基于随机森林和支持向量回归的风力发电预测算法
Wind Power Prediction Algorithm based on Random Forest and Support Vector Regression
彭嘉宁 1徐鹤勇1
作者信息
- 1. 国网宁夏电力有限公司 宁夏电力调度控制中心,宁夏 银川 750001
- 折叠
摘要
为实现精确的风能出力预测,保障风力发电系统稳定并网,提出了一种基于随机森林模型和支持向量回归模型的精确风力发电功率预测算法.该算法以回归树和随机森林模型为基础,对风力发电影响因素进行特征重要性评估;基于特征筛选理论,构建最优特征集合;使用最优特征集合输入支持向量回归模型,实现风力发电功率的预测.为验证算法的有效性,使用实测数据开展实验分析.实验结果表明:相比于单独使用随机森林模型,本文算法大幅提高了预测精度,平均绝对误差降低了19.67%;相比于长短时神经网络模型,本文算法在保持同样高精度的同时,大幅降低了模型复杂度以及所需的训练时间.本文算法能够实现风力发电功率精确预测,具有较为重要的理论和实际意义.
Abstract
To achieve accurate wind power output prediction and ensure the stable grid connection of wind power generation systems,an accurate wind power prediction algorithm based on the random forest model and support vector regression model was proposed.The algorithm was based on regression trees and random forest models to evaluate the importance of factors affecting wind power generation;based on fea-ture selection theory,an optimal feature set was constructed;the optimal feature set was input into the support vector regression model to predict wind power generation.In order to verify the validity of the al-gorithm,this paper used actually measured data to carry out experimental analysis.Experimental results show that compared to using the random forest model alone,the algorithm significantly improves the pre-diction accuracy with a reduction of 19.67%in average absolute error;compared to the long short-term memory neural network model,the algorithm achieves the same high accuracy while significantly reducing the model complexity and training time required.The algorithm can achieve accurate wind power predic-tion,which has important theoretical and practical significance.
关键词
风力发电/功率预测/支持向量回归/随机森林模型Key words
wind power generation/power prediction/support vector regression/random forest model引用本文复制引用
基金项目
国家电网科技项目(2017NR58337)
出版年
2024
NETL
NSTL
万方数据