| 引用本文: | 黄予春,曹成涛,顾海.基于IKFCM与多模态SSO优化SVR的光伏发电短期预测[J].电力系统保护与控制,2018,46(24):96-103.[点击复制] |
| HUANG Yuchun,CAO Chengtao,GU Hai.Short-term photovoltaic power generation forecasting scheme based on IKFCM and multi-mode social spider optimization SVR[J].Power System Protection and Control,2018,46(24):96-103[点击复制] |
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| 摘要: |
| 为提高短期光伏发电功率预测精度和降低气候等因素对预测结果的影响，提出了一种基于IKFCM与多模态SSO优化SVR的光伏发电功率短期预测方案。首先采用改进的KFCM(Improved KFCM, IKFCM)聚类方法对训练样本集进行处理，通过引入紧致离散聚类有效性指数，在提高IKFCM聚类准确率的同时实现了自动划分训练样本集，有效降低了样本数据差异对预测性能的影响。然后构建与训练样本集分类一一对应的SVR预测模型，并采用多模态SSO优化(Multi-mode SSO, MSSO)算法对SVR模型参数进行优化，进而得到不同分类的最优SVR参数组合。最后，运用MSSO优化SVR模型对测试数据进行预测评估。仿真结果表明，该方案实现了不同天气下短期光伏发电功率准确预测，而且同其他预测算法相比预测精度提高了25.2%~37.8%。 |
| 关键词: 光伏发电功率 核模糊C-均值聚类 群居蜘蛛优化 支持向量回归(SVR) |
| DOI：10.7667/PSPC171782 |
| 投稿时间：2017-12-07修订日期：2018-03-21 |
| 基金项目:国家星火计划项目(2015GA780024) |
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| Short-term photovoltaic power generation forecasting scheme based on IKFCM and multi-mode social spider optimization SVR |
| HUANG Yuchun,CAO Chengtao,GU Hai |
| (Luohe Power Supply Company, State Grid Henan Electric Power Company, Luohe 462000, China;South China University of Technology, Guangzhou 510640, China;Harbin Institute of Technology, Harbin 150001, China) |
| Abstract: |
| In order to improve the accuracy of short-term PV power prediction and reduce the influence of climate factors on forecasting results, a short-term photovoltaic power generation forecasting scheme based on Improved Kernel Fuzzy C-Means (IKFCM) and multi-mode social spider optimization SVR is proposed. Firstly, the improved KFCM (IKFCM) clustering method is used to process the training sample set. By introducing intra class scatter clustering validity index, the automatic training sample set separation is realized and the clustering accuracy of IKFCM is improved, thereby the effect of sample data difference on prediction performance is effectively reduced. Then, the SVR prediction models corresponding to training samples set classifications one to one are built, and the Multi-modal SSO (MSSO) optimization algorithm is used to optimize the parameters of SVR model, which helps to obtain the optimal SVR parameters combination for each SVR model. Finally, the MSSO optimization SVR model is used to predict the test data. Simulation results show that, the scheme can realize accurate short-term PV power prediction for different weather conditions, and compared with other prediction algorithms, the prediction accuracy is improved by 25.2%~37.8%. This work is supported by China Spark Program (No. 2015GA780024). |
| Key words: forecasting of photovoltaic power generation kernel fuzzy C-means clustering social spider optimization support vector regression (SVR) |
