A wind power cluster power prediction method based on dynamic self-adaptation

Abstract

The invention provides a wind power clustering power prediction method based on dynamic adaption. The wind power clustering power prediction method includes the steps of step 1 collecting the historical data and dividing wind power clusters; step 2 according to the divided wind power clusters, establishing a time sequence prediction model, a numerical weather forecasting prediction model, a spatial resource matching and prediction model, and training the power prediction of the three prediction models of the wind power clusters; step 3 selecting the prediction model with the optimal training error evaluation result according to the training error evaluation results of the three models; step 4 collect the real-time numerical weather forecasting NWP data and the real-time power measurement data; and step 5 according to the prediction model selected in the training process, substituting into the real-time NWP data and the real-time power measurement data to obtain the sub-cluster prediction results, and making addition of the sub-cluster prediction results to obtain the cluster total prediction result. The optical prediction module can be selected for wind power clusters in different work conditions, and the prediction precision can be improved.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a dynamic self-adaptive based wind power cluster power prediction method, which is suitable for power prediction of large-scale wind power clusters. Background technique [0002] In recent years, with the increasingly severe global energy problems, the development of renewable energy power generation, especially wind power generation, has become increasingly important. However, wind energy has inherent volatility, instability and intermittency, which makes the output of wind power fluctuate with the change of wind speed. If the future output of wind power can be correctly predicted, it will have a positive impact on the safe and stable operation of the power grid. By predicting the amount of wind power generation in the future, the power grid side can adjust the dispatch plan in advance to avoid problems such as unstable power and lack of supply. On the side of th...

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Problems solved by technology

These methods have a certain effect on the power prediction of the cluster, but there are problems of long model training time and low accuracy.

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