综合智慧能源2025，Vol.47Issue(6)：30-36,7.DOI:10.3969/j.issn.2097-0706.2025.06.004
基于T-Graphormer的电网碳排放因子预测方法
A prediction method for power grid carbon emission factor based on T-Graphormer
摘要
Abstract
The carbon emission factor of the power grid is an important indicator for assessing the environmental impact of electricity consumption.Accurate prediction of the power grid carbon emission factor for future time periods is crucial for guiding users to actively participate in demand-side response and achieving clean and low-carbon electricity utilization.Based on the typical spatio-temporal fusion characteristics of the power grid′s energy flow,a prediction model for hourly power grid carbon emission factor is proposed,utilizing the T-Graphormer graph neural network.The model incorporates topological information from power grid nodes and historical carbon emission factor data.Through a gated temporal convolution block,the carbon emission factor is mapped into a high-dimensional space,with central and positional encodings embedded into node features.An encoder-decoder structure is then employed for spatio-temporal data mining,and the predicted power grid carbon emission factor is obtained through a multi-layer perceptron.The performance of the proposed model is validated using carbon emission factor data from regions of the UK national grid.The results demonstrates that the prediction model outperforms traditional graph neural network prediction models.关键词
电网碳排放因子/T-Graphormer/图神经网络/Transformer/时间序列/需求侧响应Key words
power grid carbon emission factor/T-Graphormer/graph neural network/Transformer/time series/demand-side response分类
信息技术与安全科学引用本文复制引用
湛国华,张先勇,魏圣莹,张孝顺,李丽..基于T-Graphormer的电网碳排放因子预测方法[J].综合智慧能源,2025,47(6):30-36,7.基金项目
国家重点研发计划项目(2022YFF0606600) National Key R&D Program of China(2022YFF0606600) （2022YFF0606600）
