面向源网荷的智能化数据协同推断技术研究综述

张辉; 颜星雨; 毛建旭; 别克扎提·巴合提; 杜瑞; 王耀南

doi:10.16383/j.aas.c250203

面向源网荷的智能化数据协同推断技术研究综述

doi: 10.16383/j.aas.c250203 cstr: 10.16383/j.aas.c250203

张辉^{1, 2,},
颜星雨^{1, 2,},
毛建旭^{2, 3,},
别克扎提·巴合提^{1, 2,},
杜瑞^{1, 2,},
王耀南^{1, 2,}

1.
湖南大学人工智能与机器人学院长沙 410082
2.
湖南大学机器人视觉感知与控制技术国家工程研究中心长沙 410082
3.
湖南大学电气与信息工程学院长沙 410082

基金项目: 国家自然科学基金重点项目(62433010), 科技创新2030-"新一代人工智能"重大项目(2021ZD0114503), 国家电网有限公司科技项目(5700-202423229A-1-1-ZN)资助

详细信息

作者简介:
张辉：湖南大学人工智能与机器人学院教授. 2007年获得湖南大学博士学位. 主要研究方向为数据分析, 图像处理和机器人控制. 本文通信作者. E-mail: zhanghui1983@hnu.edu.cn

颜星雨：湖南大学人工智能与机器人学院硕士研究生. 2024年获得江南大学学士学位. 主要研究方向为数据分析, 时间序列分析. E-mail: yanxingyu@hnu.edu.cn

毛建旭：湖南大学电气与信息工程学院教授.　2003年获得湖南大学博士学位.　主要研究方向为数据分析, 信号分析和图像处理. E-mail: maojianxu@hnu.edu.cn

别克扎提·巴合提：湖南大学人工智能与机器人学院博士研究生.　2020年获得英国克兰菲尔德大学硕士学位.　主要研究方向为信号分析, 时间序列分析. E-mail: bbiekezati@hnu.edu.cn

杜瑞：湖南大学人工智能与机器人学院博士研究生.　2020年获得湘潭大学硕士学位.　主要研究方向为数据分析, 图像处理. E-mail: durui@hnu.edu.cn

王耀南：中国工程院院士, 湖南大学人工智能与机器人学院教授.　1995年获得湖南大学博士学位.　主要研究方向为数据分析, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

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- 文章访问数: 11
- HTML全文浏览量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2025-05-08
- 录用日期: 2025-08-04
- 网络出版日期: 2025-10-23

A Review of Intelligent Data Collaborative Inference Techniques for Source-grid-load Systems

ZHANG Hui^{1, 2
,},
YAN Xing-Yu^{1, 2
,},
MAO Jian-Xu^{2, 3
,},
BIEKEZATI Baheti^{1, 2
,},
DU Rui^{1, 2
,},
WANG Yao-Nan^{1, 2
,}

1.
School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082
2.
National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha 410082
3.
College of Electrical and Information Engineering, Hunan University, Changsha 410082

Funds: Supported by the Key Program of National Natural Science Foundation of China (62433010), the National Key Research and Development Program of China (2021ZD0114503), and the Science and Technology Project of State Grid Corporation of China (5700-202423229A-1-1-ZN)

More Information

Author Bio:
ZHANG Hui　Professor at the School of Artificial Intelligence and Robotics, Hunan University. He received his Ph.D. degree from Hunan University in 2007. His research interest covers data analysis, image processing, and robot control.Corresponding author of this paper

YAN Xing-Yu　Master student at the School of Artificial Intelligence and Robotics, Hunan University. She received her bachelor degree from Jiangnan University in 2024. Her research interest covers data analysis and time series analysis

MAO Jian-Xu　Professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Hunan University in 2003. His research interest covers data analysis, signal analysis, and image processing

BIEKEZATI Baheti　Ph. D. candidate at the School of Artificial Intelligence and Robotics, Hunan University. He received his master degree from Cranfield University in 2020. His research interest covers signal analysis and time series analysis

DU Rui　Ph. D. candidate at the School of Artificial Intelligence and Robotics, Hunan University. He received his master degree from Xiangtan University in 2020. His research interest covers data analysis and image processing

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the School of Artificial Intelligence and Robotics, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interest covers data analysis, intelligent control, and image processing

