Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for
short-term wind speed prediction was proposed to assist in power system
planning and operation in this paper. Firstly, the wind speed time series data
was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency
component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM)
after the execution of differential and normalization operations. Thereafter,
the predictive outputs for each component underwent integration through a
fully-connected neural architecture for data fusion processing, resulting in
the final prediction. The VMD decomposition technique was introduced in a
generalized CNN-LSTM prediction model; a BPNN model was utilized to predict high-frequency components obtained from VMD, and
incorporated a fully connected neural network for data fusion of individual
component predictions. Experimental results demonstrated that the proposed
improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the
safe operation of wind farms.
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