基于数据缺失容忍扩散的风电场景预测方法

doi:10.16182/j.issn1004731x.joss.26-0213

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (8): 2167-2178.doi: 10.16182/j.issn1004731x.joss.26-0213

• 专栏：基于仿真的优化及其在新型电力系统中的应用 • 上一篇

基于数据缺失容忍扩散的风电场景预测方法

施颖颖¹^,², 董骁翀³, 傅国斌⁴, 马苗苗¹^,², 李延和⁵, 王学斌⁴

^1.华北电力大学控制与计算机工程学院，北京 102206
^2.华北电力大学新能源电力系统全国重点实验室，北京 102206
^3.清华大学电机工程与应用电子技术系，北京 100084
^4.国网青海省电力公司电力科学研究院，青海西宁 810008
^5.国网青海省电力公司调度控制中心，青海西宁 810008

收稿日期:2026-03-15 修回日期:2026-05-11 出版日期:2026-08-28 发布日期:2026-08-31
通讯作者: 马苗苗
第一作者简介:施颖颖(2000-)，女，博士生，研究方向为人工智能在电力系统中的应用。
基金资助:
智能电网重大专项(2030)(2024ZD0802000);国家电网公司科技项目(52280024000L)

Missing-data-tolerant Diffusion-based Wind Power Scenario Forecasting Method

Shi Yingying¹^,², Dong Xiaochong³, Fu Guobin⁴, Ma Miaomiao¹^,², Li Yanhe⁵, Wang Xuebin⁴

^1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
^2.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
^3.Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
^4.Electric Power Research Institute, State Grid Qinghai Electric Power Company, Xining 810008, China
^5.Dispatch and Control Center, State Grid Qinghai Electric Power Company, Xining 810008, China

Received:2026-03-15 Revised:2026-05-11 Online:2026-08-28 Published:2026-08-31
Contact: Ma Miaomiao

摘要/Abstract

摘要：

针对传统先补全再预测方法存在误差累积的问题，提出一种缺失数据容忍扩散框架（missing data tolerant diffusion framework，MDTDF）。使用XGBoost回归模型将数值天气预报数据映射为确定性预测功率；去噪网络中的编码器用于提取时序特征，该特征与确定性预测融合，用于解码器中的跨注意力机制，引导去噪过程；提出历史约束机制，直接利用不完整历史数据，结合样本梯度更新与历史信息引导的噪声注入，对每步去噪结果进行动态校正。仿真结果表明：在数据缺失条件下，MDTDF的预测效果显著优于现有先进方法，并在不同缺失率下表现出良好的稳定性和鲁棒性。

关键词: 场景预测, 条件扩散模型, 数据缺失, 自适应, 风电功率

Abstract:

To address the issue of error accumulation in traditional "imputation-then-forecasting" approaches, a missing data tolerant diffusion framework (MDTDF) is proposed. An XGBoost regression model is employed to map numerical weather prediction data into deterministic power forecasts. The encoder in the denoising network extracts temporal features, which are fused with the deterministic forecasts and fed into the decoder through a cross-attention mechanism to guide the denoising process. A historical constraint mechanism is introduced to directly utilize incomplete historical data and dynamically correct the denoising result at each step through sample gradient updates and noise injection guided by historical information. The simulation results show that the proposed MDTDF achieves significantly better forecasting performance than existing state-of-the-art methods when data are missing and demonstrates good stability and robustness across different missing rates.

Key words: scenario forecasting, conditional diffusion model, missing data, adaptive, wind power output

中图分类号:

TP391.1

施颖颖,董骁翀,傅国斌等 . 基于数据缺失容忍扩散的风电场景预测方法[J]. 系统仿真学报, 2026, 38(8): 2167-2178.

Shi Yingying,Dong Xiaochong,Fu Guobin,et al . Missing-data-tolerant Diffusion-based Wind Power Scenario Forecasting Method[J]. Journal of System Simulation, 2026, 38(8): 2167-2178.

图/表 15

图1

图2

图3

图4

表1

MDTDF超参数设置

定义	取值
训练轮次E	500
批大小B	128
学习率r_l	10^–3
噪声强度参数 $β 1, β K$	(0, 0.999)
网络层{L₁, L₂}	{3, 3}
场景数R	200
扩散步K	100

表1

图5

图6

图7

图8

图9

表2

表3

图10

表4

图11

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季节	MAE	RMSE	PS	CRPS
平均	0.027 6	0.046 4	0.010 9	0.020 9
春季	0.024 2	0.042 4	0.009 5	0.018 3
夏季	0.029 3	0.044 7	0.011 5	0.022 0
秋季	0.026 0	0.045 3	0.010 3	0.019 8
冬季	0.031 0	0.052 7	0.012 3	0.023 5

缺失率/%	MAE	RMSE	PS	CRPS
0	0.027 5	0.046 3	0.010 9	0.020 8
5	0.027 6	0.046 4	0.010 9	0.020 9
15	0.027 8	0.046 7	0.011 0	0.021 0
25	0.028 0	0.046 9	0.011 0	0.021 2
35	0.028 3	0.047 3	0.011 2	0.021 4
45	0.028 5	0.047 5	0.011 2	0.021 5

模型	MAE	RMSE	PS	CRPS
MDTDF	0.015 4	0.027 6	0.005 9	0.011 3
LI+VAE	0.021 5	0.038 4	0.009 0	0.017 3
LI+WGAN-GP	0.053 0	0.084 4	0.019 8	0.038 0
文献[20]+DDPM	0.016 9	0.028 8	0.006 5	0.012 5

基于数据缺失容忍扩散的风电场景预测方法

Missing-data-tolerant Diffusion-based Wind Power Scenario Forecasting Method

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