摘要
为充分发挥组合核函数在相关向量机预测模型中的优势,有效提高负荷预测的精度,提出基于Bagging-组合核函数相关向量机的短期负荷预测模型。首先构造了高斯核函数与Morlet小波核函数加权组合的组合核函数相关向量机的预测模型,然后采用粒子群算法对两个核函数的最优权值进行优选。为提高模型的泛化能力,采用Bagging算法对原始数据多次抽样构造训练样本集。通过实际算例仿真,与多种相关向量机预测模型对比分析,验证了该模型具有较好的预测精度。
In order to give full play to the advantages of the combined kernel function in the relevance vector machine forecasting model and effectively improve the accuracy of load forecasting,a short-term load forecasting model based on the Bagging-combined kernel function correlation vector machine is proposed.Firstly,the forecasting model of combined kernel function relevance vector machine is constructed by weighted combination of Gaussian kernel function and Morlet wavelet kernel function,and then the particle swarm optimization algorithm is used to optimize the optimal weights of the two kernel functions.In order to improve the generalization ability of the model,the Bagging algorithm is used to sample the original data multiple times to construct a training sample set.Through the simulation of actual example,compared with a variety of relevance vector machine forecasting models,it is verified that the proposed model has good prediction accuracy.
作者
邱山
龚文杰
张智晟
QIU Shan;GONG Wenjie;ZHANG Zhisheng(College of Electrical Engineering,Qingdao University,Qingdao 266071;Qingdao Electric Power Company of State Grid,Qingdao 266002)
出处
《电气工程学报》
CSCD
2023年第2期142-148,共7页
Journal of Electrical Engineering
基金
国家自然科学基金资助项目(52077108)。
关键词
短期负荷预测
相关向量机
组合核函数
BAGGING算法
小波核函数
Short-term load forecasting
relevance vector machine
combinatorial kernel function
Bagging algorithm
Morlet wavelet kernel function
