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Decision‐Oriented Renewable Scenario Generation Based on Multi‐Scale Decomposition and WGAN ‐ GP

Hao Hong et al.

Expert Systems2026https://doi.org/10.1111/exsy.70241article
AJG 2
Weight
0.50

What the paper says

Nowadays, with the growing penetration of renewable generation, economic dispatch is increasingly important in short‐term power system operation. In this paper, a deep renewable scenario generation model combining Multi‐Scale Decomposition mixer and Wasserstein Generative Adversarial Network with Gradient Penalty is proposed to achieve novel decision‐oriented forecasting, thus realizing effective characterization of renewable temporal dynamics and economic performance. From the perspective of wind and solar generation, the validity of the proposed method is demonstrated on a real‐world dataset with power station at regional level. Experimental results confirm the superiority of model performance through statistical indicators and power system scheduling test, compared with a number of scenario generation and time series forecasting benchmarks.

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https://doi.org/https://doi.org/10.1111/exsy.70241

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@article{hao2026,
  title        = {{Decision‐Oriented Renewable Scenario Generation Based on Multi‐Scale Decomposition and WGAN ‐ GP}},
  author       = {Hao Hong et al.},
  journal      = {Expert Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/exsy.70241},
}

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Decision‐Oriented Renewable Scenario Generation Based on Multi‐Scale Decomposition and WGAN ‐ GP

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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