基于T-Graphormer的电网碳排放因子预测方法

doi:10.3969/j.issn.2097-0706.2025.06.004

摘要/Abstract

摘要：

电网碳排放因子是衡量电能对环境影响程度的重要指标，对未来时段电网碳排放因子的高精度预测是引导用户主动参与需求侧响应，实现电能利用清洁化和低碳化的关键。基于电网能量流的典型时空融合特性，提出一种基于T-Graphormer图神经网络的小时级电网碳排放因子预测模型。模型利用电网节点连接拓扑信息及历史电网碳排放因子数据，通过门控时间卷积块将电网碳排放因子映射到高维空间，将中心编码和位置编码嵌入节点特征，进而利用编码器与解码器进行时空数据挖掘，最后通过多层感知机得到电网碳排放因子的预测值。基于英国国家电网划分区域的电网碳排放因子数据对所提预测模型性能进行了验证，预测效果优于传统图神经网络预测模型。

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

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.

Key words: power grid carbon emission factor, T-Graphormer, graph neural network, Transformer, time series, demand-side response

中图分类号:

TP18：TM711

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.

ZHAN Guohua, ZHANG Xianyong, WEI Shengying, ZHANG Xiaoshun, LI Li. A prediction method for power grid carbon emission factor based on T-Graphormer[J]. Integrated Intelligent Energy, 2025, 47(6): 30-36.

图/表 8

图1

图2

图3

图4

表1

表2

预测结果对比

模型	3 h		6 h		12 h		24 h		平均值
模型	$k M A E$	$k R M S E$	$k M A E$	$k R M S E$	$k M A E$	$k R M S E$	$k M A E$	$k R M S E$	$k M A E$	$k R M S E$
STGCN	0.163 2	0.191 6	0.191 7	0.221 6	0.197 0	0.226 0	0.213 5	0.240 6	0.191 4	0.220 0
STGCN-Cheb	0.156 6	0.187 7	0.189 0	0.219 1	0.196 2	0.224 7	0.206 0	0.236 0	0.186 9	0.216 9
ASTGCN	0.143 4	0.171 3	0.186 5	0.211 1	0.194 8	0.223 3	0.203 1	0.230 4	0.182 0	0.208 9
T-Graphormer	0.135 2	0.164 1	0.168 4	0.199 3	0.193 9	0.222 8	0.198 0	0.225 8	0.172 7	0.201 8

表2

图5

图6

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名称	描述
ASTGCN	带注意力机制的时空图卷积神经网络
STGCN-Cheb	基于切比雪夫多项式逼近的时空图卷积网络
STGCN	时空图卷积神经网络