Short Term Wind Power Prediction Based on CEEMDAN-LSTM
DOI:
https://doi.org/10.54097/ajst.v6i3.10387Keywords:
Wind power prediction, CEEMDAN, Deep Learning, LSTM; MIC.Abstract
To improve the accuracy of wind power prediction, a wind power prediction method based on time series decomposition and error correction is proposed. Firstly, the maximum information coefficient (MIC) method was used to select the features that have strong correlation with wind power to reduce the complexity of the original data; Then, according to the non-stationary characteristics of wind power, complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) was used to decompose wind power into several stationary subsequences; Finally, the long short memory network (LSTM) was used to dynamically model the wind power multivariable time series; Add the predicted values of each subsequence to get the final predicted value. Combined with the measured data of a domestic wind farm, the simulation results showed that the proposed method had higher short-term wind power prediction accuracy compared with other prediction models.
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