Wind power cluster power interval prediction method and system based on deep learning

Abstract

The invention provides a wind power cluster power interval prediction method and system based on deep learning. The method comprises steps that numerical weather forecast and historical wind power ofeach wind power station are obtained as original input data; mutual information between an interpretation variable and a target variable in a region is extracted by calculating the mutual informationof the interpretation variable so as to extract associated information; interpretation variables conforming to relevancy are selected; data reconstruction and dimension reduction are carried out by using a principal component analysis method. According to the method, the interval constraint condition is constructed, the prediction model is constructed by using deep learning, the reconstructed anddimensionality-reduced data is input into the model to be trained, model optimization is carried out by combining a particle swarm optimization method, the final prediction model is determined, and power interval prediction is carried out by using the final prediction model, so that the method has relatively high accuracy.

Description

technical field [0001] The disclosure belongs to the field of wind power forecasting, and in particular relates to a method and system for forecasting a power interval of a wind power cluster based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The environmental problems caused by the massive combustion of fossil fuels and energy depletion have attracted more and more global attention, and it has become the consensus of all countries to vigorously develop renewable and clean energy. However, unlike the strong controllability of traditional energy sources, wind power is intermittent and random. Therefore, a high proportion of wind power connected to the grid poses severe challenges to the economical, safe and stable operation of the power system. Accurate and reliable wind power forecasting results are one of the import...

AI Technical Summary

Problems solved by technology

Although some of these methods can obtain more accurate prediction results, there is a problem that cannot be ignored in single-value prediction, namely: due to the lack of data and the fluctuation characteristics of wind power itself, single-value prediction will inevitably introduce prediction errors, and the definite prediction results cannot be provided. Uncertain information about wind power

Make the use of wind power forecasting results in the decision-making process based on stochastic optimization or risk assessment has certain limitations

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