Wind power prediction method based on singular spectrum analysis and deep learning

Abstract

The invention discloses a wind power prediction method based on singular spectrum analysis and deep learning. The method comprises the following steps: obtaining wind power, wind speed and wind direction historical data, and preprocessing the wind power, wind speed and wind direction historical data to obtain a wind power, wind speed and wind direction angle time sequence; taking a sine value anda cosine value of the wind direction angle time sequence; utilizing singular spectrum analysis to extract trend components and oscillation components of the wind power and wind speed time series, andreconstructing the two components; splicing the reconstructed sequence with the sine of the wind direction and the cosine of the wind direction to form an m@T * n tensor; dynamically selecting a training sample, and establishing a convolutional neural network-gated cycle unit deep learning prediction model; and predicting the generated tensor by adopting a convolutional neural network-gated cycleunit deep learning prediction model to obtain a predicted wind power time sequence. According to the method, the reconstruction time sequence of noise reduction is obtained through singular spectrum analysis, and the prediction precision is further improved.

Description

technical field [0001] The present invention relates to the field of short-term wind power forecasting, and more specifically, to a wind power forecasting method based on singular spectrum analysis and deep learning. Background technique [0002] Wind power data is affected by many factors, resulting in nonlinear and non-stationary characteristics of wind power. Therefore, the use of signal filtering processing technology has become an important stage in the preprocessing of wind power forecasting. The commonly used preprocessing is to use mode decomposition technology, but the high-frequency intrinsic mode function generated by the decomposition of this filtering technology will increase the difficulty of prediction. [0003] At present, wind power forecasting is mainly divided into three models: physical model, statistical model and artificial intelligence model. However, the physical model is not suitable for short-term forecasting, and the assumptions of the statistica...

AI Technical Summary

Problems solved by technology

Due to the limitation of the input structure, the traditional machine learning model cannot mine the coupling relationship between wind power and other influencing factors such as wind speed and wind direction, which makes it difficult to improve the prediction accuracy

Traditional machine learning models directly use non-stationary wind power time series as input, which cannot effectively extract the hidden features of wind power itself, resulting in low prediction accuracy

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