An ultra-short-term wind power forecasting method based on dccso optimized deep learning model

Abstract

The present invention relates to the technical field of wind power prediction, and more specifically, to a super-short-term wind power prediction method based on a DCCSO optimized deep learning model: collect original wind power data and preprocess the original wind power data, and establish a time-series attention-gate control recurrent unit deep learning prediction model, and optimize the initial weight and threshold of the temporal attention-gated recurrent unit deep learning prediction model based on the improved crossover algorithm, which can effectively improve the performance of the temporal attention-gated recurrent unit deep learning prediction model Convergence speed and generalization performance; temporal attention-gated recurrent unit deep learning prediction model, temporal attention can improve the time sensitivity of the model itself to the input, and the gated recurrent unit can further mine the hidden time correlation inherent in the input time series The combination of temporal attention and gated recurrent unit is of great significance to improve the accuracy of wind power prediction.

Description

technical field [0001] The present invention relates to the technical field of wind power forecasting, and more specifically, to an ultra-short-term wind power forecasting method based on a DCCSO optimized deep learning model. Background technique [0002] Existing wind power prediction methods can be divided into physical methods and data-driven prediction methods. The modeling of the physical prediction method is relatively complicated and the calculation cost is high. At the same time, the model is easily affected by the actual environment and has the problem of poor anti-interference ability. Data-driven forecasting methods can be subdivided into statistical methods and data-driven forecasting methods using artificial intelligence algorithms. Statistical methods obtain corresponding statistical relationships through research on historical data to achieve future wind power forecasting, but there are assumptions The data is linearly related, so it cannot reflect the nonli...

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Problems solved by technology

For example, Chinese patent CN200910193820.6 discloses a wind power prediction method based on genetic algorithm to optimize BP neural network. When genetic algorithm is used for ultra-short-term wind power prediction, the running time is too long; Chinese patent CN107274012A discloses a particle swarm algorithm based on cloud evolution The short-term wind power forecasting method, when used for ultra-short-term wind power forecasting, the particle swarm optimization algorithm is easy to fall into the local optimal solution

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