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Real-Time Stream Mining Electric Power Consumption Data Using Hoeffding Tree with Shadow Features

机译:使用带阴影特征的Hoeffding树实时流挖掘电力消耗数据

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摘要

Many energy load forecasting models have been established from batch-based supervised learning models where the whole data must be loaded to learn. Due to the sheer volumes of the accumulated consumption data which arrive in the form of continuous data streams, such batch-mode learning requires a very long time to rebuild the model. Incremental learning, on the other hand, is an alternative for online learning and prediction which learns the data stream in segments. However, it is known that its prediction performance falls short when compared to batch learning. In this paper, we propose a novel approach called Shadow Features (SF) which offer extra dimensions of information about the data streams. SF are relatively easy to compute, suitable for lightweight online stream mining.
机译:许多能量负荷预测模型已经从基于批量的监督学习模型建立,其中必须加载整个数据以学习。由于占用的累积消耗数据的庞大卷,该累积消耗数据到达连续数据流的形式,这种批量模式学习需要很长时间才能重建模型。另一方面,增量学习是在线学习和预测的替代方案,其在段中学习数据流。然而,众所周知,与批量学习相比,其预测性能差异很短。在本文中,我们提出了一种称为阴影特征(SF)的新方法,其提供有关数据流的额外信息。 SF相对容易计算,适用于轻量级的在线流挖掘。

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