Research On Ocean Wind Speed Prediction Method Based On Deep Learning

Efficient and accurate wind speed prediction methods play an extremely important guiding role in the formulation and deployment of early warning,management,control and decisionmaking schemes in military,shipping,marine wind power and new energy.However,the meteorological element information such as wind speed are affected by a variety of internal and external factors,exhibiting data characteristics such as intermittent,non-stationarity and high complexity,which makes the construction of prediction models with wide applicability and high predictability face great challenges.Therefore,taking data feature driven modeling as the core idea,this paper discusses the effective modeling of wind speed prediction from the perspectives of “Feature Decomposition-Deep Integration” and “Domain Generalization-Deep Integration” by grasping the fluctuation characteristics,trend law and distribution characteristics of main variables in the time dimension,combined with the deep neural network model,hoping to build a wind speed prediction combination model with excellent prediction performance.The main work of this paper includes the following aspects:(1)From the perspective of “Feature Decomposition-Deep Integration”,a combined model of deep neural network wind speed time series prediction based on variational modal decomposition and attention mechanism is constructed in this paper.According to the characteristics of non-stationary,nonlinear and randomness of wind speed time series,the model introduces variational modal decomposition adaptive time-frequency decomposition algorithm to decompose the characteristics of the original series,to effectively identify the internal influencing factors of time series and accurately grasp the potential law of time series change.At the same time,based on the idea of feature driven modeling,combined with deep learning model,the complex time series modeling problem is handled by self-organization and self-learning in the form of efficient and flexible fitting function.In view of the limitations of traditional recurrent neural network in dealing with long-distance dependence,this paper introduces the attention mechanism module to mine the local complex correlation information of the sequence in the way of parallel computing,effectively characterize the interaction between the data,and improve the actual prediction effect of the combined model.(2)From the perspective of “Domain Generalization-Deep Integration”,a distribution adaptive deep neural network wind speed time series prediction combined model based on transfer learning is constructed in this paper.The model draws on the ideas of domain generalization and maximum entropy principle,and focuses on how to effectively model the wind speed time series prediction from the perspective of adaptive data distribution from the perspective of transfer learning according to the characteristics that the statistical characteristics of wind speed time series data distribution change with time.This model first divides the original time series data sequence effectively by quantifying the similarity of time series distribution and matching the time series distribution,and optimizes the distribution distance between sub sequences.The evaluation method of distribution correlation weight is introduced to effectively describe the domain similarity and association impact between distributions,so as to realize a distribution adaptive model.At the same time,combined with the deep neural network integrating the attention mechanism to mine the time-series dependence between the data points in the sequence,so as to improve the prediction performance of the combined model.In this paper,verification experiments are carried out on real observation data sets,and the models proposed in this paper are fully compared and analyzed with other benchmark models.The experimental results show that the wind speed prediction models proposed in this paper have better performance in a certain prediction range.The prediction performance of wind speed has shown certain practical and engineering application value,and provides a novel research perspective for wind speed prediction modeling.