Wind speed prediction method based on local mean decomposition and deep learning neural network

Abstract

According to the wind speed prediction method based on the LMD and the LSTM, the actual wind speed is decomposed into a plurality of components, the prediction difficulty is reduced, and a time sequence model is established in combination with the LSTM for wind speed prediction; comprising the following steps: (1) extracting steady-state data; (2) wind speed feature extraction based on LMD; (3) establishing a wind speed prediction model based on LSTM; the LMD and the LSTM are combined, and the wind speed prediction precision is improved by utilizing the characteristic that the combined prediction has the advantages of the two algorithms; the local mean decomposition method is used for decomposing data, so that modal aliasing can be effectively eliminated, and the model prediction precision is improved; by using the excellent time sequence feature extraction capability of the LSTM, the wind speed prediction method can be effectively popularized to wind speed prediction of different stations with complex geographic features, and the accuracy of wind speed prediction is improved in time and space.

Description

technical field [0001] The invention relates to the field of wind power generation, in particular to a wind speed prediction method based on local mean decomposition and deep learning neural network. Background technique [0002] Renewable energy sources, such as wind, solar, geothermal and biomass energy, can reduce environmental pollution and achieve sustainable development goals. Among these renewable energy sources, wind energy has attracted increasing attention due to its clean and abundant properties. Today, much wind power generation is integrated into the grid system. However, due to the characteristics of randomness, intermittence, and volatility of wind power, if it is directly integrated into the grid, it may interfere with the reliability and stability of the grid system. Accurate prediction of wind speed and power can enable the power dispatching department to optimize the operation of the power grid system and wind farm in a timely manner, and is an effective...

AI Technical Summary

Problems solved by technology

[0003] In the past few decades, various methods have been proposed to enhance the performance of wind speed prediction. These methods are roughly divided into the following three categories: physical methods, statistical methods, and machine learning methods. Physical methods mainly use meteorological factors and geographical factors and other physical parameters to predict wind speed. However, the physical model has high computational cost and cannot capture the complex dynamic relationship of meteorological factors, so it is not suitable for short-term wind speed prediction. Statistical methods use the linear relationship of various variables in the historical time series to build statistical models , such as time series methods, autoregressive moving average methods, and Kalman filter methods, etc. These methods overcome the shortcomings of physical models, but they can only analyze the linear relationship between variables in historical time series, and it is difficult to deal with the relationship between meteorological elements. Nonlinear relationship. Machine learning such as support vector machine, multi-layer perceptron and extreme learning machine can extract complex nonlinear features in wind speed time series, and improve the accuracy of forecast to a certain extent. But these traditional linear and nonlinear The model can only extract shallow features, and requires a lot of feature engineering, and cannot automatically extract time series features.

[0004] In the actual wind speed prediction, the above single prediction method often cannot achieve the ideal prediction effect, and the single prediction model needs to be optimized and improved to improve the prediction accuracy of wind speed

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