A novel hybrid model for short-term prediction of wind speed

Abstract

Due to the randomness and contingency of wind speed size and direction, it is difficult to predict the wind speed accurately, which seriously affects the stable operation of the power system. To improve the operation stability of power system, the accurate prediction of wind speed is very important. In this paper, a new hybrid model based on gray wolf algorithm (GWO) and support vector machine (SVM) for wind speed prediction is proposed. Firstly, Neo4j(NE) is utilized to identify the data and preprocess the data. Secondly, k-means clustering(KC) is utilized to analyze data and eliminate invalid data. Thirdly, GWO is utilized to optimize the kernel function parameters and penalty factors of SVM to improve the prediction results. Fourthly, The four modules are combined into NE-KC-GWO-SVM model to predict the wind speed accurately. Finally, to verify the effectiveness of the proposed model, the prediction accuracy of the model is experimentally analyzed from two parts. One is to analyze the superiority of the model itself by using the method of single model removed. The results show that the proposed model is the best, and has high accuracy, and can reflect the characteristics of wind speed well and truly. The other one is that models similar to those proposed in the literature are selected for comparative analysis. The experimental results show that compared with the other two models, the proposed model has the best accuracy. At the same time, the proposed model has good prediction stability and acceptable time complexity. Based on all the experimental results, it can be obtained that the proposed model has better prediction effect, which can provide a scientific basis for the macro-control of power system and improve the operation security and stability of power system.