基于图卷积神经网络的负荷聚合商调节能力预测

董凌睿; 吴滨源

董凌睿,吴滨源.基于图卷积神经网络的负荷聚合商调节能力预测[J].电测与仪表,2025,62(6):93-101.
DONG Lingrui,WU Binyuan.Load aggregator adjustable capability forecasting based on graph convolution neural network[J].Electrical Measurement & Instrumentation,2025,62(6):93-101.

基于图卷积神经网络的负荷聚合商调节能力预测

Load aggregator adjustable capability forecasting based on graph convolution neural network

DOI：10.19753/j.issn1001-1390.2025.06.009

中文关键词: 可调节能力预测负荷聚合商图卷积神经网络需求响应

英文关键词:adjustable capability forecasting, load aggregator, graph convolution neural network, demand response

基金项目:国家电网公司科技项目（5626TC220003）

作者中文名	作者英文名	单位
董凌睿	DONG Lingrui	哈尔滨工业大学
吴滨源	WU Binyuan	国网上海市电力公司

摘要点击次数: 20

中文摘要:

在双碳战略推动与新型电力系统建设背景下，挖掘需求侧柔性资源可调节潜力助力电力系统供需平衡成为必然趋势。由于目前尚未积累起足够的实际需求响应数据，负荷聚合商可调节能力预测问题面临预测难度大、精度不高等问题。为此，文中提出一种基于图卷积神经（graph convolutional neural, GCN）网络的可调节能力预测方法。该方法依据用户历史负荷进行分类，并分别构建需求响应模型仿真得到响应样本库；在此基础上，将不同集群建模为节点，集群之间响应特性的相关性视作边，集群的响应特征视作节点特征矩阵，建立无向图；基于图卷积神经网络将调节能力预测问题转化为图中的点特征回归问题，通过图中的消息传递过程进行集群之间响应特征的共享，实现本节点历史数据与其余节点数据的时空双维特征利用，以提升预测精度。以算例分析所得的平均绝对百分比误差（mean absolute percentage error, MAPE)指标为例，GCN网络模型预测精度相较于长短期记忆（long short-term memory, LSTM）网络模型、支持向量机（support vector machine, SVM）模型、随机森林回归（random forest regression, RFR）模型分别提升了1.83%、2.10%和2.72%。

英文摘要:

Driven by the double-carbon goal and the construction of novel power system, excavating the adjustable potential of demand-side flexible resources becomes an inevitable trend to help maintain the supply and demand balance. However, the adjustable capacity forecasting problem of load aggregators faces great difficulty and low accuracy since there is not enough actual demand response (DR) data accumulated at present. To this end, this paper proposes a forecasting method for load aggregator based on the graph convolution neural(GCN) network. All customers are classified into several groups according to their historical load profile, and then, tailored DR models are built for each group to construct the response sample library. An undirected graph is established, whose nodes are different clusters, edges are the response characteristics correlation among clusters, and node characteristic matrix is the response characteristics of each cluster. Based on GCN, the forecasting problem is transformed into the regression problem of point characteristics on the graph. Through the message transmission process on the graph, the response characteristics of different clusters could be shared, and both the historical data of the targeted node and other nodes are effectively used to improve the prediction accuracy from both spatial and temporal aspects. Taking the mean absolute percentage error(MAPE) index obtained from the example analysis as an example, compared with long short-term memory(LSTM), support vector machine(SVM), and random forest regression(RFR), the forecasting accuracy of GCN model has increased by 1.83%, 2.10% and 2.72% in terms of RMSE.

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