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Accurate wind power forecasting has great practical significance for the safe and economical operation of power systems. In reality, wind power data are recorded at high time resolution (5 s, etc.). The original high-resolution data are averaged to produce the low-resolution time series (10 min, etc.) used in wind power forecasts. Therefore, the current wind power forecasting models neglect certain information in the high-resolution data. Moreover, the common Gaussian assumption used for the error term in the current wind power forecasting model is not consistent with the real, complex wind power forecasting error distribution. In this paper, an adaptive robust multi-kernel regression model is proposed to deal with the two disadvantages mentioned above. First, a multi-kernel regression model is constructed to process the multi-resolution wind power data. Second, a Gaussian mixture model is employed to model the …