论文标题
中期电力载荷预测的混合剩余LSTM末端末端平滑模型
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting
论文作者
论文摘要
这项工作为中期负载预测提供了混合和分层深度学习模型。该模型结合了指数平滑（ETS），高级长期记忆（LSTM）和结合。 ETS动态提取每个单个时间序列的主要组成部分，并使模型能够学习其表示形式。多层LSTM配备了扩张的复发连接和从较低层的空间快捷方式路径，可让模型更好地捕获长期的季节性关系并确保更有效的训练。 LSTM和ETS的常见学习程序，具有受惩罚的弹球损失，可同时优化数据表示和预测性能。此外，在三个层次上进行结合确保了强大的正规化。对35个欧洲国家 /地区的每月电力需求时间序列进行的一项仿真研究证实了该模型的高性能及其在基于机器学习的基于Arima和ETS（例如Arima和ETS）以及最先进的模型的经典模型中的竞争力。
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting. The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LSTM) and ensembling. ETS extracts dynamically the main components of each individual time series and enables the model to learn their representation. Multi-layer LSTM is equipped with dilated recurrent skip connections and a spatial shortcut path from lower layers to allow the model to better capture long-term seasonal relationships and ensure more efficient training. A common learning procedure for LSTM and ETS, with a penalized pinball loss, leads to simultaneous optimization of data representation and forecasting performance. In addition, ensembling at three levels ensures a powerful regularization. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model and its competitiveness with classical models such as ARIMA and ETS as well as state-of-the-art models based on machine learning.
