WIND DIRECTION PREDICTION ALGORITHM BASED ON CEEMDAN AND TIMPORAL CONVOLUTIONAL NETWORK
In order to improve the accuracy of wind direction prediction,a combined prediction algorithm based on decision tree method(CART),random forest algorithm,complete adaptive noise empirical mode decomposition(CEEMDAN)and temporal convolutional network(TCN)is proposed.Among them,the input importance evaluation based on decision tree method is used to evaluate and screen the input relevance of wind direction perdiction models.Randon forest algorithm is used to classify and process wind direction data;The complete adaptive noise integrated empirical mode decomposition is used to decompose the input wind direction data and extract the input information features;Finally,the temporal convolutional network is used to build the wind direction prediction model.The experimental results show that compared to the other eight comparison models,the prediction errors of the proposed model for wind direction in the fourth quarter data set are less than 4.95°,and the highest prediction accuracy is achieved.
deep learningfeature extractionrandom foresttemporal convolutional networkwind direction prediction
万方数据
维普
