Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

中文翻译：

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n用于多步提前短期负荷预测的基于注意力的多层GRU模型

近来，多步预测在电力负荷预测中引起了广泛的关注，因为它可以处理从现在起一天的由火和热浪等各种事件引起的功耗的突然变化。另一方面，包括长短期记忆和门控循环单元（GRU）网络在内的循环神经网络（RNN）可以很好地反映先前的点以预测当前点。由于这种特性，它们已被广泛用于多步提前预测。GRU模型简单易行；但是，它的预测性能受到限制，因为它会平等地考虑所有输入变量。在本文中，我们提出了一种基于注意力的GRU的短期负荷预测模型，将其更多地集中在关键变量上，并证明这可以实现显着的性能改进，尤其是当RNN的输入序列较长时。通过广泛的实验，我们证明了在建筑物级功耗预测中，所提出的模型优于其他最近的多步超前预测模型。