控制与信息技术Issue(3)：68-75,8.DOI:10.13889/j.issn.2096-5427.2025.03.200
融合注意力机制和图神经网络的光伏电站短期功率预测方法
Short-Term Power Prediction Method for Photovoltaic Power Plants Based on ECA-MTGNN Integration
摘要
Abstract
To address the issue of low accuracy in short-term power prediction for photovoltaic(PV)power plants,this paper proposes a power prediction method that integrates an Efficient Channel Attention Mechanism and a Multivariate Time Series Graph Neural Network(ECA-MTGNN).Firstly,to tackle challenges such as data missing and anomalies,data cleaning and imputation were performed on historical weather data and power data collected from a PV power plant located in northern China.Then,the ECA-MTGNN method was utilized to yield short-term power prediction for this plant.Finally,the prediction performance of the proposed method was evaluated using the prediction accuracy assessment criteria specific to PV power plants in the northern China region and the general evaluation metrics for time series prediction.Experimental results demonstrated that,compared to the MTGNN model,the ECA-MTGNN model achieved improvements in prediction accuracy of 8.82%,6.27%,2.89%,and 14.88%over the four days involved,respectively.In comparison with commonly used models for time series prediction,such as LSTM and CNN,the ECA-MTGNN model demonstrated improved prediction accuracy under conditions of gentle power variations,and more significant enhancements under conditions of sharp power fluctuations.Moreover,it exhibited a superior ability to fit actual power curves.关键词
光伏电站功率预测/时间序列预测/改进图神经网络/深度学习/高效通道注意力机制Key words
power prediction for photovoltaic(PV)power plant/time series prediction/improved graph neural network/deep learning/efficient channel attention mechanism分类
信息技术与安全科学引用本文复制引用
黄从智,刘彦彤..融合注意力机制和图神经网络的光伏电站短期功率预测方法[J].控制与信息技术,2025,(3):68-75,8.基金项目
中央高校基本科研业务费专项资金项目(2025JC003) （2025JC003）
