Wind Speed Prediction Model Based on Deep Learning

Abstract

This article selects hourly wind speed data recorded by meteorological monitoring stations as the dataset, and conducts in-depth analysis on the preprocessing methods of wind speed data in response to the nonlinearity and instability of wind speed time series. At the same time, the algorithm principles and steps of empirical mode decomposition, comprehensive empirical mode decomposition, and complementary ensemble empirical mode decomposition were introduced, and the decomposition results of different methods were compared. In addition, in the selection of prediction algorithms for wind speed prediction models, the theoretical basis and algorithm steps of backpropagation neural networks, deep confidence networks, and long-term and short-term memory neural networks were studied, and a single model prediction performance comparison was conducted on three time series short-term prediction models. Compared with the LSTM model, the RMSE of the model established in this article decreased by 0.9422, MAE decreased by 0.6789, and MAPE decreased by 7.23%.