Short-term Wind Speed Forecasting Based on ICEEMDAN-PSO-LSTM
This article proposes a short-term wind speed prediction method based on improved adaptive noise complete set empirical mode decomposition ( ICEEMDAN) and particle swarm optimization ( PSO) long and short term memory neural network ( LSTM ) models. Use ICEEMDAN algorithm to decompose daily wind speed data and calculate corresponding marginal spectra,and screen historical data based on spectral correlation;Using PSO algorithm to optimize LSTM neural network parameters,ICEEMDAN decomposition is performed on the input data,and multiple modal components obtained are predicted using PSO-LSTM. The wind speed prediction results are obtained by overlaying the predicted values of each component. Use the proposed method to predict the wind speed of a domestic wind farm,and verify the effectiveness of the proposed method through comparative analysis.
万方数据
维普
