基于数据挖掘的双模式组合光伏功率日前预测  

Day-ahead prediction of dual-mode combined photovoltaic power based on data mining

作　　者：刘丽桑 郭凯琪 徐哲壮[2] 郭琳 LIU Lisang;GUO Kaiqi;XU Zhezhuang;GUO Lin(Fujian University Engineering Research Center of Industrial Automation,Fujian University of Technology,Fuzhou 350118,China;School of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350118,China;Yongchun Power Supply Company,State Grid Fujian Power Supply Co.,Ltd.,Quanzhou 362600,China)

机构地区：[1]福建理工大学工业自动化福建省高校工程研究中心,福建福州350118 [2]福州大学电气工程与自动化学院,福建福州350118 [3]国网福建省供电有限公司永春县供电公司,福建泉州362600

出　　处：《武汉大学学报（工学版）》2024年第10期1459-1468,共10页Engineering Journal of Wuhan University

基　　金：国家自然科学基金面上项目(编号:61973085);福建省自然科学基金项目(编号:2022J01952);福建省科技厅高校产学研合作项目(编号:2022H6005)。

摘　　要：为了提高光伏发电功率的预测精度,在数据挖掘分析基础上提出双模式组合的日前光伏预测方法。首先,利用波动量分析建立输出功率与天气类型之间更精确的匹配模型,将天气划分为简单与复杂2种天气类型。其次,对于简单天气类型,采用K-means聚类分析选取最相似日和XGBoost回归组合的预测模型;对于复杂天气类型,提出基于变分模态分解(variational modal decomposition,VMD)、采用麻雀算法(sparrow search algorithm,SSA)优化极限学习机(extreme learning machine,ELM)的日前光伏预测模型。最后,选用DKASC Alice Spring光伏电站数据集对2种模型进行验证,并进行仿真实验。实验结果显示,使用双模式组合方法构建的光伏发电功率预测模型在春季和夏季2个不同数据集下,相关系数分别达到96.44%和96.61%,比其他4种常用模型具有更高的预测精度。In order to improve the prediction accuracy of photovoltaic(PV)power,this paper proposes a dualmode combination prediction method of day-ahead PV power generation based on data mining analysis.Firstly,fluctuation volume analysis is utilized to establish a more accurate matching model between output power and weather type,and the weather is divided into two types including simple weather type and complex weather type.Secondly,a prediction model combining K-means clustering analysis to select the most similar day and XGBoost regression is adopted for simple weather types.For complex weather types,a day-ahead PV forecasting model based on variational modal decomposition(VMD)and using sparrow search algorithm(SSA)to optimize extreme learning machine(ELM)is proposed.Finally,the DKASC Alice Spring PV power plant data set is selected to validate the two models and conduct simulation experiments.The experimental results show that the correlation coefficients of the PV power prediction model constructed by the dual-mode combination method reach 96.44%and 96.61%respectively under two different datasets of spring and summer,which has higher prediction accuracy than the other four commonly used models.

关 键 词：光伏功率日前预测 双模式组合模型 波动量分析 变分模态分解 极限学习机 麻雀优化算法 

分 类 号：TM615[电气工程—电力系统及自动化]