Accurate ultra-short-term load forecasting is of great significance for real-time power generation scheduling and development of power cyber physical systems (Power CPS). However, in order to forecast the future load using the current high-dimensional, diverse and heterogeneous electric power consumption information, new challenges have been raised to the effective feature selection and the accurate load forecasting algorithms. However, very limited existing works consider the feature selection for the electric power consumption information and impacts to the thereafter load forecasting model. In view of this point, features that are critical to the load forecasting are selected using an embedded feature selection algorithm based on LightGBM to form an optimal feature set, with which a sequence to sequence (S2S) and gated recurrent unit (GRU)-based ultra-short-term load forecasting model that incorporates Bahdanau attention (BA) mechanism is presented. The S2S-GRU model is based on an encoding–decoding framework that is compatible to the input and output data series with variable lengths. By introducing the BA mechanism, loss of previous information issue of GRU can be solved. Experimental results show that first the presented feature selection algorithm can help to improve the performance of the load forecasting model. Second, the presented load forecasting model can find a compromise between the forecasting efficiency and accuracy.

中文翻译：

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基于最优特征选择和Bahdanau注意力机制的用户级超短期负荷预测模型

准确的超短期负荷预测对于实时发电调度和电力信息物理系统（Power CPS）的开发具有重要意义。然而，为了利用当前高维、多样化和异构的用电信息来预测未来的负荷，对有效的特征选择和准确的负荷预测算法提出了新的挑战。然而，非常有限的现有工作考虑了电力消耗信息的特征选择以及对后续负荷预测模型的影响。鉴于这一点，使用基于 LightGBM 的嵌入式特征选择算法选择对负载预测至关重要的特征，形成最优特征集，提出了一种基于序列到序列 (S2S) 和门控循环单元 (GRU) 的超短期负荷预测模型，该模型结合了 Bahdanau 注意力 (BA) 机制。S2S-GRU 模型基于与可变长度的输入和输出数据序列兼容的编码-解码框架。通过引入BA机制，可以解决GRU之前的信息丢失问题。实验结果表明，首先提出的特征选择算法有助于提高负荷预测模型的性能。其次，提出的负荷预测模型可以在预测效率和准确性之间找到折衷。S2S-GRU 模型基于与可变长度的输入和输出数据序列兼容的编码-解码框架。通过引入BA机制，可以解决GRU之前的信息丢失问题。实验结果表明，首先提出的特征选择算法有助于提高负荷预测模型的性能。其次，提出的负荷预测模型可以在预测效率和准确性之间找到折衷。S2S-GRU 模型基于与可变长度的输入和输出数据序列兼容的编码-解码框架。通过引入BA机制，可以解决GRU之前的信息丢失问题。实验结果表明，首先提出的特征选择算法有助于提高负荷预测模型的性能。其次，提出的负荷预测模型可以在预测效率和准确性之间找到折衷。