电子测量与仪器学报

韩莹,朱宏宇,李琨.融合聚类及随机配置网络的短期光伏功率预测[J].电子测量与仪器学报,2023,37(11):205-216

融合聚类及随机配置网络的短期光伏功率预测

Short-term PV power prediction by fusion of clustering and SCN

DOI：

中文关键词: 光伏功率预测融合聚类北方苍鹰优化算法莱维飞行策略变分模态分解随机配置网络

英文关键词:photovoltaic power prediction integrated clustering northern gohawk optimization algorithm L􀆧vy flight strategy variational modal decomposition stochastic configuration network

基金项目:国家自然科学基金(62203197)、辽宁省“兴辽英才计划”青年拔尖人才项目(XLYC2007091)资助

作者	单位
韩莹	1.辽宁工程技术大学电气与控制工程学院
朱宏宇	1.辽宁工程技术大学电气与控制工程学院
李琨	1.辽宁工程技术大学电气与控制工程学院

Author	Institution
Han Ying	1.Faculty of Electrical and Control Engineering, Liaoning Technical University
Zhu Hongyu	1.Faculty of Electrical and Control Engineering, Liaoning Technical University
Li Kun	1.Faculty of Electrical and Control Engineering, Liaoning Technical University

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中文摘要:

为了降低天气因素对光伏发电功率的输出值预测精度的影响,从聚类分析和信号分解两方面入手,提出了一种融合聚类算法(KDGMM),改进的变分模态分解(VMD)与随机配置网络(SCN)的预测模型。首先通过 KDGMM 聚类将气象数据划分成晴天、阴天和雨天,针对阴天难以准确预测的问题,采用灰色关联度分析(GRA)选择相似日,其次引入莱维飞行北方苍鹰优化算法(LNGO)优化 VMD 得到最优参数,从而降低阴天光伏功率的非平稳性。最后构建 SCN 预测模型对光伏功率数据进行预测,输出其预测结果。通过实验分析,所提方法的均方根误差(RMSE)和平均绝对百分比误差(MAPE)仅为 1. 44 和 1. 3%,拟合优度指标 R 2 高达 0. 99,与其他预测方法相比,本文所提方法有较高的预测精度。

英文摘要:

In order to reduce the influence of weather factors on the prediction accuracy of the output value of photovoltaic power generation, it is proposed a prediction model incorporating the clustering algorithm ( KDGMM), the improved variational modal decomposition (VMD) and the stochastic configuration network ( SCN), starting from both cluster analysis and signal decomposition. Firstly, the meteorological data are classified into sunny, cloudy and rainy days by KDGMM clustering, and for the problem that it is difficult to predict accurately on cloudy days, gray correlation analysis (GRA) is used to select similar days, and secondly, the L􀆧vy northern goshawk optimization (LNGO) algorithm is introduced to optimize VMD to get the optimal parameters, so as to reduce the nonsmoothness of PV power on cloudy days. Finally, the SCN prediction model is constructed to predict the PV power data and output its prediction results. Through experimental analysis, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed method are only 1. 44 and 1. 3%, and the R 2 index for goodness of fit is as high as 0. 99. Compared with other prediction methods, the proposed method has higher prediction accuracy

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