Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Chao Fu,Hui-Juan Zhang,Guo-Quan Li,Kuo-Ping Lin

doi:10.3390/su11020512

Abstract

Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

Highlights

The problems of environmental pollution, ecological damage, conventional energy depletion and haze weather have become increasingly serious
Through the analysis of the test result data, it can be found that the convergence precision of improved chicken swarm optimization algorithm (ICSO) algorithm is better than particle swarm optimization (PSO), differential evolution algorithm (DE) and chicken swarm optimization algorithm (CSO) algorithms
Compared445500with Figure 4, it can be found that the prediction errors of the two models are obviously increased4.40000The predictive effect of ICSO model for the second set of wind power data is better than CSO-SVM335500 model

Summary

Introduction

The problems of environmental pollution, ecological damage, conventional energy depletion and haze weather have become increasingly serious. The position update equation of hen particles is improved, and a self-learning factor is introduced in the equation. It can be found from Equation (11) that the value of the learning factor is large at the beginning of the iteration, and the hen particles have better global search ability. As the number of iterations increases, the value of learning factor decreases gradually, and the hen particles have better local search ability. The improved hen position update formula is shown in Equation (12): Pij(t + 1) = w(t) ∗ Pij(t) + H1 ∗ Random ∗ (Prj1(t) − Pij(t)) +H2 ∗ Random ∗ (Prj2(t) − Pij(t)) It can be seen from Equation (12) that the hen particles cannot learn from the cock particles with good fitness value, and can learn by themselves.

PSO DE CSO ICSO

DE ICSO

OutpOuut tput PowePrower

Sample number

Findings

Number of Training Samples Model