摘要

摘要: 随着可再生能源并网比例的持续提升, 风电、光伏等新能源发电形式对电力系统的稳定性与调度智能化提出更高要求. 源网荷储一体化背景下, 如何高效利用多源异构电力数据实现精准预测与协同分析, 已成为关键问题. 近年来, 深度学习、大数据、大模型等技术推动智能化推断技术取得飞跃式进展. 本文首先结合深度学习技术, 对时间序列数据协同推断共性技术研究现状进行阐述, 重点分析趋势−季节性分解、频域建模、外生变量融合等关键方法, 分析基于不同架构的时间序列模型的研究现状. 其次针对源网荷智能化关键技术进行阐述, 进一步梳理源网荷储系统中智能预测、状态评估与负荷调度等典型场景中的关键技术路径, 并对其具体应用场景进行分析. 最后, 结合日益复杂的电力系统背景, 对数据协同推断技术的发展方向进行展望.
- 源网荷智能化 /
- 数据协同推断 /
- 时间序列分析 /
- 深度学习
Abstract: With the continuous increase in the proportion of renewable energy integration, new energy generation forms such as wind power and photovoltaic systems have imposed higher requirements on the stability and intelligent dispatch of power systems. Under the integrated framework of source-grid-load-storage, how to efficiently utilize multi-source heterogeneous power data for accurate forecasting and collaborative analysis has become a critical issue. In recent years, technologies such as deep learning, big data, and large-scale models have driven rapid advancements in intelligent inference techniques. This paper first elaborates on the current research status of common technologies for collaborative inference of time series data, in combination with deep learning techniques. Key approaches including trend-seasonality decomposition, frequency-domain modeling, and exogenous variable fusion are emphasized, alongside an analysis of time series models based on different architectures. Secondly, the paper discusses the key intelligent technologies for source-grid-load integration, further outlining critical technological pathways in typical scenarios such as intelligent forecasting, state assessment, and load scheduling within the source-grid-load-storage system, with detailed analyses of their specific application contexts. Finally, in light of the increasingly complex power system environment, prospects for the future development of data collaborative inference technologies are presented.
- Intelligentization of source-grid-load /
- data collaborative inference /
- time series analysis /
- deep learning

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图 1 文章总体架构

Fig. 1 Overall structure of the article

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图 2 数据协同推断技术

Fig. 2 Data collaborative inference technologies

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图 3 数据协同推断技术发展历程

Fig. 3 Development history of data collaborative inference technologies

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图 4 时间序列分解技术

Fig. 4 Time series decomposition techniques

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图 5 改进的RNN模型

Fig. 5 Improved RNN models

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图 6 扩散图卷积模型结构

Fig. 6 Diffusion graph convolution model structure

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图 7 Transformer骨干架构

Fig. 7 Transformer backbone architecture

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图 8 用于时序分析的自回归LLM

Fig. 8 Autoregressive LLM for time series analysis

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图 9 源网荷储系统

Fig. 9 Source-grid-load-storage system

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图 10 基于图卷积的电力设备状态检测模型

Fig. 10 Graph convolutional network-based model for power equipment condition monitoring

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图 11 南方电网储能设备状态大数据智能分析系统 XS-1000D^[174]

Fig. 11 China Southern Power Grid XS-1000D energy storage equipment status big data intelligent analysis system

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图 12 未来源网荷智能化系统

Fig. 12 Future source-grid-load intelligent system

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表 1 基于交叉注意力的外生变量融合方法

Table 1 Cross-attention-based exogenous variable fusion methods

方法	核心机制	方法特点
Crossformer^[12]	通过二维分段嵌入(DSW embedding)保留时空结构信息, 结合两阶段注意力(TSA)模块交替捕获跨时间与跨维度依赖	分层编码解码结构以实现多尺度时空特征提取, 并通过参数共享降低模型复杂度
ExoTST^[47]	将历史外生、当前外生与主序列视为三模态输入, 通过分层多头交叉注意力模块逐层融合	对缺失与噪声鲁棒性强, 动态整合多源异质驱动信号
TimeXer^[48]	补丁级自注意力先行建模局部时序依赖, 再通过变量级交叉注意力引入外生变量	双流架构兼容局部与全局信息, 适应多模态输入与分布漂移
TGForecaster^[61]	利用文本描述与数值序列共同作为查询与键值, 通过交叉注意力实现多模态融合	支持场景化预测, 增强模型可解释性与上下文感知能力
CATS^[62]	舍弃自注意力, 仅保留未来—历史跨注意力机制, 以参数共享和可学习查询捕获预测依赖	架构简化, 参数与内存开销低, 长程预测精度优异

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表 2 CNN架构时空协同推断模型

Table 2 Spatiotemporal collaborative inference model based on CNN architecture

技术	主要特点	代表文献
1D CNN	直接将时空数据视为1D序列, 通过标准、扩张或因果卷积捕获局部特征和长程依赖	[69][70][71]
2D CNN	先将1D时空数据转换为2D张量, 利用Inception块对信息进行融合, 再将2D张量重塑为1D序列	[72]

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表 3 AI气象大模型

Table 3 AI-based large-scale meteorological models

模型名称	模型结构	预测时效性	空间分辨率	预测范围
FuXi^[142]	级联机器学习天气预报系统	中期15天	0.25°	6h
FengWu^[143]	多模态多任务Transformer	中期10.75天	0.25°	6h
GraphCast^[144]	机器学习+图形神经网络	中期10天	0.25°	6h
Pangu-Weather^[2]	3D多尺度Transformer	短、中期 1h7天	0.25°	6h
NowcastNet^[145]	物理约束的深度生成模型	临近3h	20km	10min
SwinVRNN^[146]	递归神经网络+Transformer	中期	5.6250°	6h
FourCastNet^[147]	自适应傅里叶算子Transformer	短、中期	0.25°	6h
DLWP^[148]	卷积神经网络	短、中期	1.9°	6h

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