Short-term wind power prediction method fusing multi-source information

Abstract

The invention discloses a short-term wind power prediction method fusing multi-source information, and the method comprises the following steps: (1) a sample generation step: constructing a training sample according to numerical weather forecast data and wind power sequence data; (2) a feature extraction and feature fusion step: performing feature extraction and fusion on the training sample constructed in the sample generation step; and (3) a power prediction step: obtaining the prediction power output of the feature code obtained in the feature extraction and feature fusion step at the corresponding moment through a multi-layer perceptron, i.e., a final prediction result. Compared with a traditional wind power prediction method, the wind power prediction method has the advantages that the weather forecast data and the historical wind power data are fused, so that the periodic characteristics implied in the historical power data are captured, the time sequence characteristics of the numerical weather forecast data are mined, the difference characteristics of different fans are modeled, and the prediction precision is higher.

Description

technical field [0001] The invention relates to the field of wind power prediction, in particular to a short-term wind power prediction method. Background technique [0002] At present, there is no short-term wind power prediction method realized by fusion of multi-source information through variational mode decomposition, convolutional neural network, gated recurrent unit and wind turbine embedding, but there is a method of fusing wind power sequence and numerical weather prediction data by simple splicing. There are also wind power prediction methods that only rely on wind power sequences or numerical weather prediction data. This method is completely different from these methods. [0003] The prediction of wind power can be divided into indirect prediction and direct prediction. The indirect prediction first predicts the wind speed based on historical measurement data, and then further predicts the output power according to the obtained wind speed. However, due to the in...

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Problems solved by technology

However, this type of method only considers the simple splicing of historical power and numerical weather prediction data, and cannot capture the hidden periodic characteristics in power information.

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