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Modeling flexibility using artificial neural networks

机译：使用人工神经网络建模灵活性

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The flexibility of distributed energy resources (DERs) can be modeled in various ways. Each model that can be used for creating feasible load profiles of a DER represents a potential model for the flexibility of that particular DER. Based on previous work, this paper presents generalized patterns for exploiting such models. Subsequently, the idea of using artificial neural networks in such patterns is evaluated. We studied different types and topologies of ANNs for the presented realization patterns and multiple device configurations, achieving a remarkably precise representation of the given devices in most of the cases. Overall, there was no single best ANN topology. Instead, a suitable individual topology had to be found for every pattern and device configuration. In addition to the best performing ANNs for each pattern and configuration that is presented in this paper all data from our experiments is published online. The paper is concluded with an evaluation of a classification based pattern using data of a real combined heat and power plant in a smart building.
机译：分布式能源（DER）的灵活性可以通过多种方式建模。可以用于创建DER的可行负荷曲线的每个模型都代表了一个潜在的模型，可用于特定DER的灵活性。在以前的工作的基础上，本文提出了利用这种模型的通用模式。随后，评估了在这种模式下使用人工神经网络的想法。我们针对提出的实现模式和多种设备配置研究了ANN的不同类型和拓扑，在大多数情况下实现了给定设备的非常精确的表示。总体而言，没有单一的最佳ANN拓扑。相反，必须为每种模式和设备配置找到合适的单独拓扑。除了本文介绍的每种模式和配置的最佳ANN，所有来自我们实验的数据都在线发布。本文的结尾是使用智能建筑中真实的热电联产电厂数据对基于分类的模式进行评估。

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