Short-term photovoltaic power forecasting based on Stacking-SVM

被引：12

作者：

Zhou, Hangxia ^{[1
]}

Zhang, Yujin ^{[1
]}

Yang, Lingfan ^{[1
]}

Liu, Qian ^{[1
]}

机构：

[1] China Jiliang Univ, Coll Informat Engn, Hangzhou, Zhejiang, Peoples R China

来源：

2018 NINTH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY IN MEDICINE AND EDUCATION (ITME 2018) | 2018年

关键词：

pv power generation; short term power forecast; stacking algorithm; cluster analysis; support vector machine; PREDICTION; ENSEMBLE; OUTPUT;

D O I：

10.1109/ITME.2018.00221

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Short-term photovoltaic(PV) power forecast is of great significance for maintaining the safety of the power grid and coordinating the use of resources. This paper introduces the idea and method of ensemble learning, and proposes a short-term photovoltaic power forecast model based on Stacking-SVM. This model uses SVM as base learner and meta learner of the Stacking algorithm, the K-means algorithm is used to cluster the training set, and the meta learner is trained by training samples of the same category as the forecast sample. In order to investigate the forecast performance, the proposed model is used for a 20kW PV power plant, the traditional forecast model is compared with the proposed model in stable meteorological conditions and abrupt meteorological conditions. The results show that the performance of proposed model has been significantly improved.

引用

页码：994 / 998

页数：5

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