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Day-ahead electricity price forecasting via the application of artificial neural network based models

机译:基于人工神经网络模型的日间电价预测

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Traditionally, short-term electricity price forecasting has been essential for utilities and generation companies. However, the deregulation of electricity markets created a competitive environment and the introduction of new market participants, such as the retailers and aggregators, whose economic viability and profitability highly depends on the spot market price patterns. The aim of this study is to examine artificial neural network (ANN) based models for Day-ahead price forecasting. Specifically, the models refer to the sole application of ANNs or to hybrid models, where the ANN is combined with clustering algorithm. The training data are clustered in homogenous groups and for each cluster, a dedicated forecaster is employed. The proposed models are characterized by comprehensive operation and by high level of flexibility; different inputs can be taken under consideration and different ANN topologies can be examined. The models are tested on a data set that consists of atypical price patterns and many outliers. This approach makes the price forecasting problem a more challenging task, providing evidence that the proposed models can be considered as useful and robust forecasting tools to the actual needs of market participants, including the traditional generation companies and self-producers, but also the retailers/suppliers and aggregators. (C) 2016 Elsevier Ltd. All rights reserved.
机译:传统上,短期电价预测对于公用事业和发电公司至关重要。但是,电力市场的放松管制创造了竞争环境,并引入了新的市场参与者,例如零售商和聚合商,其经济可行性和盈利能力在很大程度上取决于现货市场的价格格局。这项研究的目的是检查基于人工神经网络(ANN)的日间价格预测模型。具体而言,模型是指ANN的唯一应用或混合模型,其中ANN与聚类算法结合在一起。训练数据按均匀的组进行聚类,并且对于每个聚类,将使用专用的预测器。提出的模型的特点是操作全面,灵活性高。可以考虑不同的输入,可以检查不同的ANN拓扑。在包含非典型价格模式和许多异常值的数据集上对模型进行了测试。这种方法使价格预测问题成为更具挑战性的任务,提供的证据表明,所提出的模型可以被视为满足市场参与者(包括传统发电公司和自家生产者,以及零售商/供应商和整合商。 (C)2016 Elsevier Ltd.保留所有权利。

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