Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency. Electricity demand profiles can vary drastically from one region to another on diurnal, seasonal and yearly scale. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, specially when the training data is limited, is a big challenge. This paper presents a deep learning architecture for short-term load forecasting based on bidirectional sequential models in conjunction with feature engineering that extracts the hand-crafted derived features in order to aid the model for better learning and predictions. In the proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), the raw input and hand-crafted features are trained at separate levels and then their respective outputs are combined to make the final prediction. The efficacy of the proposed methodology is evaluated on datasets from five countries with completely different patterns. The results demonstrate that the proposed technique is superior to the existing state of the art.

中文翻译：

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使用双向顺序模型和特征工程的小型数据集短期负荷预测

电力负荷预测使电网运营商能够最佳地实现智能电网最重要的功能，例如需求响应和能效。电力需求概况在一个地区，另一个地区的日，季节和年度规模上可能会发生巨大变化。因此，设计一种负荷预测技术，尤其是在训练数据有限的情况下，能够在各种数据集上产生最佳估计是一项巨大的挑战。本文提出了一种基于双向顺序模型并结合特征工程的短期负荷预测的深度学习架构，特征工程提取了手工派生的特征，以帮助模型更好地学习和预测。在提议的架构中，称为深度衍生特征融合（DeepDeFF），原始输入和手工制作的特征在不同的级别上进行训练，然后将它们各自的输出进行组合以进行最终预测。拟议方法的有效性是在来自五个国家的模式完全不同的数据集上进行评估的。结果表明，所提出的技术优于现有技术。