| 引用本文: | 张雨金,杨凌帆,葛双冶,周杭霞.基于Kmeans-SVM的短期光伏发电功率预测[J].电力系统保护与控制,2018,46(21):118-124.[点击复制] |
| ZHANG Yujin,YANG Lingfan,GE Shuangye,ZHOU Hangxia.Short-term photovoltaic power forecasting based on Kmeans algorithm and support vector machine[J].Power System Protection and Control,2018,46(21):118-124[点击复制] |
|
| 摘要: |
| 短期光伏发电功率预测对维护电网安全稳定和协调资源利用具有重要的意义。提出了一种基于K均值算法(Kmeans)和支持向量机(SVM)的短期光伏发电功率预测方法。根据短期光伏发电特性和光伏发电季节特性，组织预测模型的训练样本集。通过K均值算法对训练样本集进行聚类分析，在聚类得到的各类别数据上分别训练支持向量机。预测时根据预测样本的类别使用相应的支持向量机进行发电功率预测。经实验表明所提出的方法相较于传统的BP、SVM模型精度有了明显的提升，具有较好的工程应用潜力。 |
| 关键词: 光伏发电 预测模型 特性分析 K均值算法 支持向量机 |
| DOI：10.7667/PSPC171595 |
| 投稿时间：2017-10-27修订日期：2018-02-01 |
| 基金项目:浙江省基础公益研究计划项目(LGF18F020017) |
|
| Short-term photovoltaic power forecasting based on Kmeans algorithm and support vector machine |
| ZHANG Yujin,YANG Lingfan,GE Shuangye,ZHOU Hangxia |
| (College of Information Engineering, China Jiliang University, Hangzhou 310018, China) |
| Abstract: |
| Short-term photovoltaic power forecasting is of great significance for maintaining the security and stability of the power grid and coordinating the utilization of resources. In this paper, a short-term photovoltaic power generation prediction method based on Kmeans algorithm and Support Vector Machine (SVM) is proposed. According to short-term photovoltaic power generation characteristics and seasonal characteristics, the training set of the prediction model is organized. The Kmeans algorithm is used to cluster the training set. Each class of data obtained by clustering is used to train a SVM. The SVM of the same type is used as the forecast sample for power generation prediction. Experiments show that the prediction accuracy of the proposed model is better than that of the traditional BP model and SVM model, so it has a good engineering application value. This work is supported by Basic Public Benefit Research Program of Zhejiang Province (No. LGF18F020017). |
| Key words: PV power generation prediction model characteristic analysis Kmeans algorithm SVM |
