Ultra-short-term probabilistic wind power forecasting with spatial-temporal multi-scale features and K-FSDW based weight

•Features are constructed in both space and time, and spatial-temporal multi-scale feature selection is performed on them.•A dynamic sparse weighting algorithm based on K-Forward nearest neighbours is proposed to combine individual quantile models.•The probability density function provides uncertainty in the wind speed and the results confirm the validity of the model.