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Deterministic and probabilistic wind speed forecasting using decomposition methods: Accuracy and uncertainty

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  • Sun, Qian
  • Che, Jinxing
  • Hu, Kun
  • Qin, Wen

Abstract

Wind energy is gaining in importance as a source of renewable energy. However, because wind speed is intermittent, integrating wind energy into the grid requires accurate forecasts. This paper tests several forecasting models based on decomposition, using data from two representative sea-land sites, Fujian and Inner Mongolia. It also analyzes the diverse uncertainty perspective of probability distributions and the role of causal factors. The main finding is that a method based on extreme machine learning achieves greater forecast accuracy than other decomposition-based methods. A further finding is that the uncertainty associated with forecasts can vary as a function of the model, and more specifically, the decomposition method. The results of experiments show that the optimal forecasting model for Fujian and Inner Mongolia wind speed is EMD-SE-WT-PSO-ELM and CEEMDAN-SE-WT-PSO-ELM respectively, with error indicator improvement rates both exceeding 27.99 %. However, in the long-term forecasts for the next 4 h, VMD-SE-WT-PSO-ELM gave the best forecasts, with goodness-of-fit above 90 % in all cases.

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  • Sun, Qian & Che, Jinxing & Hu, Kun & Qin, Wen, 2025. "Deterministic and probabilistic wind speed forecasting using decomposition methods: Accuracy and uncertainty," Renewable Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:renene:v:243:y:2025:i:c:s0960148125001776
    DOI: 10.1016/j.renene.2025.122515
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    3. Mi, Lihua & Han, Yan & Long, Lizhi & Chen, Hui & Cai, C.S., 2025. "A physics-informed temporal convolutional network-temporal fusion transformer hybrid model for probabilistic wind speed predictions with quantile regression," Energy, Elsevier, vol. 326(C).
    4. 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).
    5. Dirk Schindler & Jonas Wehrle & Leon Sander & Christopher Schlemper & Kai Bekel & Christopher Jung, 2025. "Assessment of Spatiotemporal Wind Complementarity," Energies, MDPI, vol. 18(14), pages 1-21, July.

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