A Combined Prediction Method for Short-term Wind Speed Using Variational Mode Decomposition Based on Parameter Optimization

As one of the most important renewable energy sources in the world, wind energy has been widely studied and applied. When using wind energy to generate electricity, it will be of great help to the safety and stability of power supply, if the wind speed in the future can be accurately known. In this paper, a new short-term wind speed prediction method based on historical data is proposed. Firstly, the wind speed is pre-processed through variational mode decomposition (VMD) of which the parameters are optimized using a multi-objective optimization method in this paper, owing to the effect of VMD is greatly affected by parameters. Then, the combined forecasting method combining support vector machine improved by particle swarm optimization algorithm (PSO-SVM), back propagation neural network (BP) and long short-term memory network (LSTM) is used to predict each wind speed component. This paper takes data sets available at the US Virgin Islands Bovoni measurement station as an example, conducting experiments and performing analysis. Compared with other prediction models, it is demonstrated that the model proposed in this paper significantly improves the prediction accuracy. Also, the wind speed in January, April, July and October are forecasted respectively to test the stability of models, and the result shows that the proposed model has the best adaptability.

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