专栏名称: AEii国际应用能源
发布应用能源领域资讯,介绍国际应用能源创新研究院工作,推广应用能源优秀项目,增进应用能源领域合作
目录
相关文章推荐
gooood谷德设计网  ·  上海招聘 | SLT设计咨询 – ... ·  昨天  
gooood谷德设计网  ·  葡萄牙SA住宅 ·  2 天前  
gooood谷德设计网  ·  意大利Teatro Borsoni新剧院 ·  2 天前  
51好读  ›  专栏  ›  AEii国际应用能源

【Advances in Applied Energy】明天要耗多少电?一个精准、自适应、且可解释的理论指导短期电力负荷预测模型

AEii国际应用能源  · 公众号  ·  · 2023-07-03 18:30

正文

原文信息:

An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge

原文链接:

https://www.sciencedirect.com/science/article/pii/S2666792423000215

近日,东方理工高等研究院、上海交通大学和合肥工业大学等单位的研究者在Advances in Applied Energy上发表了题为“An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge”的研究,针对电力负荷日前预测问题,提出了融合领域知识和自适应学习的新模型,在保证模型精度的同时,降低数据需求,提升模型鲁棒性和计算效率,利于在实际场景中的应用。

Highlights

(1) Constructed a novel knowledge and data dual-driven approach (Adaptive-TgDLF) that makes full use of human knowledge and advanced deep learning techniques .

(2) Employed adaptive learning to utilize load data at various locations and times, which improves the generalization ability of model .

(3) Proposed a method to mine interpretability of the deep-learning model for load forecasting via attention matrix .

(4) The proposed model is stronger (being 16% more accurate), more robust (the performance of the proposed model with 50% weather noise is the same as that of the previous efficient model without weather noise), easier to train (saving more than half of the training time), and requires less data .

电力作为最主要的二次能源,在当今社会中至关重要。精确的电力负荷预测有助于更好地安排发电规划,进而节省能源。本文提出了一种自适应的深度学习负荷预测框架Adaptive-TgDLF,实现了Transformer和领域知识的融合。Adaptive-TgDLF引入了深度学习模型Transformer和自适应学习方法(包括针对不同区域的迁移学习和不同时间的在线学习),可以捕获负荷序列的长期依赖性,更适用于实际场景中数据稀缺和数据分布变化的问题。

在理论指导框架下,电力负荷可以被分解为无量纲趋势和局部波动。无量纲趋势反映了不同区域负荷的固有模式,而局部波动则是由外部驱动力决定的。自适应学习可以处理空间和时间上的负荷变化,并充分利用不同空间和时间的电力负荷数据来更有效地训练模型。

针对不同区域的交叉验证实验表明,Adaptive-TgDLF比前一版本的TgDLF模型准确率提高了约16%,并节省了一半以上的训练时间。同时Adaptive-TgDLF具有更高的鲁棒性,在天气数据具有50%噪声的情况下,其准确率与没有噪声的TgDLF模型相同。我们还初步挖掘了Adaptive-TgDLF中Transformer模型的可解释性,这有助于提炼更好的领域知识并指导机器学习模型的训练。在自适应学习方法方面,实验证明迁移学习有助于加速模型收敛,能够减半所需的训练次数,而在线学习使模型能够自适应电力负荷模式所发生的变化,提升模型的结果

Abstr act

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 an adaptive deep-learning load forecasting framework by integrating Transformer and domain knowledge (Adaptive-TgDLF). Adaptive-TgDLF introduces the deep-learning model Transformer and adaptive learning methods (including transfer learning for different locations and online learning for different time periods), which captures the long-term dependency of the load series, and is more appropriate for realistic scenarios with scarce samples and variable data distributions.Under the theory-guided framework, the electrical load is divided into dimensionless trends and local fluctuations. The dimensionless trends are considered as the inherent pattern of the load, and the local fluctuations are considered to be determined by the external driving forces. Adaptive learning can cope with the change of load in location and time, and can make full use of load data at different locations and times to train a more efficient model. Cross-validation experiments on different districts show that Adaptive-TgDLF is approximately 16% more accurate than the previous TgDLF model and saves more than half of the training time. Adaptive-TgDLF with 50% weather noise has the same accuracy as the previous TgDLF model without noise, which proves its robustness.We also preliminarily mine the interpretability of Transformer in Adaptive-TgDLF, 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, and online learning enables the model to achieve better results on the changing load .

Keywords

load forecasting; deep-learning; domain knowledge; transfer learning; online learning; interpretability

Graphics

图1:Adaptive-TgDLF示意图,包含由领域知识确定的无量纲趋势以及由Transformer模型预测的局部波动,以及使用自适应学习去应对负荷在空间和时间上的变化

图2:丰台区负荷真实值(黑色)与Adaptive-TgDLF预测值(红线)对比图

图3:(房山,门头沟)区域组迁移学习效果图,蓝色实线代表非迁移学习,其他颜色实线代表采用不同策略的迁移学习

图4:丰台、平谷和房山区域在线学习效果图







请到「今天看啥」查看全文