WIND POWER FORECASTING BASED ON INTERPRETABLE OPTIMAL STACKING MODEL

Qi Huanxing; Zhuo Yixin; Li Ling; Yin Linfei; Qin Yiming; Meng Wenchuan

doi:10.19912/j.0254-0096.tynxb.2023-2152

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 559-569. DOI: 10.19912/j.0254-0096.tynxb.2023-2152

WIND POWER FORECASTING BASED ON INTERPRETABLE OPTIMAL STACKING MODEL

Qi Huanxing^1,2, Zhuo Yixin¹, Li Ling¹, Yin Linfei³, Qin Yiming¹, Meng Wenchuan⁴

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Abstract

High accuracy of wind power forecasting is an important support to ensure the balance of the power and load and stable operation of power system. In view of the trial-error and weak interpretative problems of current wind power forecasting algorithms in stack modeling, an interpretable optimal stacking model （IOSM） is proposed in this paper, which is applied to wind power forecasting. Firstly, establish an initial redundant base learner and interpret the feature weights. Further, construct a feature optimization metric that comprehensively measures model performance and computation cost, to optimize and select key features and train them to form optimized base learners. Finally, based on feature optimization metrics,the meta learner is established by the the optimized base learner. As above, the complete IOSM is constructed. The proposed algorithm is applied to typical wind farms in Guangxi. Compared with the optimal single model, the RMSE and MAE indexes of IOSM are reduced by 13.01% and 18.23% respectively. Compared with other mainstream combinatorial algorithms, the RMSE and MAE indexes of IOSM can be decreased by 8.24% and 10.28% respectively, while still reducing 142.38% modeling computation cost at least. The effectiveness and advancement of the proposed algorithm are verified, which provides a new idea and method for interpretability modeling of wind power forecasting.

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wind power forecasting / interpretability / stacking model / feature optimization / ensemble learning

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Qi Huanxing, Zhuo Yixin, Li Ling, Yin Linfei, Qin Yiming, Meng Wenchuan. WIND POWER FORECASTING BASED ON INTERPRETABLE OPTIMAL STACKING MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 559-569 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2152

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