A wind speed prediction method and system based on a neighborhood gate long-short-term memory network

Abstract

The invention belongs to the technical field of wind speed prediction, and discloses a wind speed prediction method and system based on a neighborhood gate long-short-term memory network. The Pearsoncorrelation coefficient and the maximum information coefficient are respectively adopted to explore the linear and nonlinear correlation among variables to screen the wind speed correlation factors. On the basis of correlation analysis, the Granger causality test is used to explore the statistical causality of wind speed and wind speed factors. The structure of causality is divided into five types, and all types of causality are unified into an equivalent tree causality structure by the method of 'decomposition-dummy variable-pruning'. Aiming at the causality structure of equivalent tree, a long-term and short-term memory network model based on neighborhood gates is proposed to predict wind speed. The prediction method (NLSTM) of the invention accurately considers the causality between thewind speed and the wind speed factors, effectively improves the prediction accuracy of the wind speed, and plays a vital role in the application of the wind power and the dispatching of the power grid.

Description

Technical field [0001] The invention belongs to the technical field of wind speed prediction, and in particular relates to a wind speed prediction method and system based on a neighborhood gate long short-term memory network. Background technique [0002] At present, the existing technology commonly used in the industry is as follows: [0003] Wind energy is a promising renewable and clean energy, which has received extensive attention from all over the world in recent years. More and more wind power is connected to the power system, making the power system unreliable, which is caused by the strong volatility and strong randomness of wind speed. Therefore, accurate prediction of wind speed is of vital importance to the utilization of wind energy and the efficient dispatch of power systems. Wind speed is affected by many meteorological factors, including air pressure, temperature, humidity and other factors. The intricate relationship between factors makes it difficult to predict...

AI Technical Summary

Problems solved by technology

[0010] In the existing technology, the intricate relationship between factors makes wind speed prediction difficult, and the accuracy that traditional machine learning methods can achieve in predicting wind speed is limited

[0011] In the prior art, since LSTM has only one feature input interface, LSTM can only input all factors indiscriminately and cannot accurately consider the causal relationship obtained through feature engineering, and cannot accurately consider the causal relationship structure of wind speed factors into LSTM

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