内蒙古电力技术2024，Vol.42Issue(2)：1-7,7.DOI:10.19929/j.cnki.nmgdljs.2024.0017
基于改进灰狼算法优化WLSSVM的短期风功率预测
Short Term Wind Power Prediction Based on WLSSVM Optimized by Improved Grey Wolf Optimization Algorithm
摘要
Abstract
In order to improve the accuracy of short-term wind power prediction,the author proposes a short-term wind power prediction method based on an improved grey wolf algorithm optimized weighted least squares support vector machine.The embedding dimension of wind power time series is calculated using the C-C method,and the relationship between short-term wind speed prediction input and output is determined based on the calculation results.The improved Grey Wolf Optimization algorithm with stronger optimization performance is obtained by utilizing Tent mapping and parameter nonlinear adjustment strategies.The improved IGWO algorithm is validated through test functions to accelerate iterative convergence and improve computational accuracy.Using the IGWO algorithm to optimize the penalty coefficients and kernel parameters of Weighted Least Squares Support Vector Machine,a short-term wind power prediction model based on IGWO-WLSSVM is established.Using wind power data from two different seasons of a wind farm,spring and summer,a numerical analysis is conducted.The results show that the accuracy and stability of the wind power prediction results are better than other methods,verifying the effectiveness and practicality of the proposed method.关键词
风功率/改进灰狼算法/WLSSVM/C-C法Key words
wind power/the improved grey wolf optimization algorithm/weighted least squares support vector machine/C-C method分类
动力与电气工程引用本文复制引用
陈琨,丁苗,刘炬,段洁,刘闯,徐达..基于改进灰狼算法优化WLSSVM的短期风功率预测[J].内蒙古电力技术,2024,42(2):1-7,7.基金项目
中国博士后基金面上资助项目"风-光-地热园区综合能源系统的多能互补建模与协同优化"(2021M692992) （2021M692992）
