Offshore wind power ultra-short-term prediction method and device based on climbing identification

The invention provides an offshore wind power ultra-short-term segmented prediction method and device based on climbing identification, and belongs to the technical field of offshore wind power prediction. The method comprises the following steps: firstly, collecting historical power data and weather forecast information data of an offshore wind turbine, and then realizing weather forecast information data feature selection according to a random forest feature importance calculation module and a Pearson coefficient; inputting the historical power data and the weather forecast information data after feature selection into a deep learning model for climbing identification; according to a climbing identification result, a TCN-XGBoost combination model is adopted to carry out ultra-short-term prediction on the offshore wind power; and finally, proposing an improved quantile regression probability prediction model according to an ultra-short-term prediction result, and obtaining an ultra-short-term probability pre