用于配电网短期负荷预测的混合深度学习模型,集成了渠道关注机制

IF 2 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Boyu Qin, Xin Gao, Tao Ding, Fan Li, Dong Liu, Zhe Zhang, Ruanming Huang
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引用次数: 0

摘要

由于非线性电力负荷的随机性和系统运行模式的多变性,优化短期负荷预测性能是一项挑战。现有方法普遍忽视了如何合理有效地将各种方法之间的优势互补结合起来,未能从负荷数据中捕捉到足够的内部信息,从而导致精度降低。为实现准确高效的短期负荷预测,本文提出了基于卷积神经网络(CNN)、门控递归单元(GRU)和通道注意机制的整体实现框架。首先将 CNN 和 GRU 结合起来,以充分提取高度复杂的动态特性,并学习负荷序列的时间顺应关系。在 CNN-GRU 网络的基础上,引入通道注意机制,进一步减少历史信息的损失,增强重要特征的影响。然后,提出了基于 CNN-GRU-Attention 网络的短期负荷预测总体框架,并揭示了各阶段之间的耦合关系。最后,在现实的配电网负荷数据集上实现了所开发的框架,实验结果验证了所提方法的有效性。与最先进的模型相比,CNN-GRU-Attention 模型在不同的评价指标上均表现优异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A hybrid deep learning model for short-term load forecasting of distribution networks integrating the channel attention mechanism

A hybrid deep learning model for short-term load forecasting of distribution networks integrating the channel attention mechanism

Optimizing short-term load forecasting performance is a challenge due to the randomness of nonlinear power load and variability of system operation mode. The existing methods generally ignore how to reasonably and effectively combine the complementary advantages among them and fail to capture enough internal information from load data, resulting in accuracy reduction. To achieve accurate and efficient short-term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the highly complicated dynamic characteristics and learn time compliance relationships of load sequence. Based on CNN-GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important characteristics. Then, the overall framework of short-term load forecasting based on CNN-GRU-Attention network is proposed, and the coupling relationship between each stage is revealed. Finally, the developed framework is implemented on realistic load dataset of distribution networks, and the experimental results verify the effectiveness of the proposed method. Compared with the state-of-the-art models, the CNN-GRU-Attention model outperforms in different evaluation metrics.

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来源期刊
Iet Generation Transmission & Distribution
Iet Generation Transmission & Distribution 工程技术-工程:电子与电气
CiteScore
6.10
自引率
12.00%
发文量
301
审稿时长
5.4 months
期刊介绍: IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix. The scope of IET Generation, Transmission & Distribution includes the following: Design of transmission and distribution systems Operation and control of power generation Power system management, planning and economics Power system operation, protection and control Power system measurement and modelling Computer applications and computational intelligence in power flexible AC or DC transmission systems Special Issues. Current Call for papers: Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf
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