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

Authors

• Yingying Shi, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
• Xiaochong Dong, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
• Guobin Fu, Electric Power Research Institute, State Grid Qinghai Electric Power Company, Xining 810008, China
• Miaomiao Ma, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
• Yanhe Li, Dispatch and Control Center, State Grid Qinghai Electric Power Company, Xining 810008, China
• Xuebin Wang, Electric Power Research Institute, State Grid Qinghai Electric Power Company, Xining 810008, China

Abstract

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.

Recommended Citation

Shi, Yingying; Dong, Xiaochong; Fu, Guobin; Ma, Miaomiao; Li, Yanhe; and Wang, Xuebin (2026) "Missing-data-tolerant Diffusion-based Wind Power Scenario Forecasting Method," Journal of System Simulation: Vol. 38: Iss. 8, Article 4.
DOI: 10.16182/j.issn1004731x.joss.26-0213
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss8/4

First Page

2167

Last Page

2178

CLC

TP391.1

Recommended Citation

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.

Corresponding Author

Ma Miaomiao

DOI

10.16182/j.issn1004731x.joss.26-0213