基于特征因素选取的IVMD-GLSSVM光伏出力短期预测  

Short term forecasting of photovoltaic output based on feature factor selection and IVMD-GLSSVM

作　　者：袁建华[1] 李洪强 谢斌斌 何宝林 蒋文军 徐杰 Yuan Jianhua;Li Hongqiang;Xie Binbin;He Baolin;Jiang Wenjun;Xu Jie(College of Electrical Engineering&New Energy,Three Gorges University,Yichang 443000,China)

机构地区：[1]三峡大学电气与新能源学院,宜昌443000

出　　处：《电子测量技术》2023年第12期77-83,共7页Electronic Measurement Technology

基　　金：煤燃烧国家重点实验室开放基金(FSKLCCA1607);梯级水电站运行与控制湖北省重点实验室基金(2015KJX07)项目资助。

摘　　要：针对短期光伏发电功率预测输入特征数据冗余,抗干扰能力差,预测精度受限等问题,提出了基于特征因素选取的IVMD-GLSSVM短期光伏出力预测模型。首先利用GRA-KCC对影响特征因素进行分析,提取影响光伏发电功率的极相关特征因素,随后采用IVMD对光伏发电数据进行分解,降低数据非线性和波动性对预测精度的影响。然后将各模态分量分别输入GLSSVM预测模型进行预测,求得的各子序列预测结果叠加即为最终预测结果。最后在MATLAB中对该预测模型及其他模型进行算例验证和误差分析,结果表明采用所提预测模型抗干扰能力强,预测精度高。Aiming at the problems of redundant input characteristic data,poor anti-interference ability and limited predictive accuracy in short-term photovoltaic power prediction,short term forecasting of photovoltaic output based on feature factor selection and IVMD-GLSSVM is proposed.Firstly,GRA-KCC is used to analyze the characteristic factors that affect the photovoltaic power,and extract the extremely relevant characteristic factors that affect the photovoltaic power.Then IVMD is used to decompose the photovoltaic power data to reduce the impact of data nonlinearity and volatility on prediction accuracy.Then each modal component is input into the GLSSVM prediction model for prediction,and the superposition of the prediction results of each subsequence is the final prediction result.Finally,the prediction model and other models are verified and analyzed in MATLAB.The results show that the proposed prediction model has strong anti-interference ability and high prediction accuracy.

关 键 词：光伏功率预测 特征提取 改进变分模态分解 灰狼优化 最小二乘支持向量机 

分 类 号：TP271[自动化与计算机技术—检测技术与自动化装置]