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An enhanced differential learning wind speed interval-value prediction system based on optimal collaborative interval decomposition and strategic model selection

Author

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  • Jiang, Weiyi
  • Wang, Jujie
  • Shu, Shuqin
  • He, Xuecheng

Abstract

Wind speed prediction is crucial for enhancing clean energy generation and optimizing power resource scheduling. Traditional point prediction oversimplifies the complex interplay of factors contributing to variability and uncertainty. In contrast, interval-value prediction more effectively reflects the complexity of wind speed variation by constructing a series of wind speed variation ranges. Therefore, an enhanced differential learning wind speed interval-value prediction system based on optimal collaborative interval decomposition and strategic model selection is proposed in this paper. Firstly, the improved singular spectrum analysis (SSA) is established to decompose the maximum and minimum wind speed series of the target time period respectively, and the optimized reconstruction error is created to match the interval subsequences with different characteristic attributes. Then a variety of advanced intelligent prediction models are introduced to predict each interval subsequence. The prediction model with the best performance is selected by designing the comprehensive evaluation index (CEI). Finally, enhanced mutual information analysis is used to evaluate the contribution of each selected model and make further fine-tuning and integration. Through the empirical study of two wind farms in China, the CEI reached 0.39 and 0.14, respectively, which confirmed that the system has excellent predictive performance and robustness.

Suggested Citation

  • Jiang, Weiyi & Wang, Jujie & Shu, Shuqin & He, Xuecheng, 2026. "An enhanced differential learning wind speed interval-value prediction system based on optimal collaborative interval decomposition and strategic model selection," Renewable Energy, Elsevier, vol. 256(PB).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pb:s0960148125016696
    DOI: 10.1016/j.renene.2025.124005
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    References listed on IDEAS

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