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Large‐scale day‐ahead wind power forecasting (WPF) for wind farm clusters (WFCs) can enable dispatching agencies to formulate scientifically sound power generation plans and enhance the robustness of power grids. Most available WPF methods for WFCs only involve mathematical models and rarely consider spatial correlation factors. This necessitates further improvements to forecasting systems. In this study, to increase the day‐ahead WPF accuracy for WFCs, fractal transform theory is introduced to optimize the process of WPF for WFCs through spatial upscaling and establish a day‐ahead WPF model for WFCs based on an improved spatial upscaling method. First, a WFC is partitioned into subclusters. Then, using fractal transform theory, an affine relation is established between the local output of each subcluster and the overall output of the WFC. Finally, a day‐ahead power forecast is produced for the WFC through spatial upscaling of the forecast for the subcluster with the highest grey relational grade with the output of the WFC. The applicability of the proposed forecasting model is examined using historical measured data for a large WFC in north‐eastern China. The case study results show that the proposed forecasting model outperforms the summation method and the statistical upscaling method in terms of forecasting accuracy. [ABSTRACT FROM AUTHOR]

Wind speed is the dominant meteorological factor affecting wind turbine power generation. Existing wind speed fluctuation division algorithms only focus on the wind speed changing process, but strong uncertainty exists in the wind speed–power conversion, which has been demonstrated as a multi-power transfer relationship under wind speed fluctuation. Therefore, a wind speed power transfer fluctuation partitioning (PTFP) algorithm is proposed, which fully considers the wind speed variation and power transfer characteristics to refine the modeling of wind power forecasting. The power transfer characteristics are investigated according to the wind speed–power conversion error of a wind farm. The measured wind speeds from three sources and fuzziness under different fluctuation trends are discussed based on the scatter distribution and probabilistic wind speed power curve. The model errors under different changing trends are quantitatively analyzed. Subsequently, the operation data of two wind farms in China are divided using the PTFP algorithm. The modeling error is reduced compared to that without considering the power transfer characteristics and the rationality of the proposed algorithm is demonstrated. The PTFP algorithm is also applied to wind power forecasting to verify its applicability. The results demonstrate that the proposed model can improve prediction accuracy on all time scales. [ABSTRACT FROM AUTHOR]