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Abstract：
Addressing the challenges of suboptimal model performance and excessive parameters and operations in the optimization of energy storage power plants utilizing Graph Convolutional Network (GCN), this paper introduces a novel approach - the packet-switched graph convolutional network. Initially, a GCN extreme learning machine is established. Drawing inspiration from this solid foundation, we have innovatively crafted a group exchange graph convolution module. This module leverages group graph convolution techniques to amalgamate unique node feature information, tailored to diverse topology graph matrices based on various groupings. This innovative approach ensures that information flows freely and effectively among distinct groupings. Furthermore, we have designed a cutting-edge timing depth separation convolution module, comprising two innovative components. The first component introduces timing depth separation convolution, revolutionizing the original timing convolution module. The second component, the packet-switching graph convolutional network, revolutionizes the time sequence depth separation convolution process. It achieves this by employing 1 × 1 convolutional layers between different feature fusion packets, enabling seamless information exchange between distinct packets. Experimental results demonstrate the efficacy of the proposed model, with root mean square error (RMSE) metrics and root mean square error (MAE) metrics for single-step prediction reaching 46.08 and 26.22 at 60 min, respectively. In multi-step testing, the proposed model exhibits a 14.71 % reduction in RMSE error at the 15-min scale and a 9.29 % reduction at the 60-min scale compared to the benchmark model. This performance improvement enhances the operational efficiency and reliability of the energy storage plant, particularly under dynamic changes in the time series.
摘要：
在利用图卷积网络（GCN）优化储能发电厂时，解决了模型性能欠佳，参数和操作过多的挑战，本文介绍了一种新的方法——分组交换图卷积网络。最初，建立了GCN极限学习机。从这个坚实的基础中汲取灵感，我们创新地制作了一个群交换图卷积模块。该模块利用组图卷积技术来合并独特的节点特征信息，根据各种分组为各种拓扑图矩阵量身定制。这种创新方法确保信息在不同的群体之间自由有效地流动。此外，我们设计了一个前沿的定时深度分离卷积模块，包括两个创新的组成部分。第一个组件引入时序深度分离卷积，彻底改变了原来的定时卷积模块。第二部分,分组交换图卷积网络，彻底改变了时间序列深度分离卷积过程。它通过在不同特征融合包之间采用1×1卷积层来实现这一点，实现不同数据包之间的无缝信息交换。实验结果证明了该模型的有效性,单步预测的均方根误差(RMSE)指标和均方根误差(MAE)指标在60分钟时达到46.08和26.22，分别。在多步骤测试中，与基准模型相比，所提出的模型在15分钟范围内的RMSE误差减少了14.71％，在60分钟范围内的RMSE误差减少了9.29％。这种性能改进提高了储能设备的运行效率和可靠性，特别是在时间序列的动态变化下。
