CNN-LSTM-based wind speed prediction method

Abstract

The invention discloses a CNN-LSTM-based wind speed prediction method. The CNN-LSTM-based wind speed prediction method comprises the steps of: cleaning original record data of meteorological elements;taking the data of the F meteorological elements of N stations as input, and performing standardization processing on the data through adopting a Z-score method to enable the data to meet (0, 1) standard normal distribution; linearly combining the original meteorological elements by utilizing a PCA technology, and converting the original meteorological elements into a group of linearly irrelevantvariables; extracting a meteorological element feature set influencing the wind speed change through adopting an LASSO algorithm, and taking the meteorological element feature set as the input of a prediction model; extracting a potential spatial relationship between a target station and adjacent stations through adopting a spatial feature extraction algorithm to obtain Tspatial feature vectors on forecast times, and analyzing and checking the spatial relationship of wind speed change in combination with a Moran index; extracting a time feature relationship on the T spatial feature vectors through employing a time feature extraction algorithm, and continuously optimizing the time feature relationship by adopting an Adam algorithm; and taking an MAPE as an evaluation index, and verifying the accuracy rate of wind speed prediction on the test set.

Description

technical field [0001] The invention belongs to the field of computer data mining, and the design specifically relates to a wind speed prediction method based on CNN-LSTM. Background technique [0002] In recent years, windy weather prediction has become a research hotspot in the field of meteorological prediction and computer big data analysis, and has attracted the attention of many scholars. With the rise of big data, people hope that computers can independently identify and understand the meaning of various meteorological elements and their impact on wind speed changes, so that computers can analyze meteorological data more intelligently, so as to prevent meteorological disasters in advance. Reduce the resulting loss of human and material resources, and protect people's lives and property safety. As a basic element in the atmospheric environment, the study of wind is of great significance to weather and climate, environmental science, clean energy, and meteorological di...

AI Technical Summary

Problems solved by technology

However, due to the influence of many factors such as temperature, air pressure, altitude, terrain, and latitude, the wind has the characteristics of randomness, intermittent, and volatility, making it one of the most difficult elements of weather forecasting.

[0003] The traditional forecast method is mainly that the forecaster uses the experience knowledge to forecast the weather, but this will inevitably lead to forecast deviation due to human subjective limitations, and the traditional technology only roughly forecasts the wind speed in a large area, and cannot achieve high resolution, Short-term refined forecasting effect

Statistics-based forecasting methods can only analyze the linear relationship between meteorological elements in the historical time series, and it is difficult to deal with the nonlinear relationship between meteorological elements

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