论文标题
TGDLF2.0：通过变压器和转移学习的理论指导对电气负载预测的深度学习
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
论文作者
论文摘要
电能在当今社会中至关重要。准确的电气载荷预测有益于更好地安排发电和节省电能。在本文中，我们提出了理论引导的深度学习载荷预测2.0（TGDLF2.0）来解决此问题，这是通过合奏长短期内存（TGDLF）的理论导入深度学习框架的改进版本。 TGDLF2.0基于将电载荷分为无量纲趋势和局部波动的深度学习模型变压器和传递学习，从而实现了域知识的利用，捕获了负载序列的长期依赖性，并且更适合于稀缺的样本进行现实的场景。在不同地区进行的交叉验证实验表明，TGDLF2.0比TGDLF准确16％，并节省了超过一半的训练时间。 TGDLF2.0具有50％天气噪声的TGDLF2.0与没有噪声的TGDLF具有相同的精度，这证明了其稳健性。我们还初步挖掘了TGDLF2.0中变压器的解释性，这可能为更好的理论指导提供了未来的潜力。此外，实验表明，转移学习可以在训练时期数量的一半中加速模型的收敛，并取得更好的性能。
Electrical energy is essential in today's society. Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy. In this paper, we propose theory-guided deep-learning load forecasting 2.0 (TgDLF2.0) to solve this issue, which is an improved version of the theory-guided deep-learning framework for load forecasting via ensemble long short-term memory (TgDLF). TgDLF2.0 introduces the deep-learning model Transformer and transfer learning on the basis of dividing the electrical load into dimensionless trends and local fluctuations, which realizes the utilization of domain knowledge, captures the long-term dependency of the load series, and is more appropriate for realistic scenarios with scarce samples. Cross-validation experiments on different districts show that TgDLF2.0 is approximately 16% more accurate than TgDLF and saves more than half of the training time. TgDLF2.0 with 50% weather noise has the same accuracy as TgDLF without noise, which proves its robustness. We also preliminarily mine the interpretability of Transformer in TgDLF2.0, which may provide future potential for better theory guidance. Furthermore, experiments demonstrate that transfer learning can accelerate convergence of the model in half the number of training epochs and achieve better performance.
