Short Term Wind Speed Prediction Based on VMD and DBN Combined Model Optimized by Improved Sparrow Intelligent Algorithm

Abstract

Accurate wind speed prediction can help the power department to perceive the change rule of wind power in advance, reduce the impact of wind power grid connection, and then improve the wind power consumption rate. Therefore, an optimized variational modal decomposition (OVMD) method combined with optimized depth belief neural network (ODBN) is proposed to predict wind speed. First, the original wind speed data are processed by OVMD method, then the decomposed data are predicted by ODBN method, and the predicted component values are superimposed to obtain the wind speed prediction results. Taking the actual wind speed data of a certain area in Northwest China as an example, the proposed combined model is compared with common prediction methods such as DBN, long short term memory (LSTM), extreme learning machine (ELM), BP neural network, etc. The experimental results show that its RMSE decreases by 0.4494, 0.4778, 0.6217 and 0.6587, and its MAPE decreases by 10.3554%, 11.5484%, 14.6226% and 15.9493% respectively. The results verify the effectiveness of the prediction model.

Keywords

• Wind farm
• wind speed
• prediction accuracy
• VMD
• DBN