摘要
针对现有电力负荷预测模型难以深入提取时空关联特征,模型泛化能力弱,无法同时胜任短期和长期的电力负荷预测的问题,提出一种面向多用户的基于余弦相似度和全局-局部协同图卷积网络的电力负荷预测方法。首先,利用余弦相似度来学习不同节点负荷数据之间的相似模式,以提取深层次的时空关联特征。其次,对影响电力负荷变化趋势的静态全局因素和动态局部因素进行协同建模,以提升模型的泛化能力。最后,通过在一个实测数据集上进行的大量实验,验证了该方法在短期和长期负荷序列预测任务中同时具备有效性和稳定性。
To address the challenges of existing power load forecasting models,which struggle to deeply extract spatiotemporal correlation features and exhibit weak generalization capabilities—failing to simultaneously manage both short-term and long-term forecasting—this study proposes a multi-user power load forecasting method using cosine similarity and a global-local collaborative graph convolutional network.First,cosine similarity is utilized to learn similar patterns between load data from different nodes,allowing for the deep extraction of spatiotemporal correlation features.Second,a collaborative modeling approach is applied to static global factors and dynamic local factors that influence power load trends,enhancing the model’s generalization ability.Finally,extensive experiments on a real-world dataset demonstrate the method’s effectiveness and robustness in forecasting both short-term and longterm load series.
作者
JI Shan
姜巍
景鑫
JI Shan;JIANG Wei;JING Xin(Hangzhou Data Far East Technology Pty.Ltd.,Hangzhou 310007,China;Center of Technology and Innovation State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310007,China)
出处
《浙江电力》
2025年第1期68-75,共8页
Zhejiang Electric Power
基金
国网浙江省电力有限公司科技项目(B311JZ230003)。
关键词
多节点电力负荷预测
时空关联特征
余弦相似度
图卷积
multi-node power load forecasting
spatiotemporal correlation features
cosine similarity
graph convo⁃lutional
