Wind energy resource prediction system and method based on fluid dynamics

Abstract

The invention discloses a wind energy resource prediction system and method based on hydrodynamics, and the method comprises the following steps: obtaining first data and second data, the first data comprising historical wind speed data, and the second data comprising historical wind energy data corresponding to the first data; performing wind direction prediction based on the first data and the second data, establishing a prediction model, and obtaining a mainstream wind direction of the target area; collecting historical wind speed and wind direction data in the target area as a training sample of an SAHDE algorithm; and training the sample data set to obtain optimal parameters of the prediction model. According to the method, the wind energy data in a longer time range can be predicted, the prediction result can have higher robustness and accuracy, meanwhile, the prediction error rate is reduced, it is guaranteed that the wind energy prediction error meets the preset value, the calculation amount is reduced to a certain degree, the calculation efficiency is improved, and the real-time performance of wind power plant wind energy prediction is guaranteed.

Description

Technical field [0001] The present invention relates to the field of wind energy prediction techniques, in particular to a hydrodynamics-based wind energy resource forecasting system and method. Background [0002] At present, the machine learning algorithms used for wind energy prediction mainly include artificial neural networks, decision trees, and support vector machine regression. Because the wind energy prediction problem itself is a problem of "predicting values according to characteristics", it has a good combination with general machine learning methods, so that most commonly used machine learning methods include: random forests, neural networks, and various regression algorithms can be easily migrated to this field. [0003] However, there are also drawbacks to current techniques for wind energy forecasting. First of all, a single model can only meet the predictions in a specific environment, cannot meet the general conditions of wind power forecasting, and cannot accur...

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Problems solved by technology

[0003] However, the current technology for wind energy forecasting also has shortcomings

First of all, a single model can only meet the forecast in a specific environment, but cannot meet the wind power forecast under general conditions, and cannot accurately forecast the demand for wind energy

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