摘要
短期电力负荷预测对电力系统的安全稳定运行和电力市场交易具有重要作用,提出了一种基于“分解与重构”框架的短期电力负荷预测方法。首先,通过变分模态分解(variable mode decomposition,VMD)将负荷序列分解为5个分量,建立了5个分量的支持向量回归(support vector regression,SVR)预测模型;其次,对电力负荷分量进行预测,并采用灰狼优化(grey wolf optimization,GWO)算法对SVR的参数进行优化;最后,将5个分量的预测值进行叠加,得到最终的电力负荷预测结果。将该方法与无模态分解的SVR方法及其他预测方法进行对比,该方法的3个评价指标均为最优,表明该方法在短期电力负荷预测中具有良好的应用前景。
Short-term power load forecasting plays an important role in the safe and stable operation of power system and power market transactions.It proposes a short-term power load forecasting method based on decomposition and reconstruction framework.Firstly,the load sequence is decomposed into five components by variational mode decomposition(VMD),and the support vector regression(SVR)prediction model of five components is established to predict the power load component.Secondly,the power load component is predicted and the grey wolf optimization(GWO)algorithm is used to optimize the parameters of SVR.Finally,the predicted values of the five components are superimposed to obtain the final power load forecasting results.It compares this method with SVR method without modal decomposition and other prediction methods.The three evaluation indexes of this method are optimal.It shows that the method has a good application prospect in short-term power load forecasting.
作者
张异殊
李宜伦
姚志远
陈蕾宇
ZHANG Yishu;LI Yilun;YAO Zhiyuan;CHEN Leiyu(State Grid Dandong Power Supply Company,Dandong,Liaoning 118000,China)
出处
《东北电力技术》
2024年第7期27-31,37,共6页
Northeast Electric Power Technology
关键词
变分模态分解
灰狼算法
支持向量机
电力负荷预测
variable mode decomposition
grey wolf algorithm
support vector machine
power load forecasting
