摘要
为提升光伏电站的发电量预测精度,提出一种基于改进松鼠搜索算法优化支持向量机回归的光伏发电量预测模型。首先提出适用支持向量机回归作为基础预测模型,然后提出一种改进的松鼠搜索算法对SVR中参数进行寻优,最后在测试ISSA寻优性能后,构建发电量预测模型,仿真结果证明本文所提模型的预测效果较好,为相关研究也起到参考作用。
In order to improve the forecasting accuracy of photovoltaic power generation, this paper proposes a photovoltaic power generation forecasting model based on improved squirrel search algorithm to optimize support vector machine regression. First, support vector machine regression is proposed as the basic prediction model, and then an improved squirrel search algorithm is proposed to optimize the parameters in SVR. Finally, after testing the optimization performance of ISSA, this paper builds a power generation prediction model. The simulation results show that the prediction effect of the model proposed in this paper is high, which also plays a reference role for related research.
作者
李沛隆
Li Peilong(Lianyungang Sanxin Power Supply Service Co.,Ltd.,Lianyungang,China)
出处
《科学技术创新》
2023年第1期34-37,共4页
Scientific and Technological Innovation
关键词
光伏发电量预测
支持向量机回归
松鼠搜索算法
非线性因子
photovoltaic power generation forecast
support vector machine regression
squirrel search algorithm
nonlinear factor
