Hybrid wind power prediction method and device based on statistics

Abstract

The invention provides a hybrid wind power prediction method and device based on statistics, a storage medium and computer equipment. The method comprises the steps: training a prediction model by using various historical numerical weather prediction source data and historical power data, wherein the training process only needs to use historical data within a short time, the requirement for training data is low, and the training period is short; after training is finished, testing the trained prediction model by using prediction meteorological data provided by various historical numerical weather prediction source data in a second preset time period, so that whether the relationship among various parameters in the training stage is established or not is verified, and the prediction accuracy of the prediction model is ensured; under the condition that the relationship among the parameters is established, predicting the wind power of various prediction meteorological data; and finally, combining various prediction results to obtain a final wind power prediction result, so the prediction model can obtain a more accurate prediction result under the condition of having less historical data.

Description

technical field [0001] The present invention relates to the technical field of wind power generation, in particular to a statistically based hybrid wind power prediction method, device, storage medium and computer equipment. Background technique [0002] As the world's population continues to grow, non-renewable energy as the main energy provider is decreasing day by day. Therefore, countries around the world are striving to improve the utilization efficiency of renewable energy. As one of the most important renewable energy sources, wind energy is more volatile than other renewable energy sources, so wind power generation needs to be evaluated and planned. [0003] At present, predictive models in the prior art are generally based on artificial neural network (ANN), support vector machine (SVM) and linear regression; the ANN model mainly predicts the principle of perception obtained by historical data through the logic of the nervous system; The vector machine model is sim...

AI Technical Summary

Problems solved by technology

However, these prediction models have high requirements for training data, and usually need 1-2 years of historical data to construct a mathematical model, and the training period is long. For new wind farms, due to the lack of historical data, traditional prediction methods cannot Wind Power Forecasting

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