形评估来做出其他说明。为了促进大型虚拟电厂的运行，准备好将提供的集群工作流程应用于具有大数据功能的平台，例如通过Apache Spark执行框架分析的存储在Apache Cassandra中的时间序列。此处以2018年电力市场的日前价格示例显示了此过程。