Meteorological factor fluctuation feature extraction method for short-term wind power prediction

Abstract

The invention discloses a meteorological factor fluctuation feature extraction method for short-term wind power prediction. The method comprises the following steps: 1, constructing a meteorological factor day-ahead data set outputted by a numerical weather forecast mode; 2, analyzing the correlation between the actually measured wind power and different meteorological factors in the day-ahead data set, and screening out an optimal meteorological factor feature subset; 3, dividing fluctuation types of feature meteorological factors in the optimal meteorological factor feature subset, and analyzing a mapping relationship between different meteorological factor fluctuations and wind power fluctuations; 4, extracting feature meteorological factor fluctuation time granularity and feature parameters, and establishing a feature meteorological factor fluctuation characteristic matrix; and 5, taking the feature meteorological factor fluctuation feature matrix as model input, and predicting theshort-term wind power of the day-ahead wind power plant. According to the method, the problem that fluctuation complexity of different meteorological factors is difficult to match with the wind poweris solved, the data dimension and the data volume inputted by the prediction model can be effectively reduced, and the problems of overfitting and the like during prediction are avoided.

Description

technical field [0001] The invention relates to the field of power system operation and control, in particular to a method for extracting meteorological factor fluctuation features for short-term wind power prediction. Background technique [0002] In recent years, with the increasing consumption of fossil energy such as coal and oil and the environmental pollution caused by their development and utilization, energy security and climate issues have become one of the major challenges facing mankind. Low-carbon and clean renewable energy represented by wind energy and solar energy has become an inevitable trend of global energy development in the future. Among them, wind power generation, as the most widely used and most mature technology, has become the most promising form of new energy power generation. one. [0003] Wind in nature is a changing process of atmospheric physical movement, and its fluctuation regularity conforms to the corresponding physical change law, while ...

AI Technical Summary

Problems solved by technology

The present invention overcomes the problem of the complexity of fluctuations in different meteorological factors and the difficulty in matching wind power, can effectively reduce the data dimension and data volume input by the forecasting model, avoids problems such as overfitting during forecasting, and improves the calculation of forecasting speed and efficiency

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