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基于密度峰值层次聚类的短期光伏功率预测模型

发布时间:2018-11-07 16:12
【摘要】:针对传统聚类算法不易选取初始聚类中心、对噪声值较敏感、收敛速度慢及易陷入局部最优等问题,提出一种基于密度峰值的层次聚类算法对天气类型进行聚类。首先确定气象数据的密度峰值参数,采用分层聚类算法将气象数据划分为不同类别,然后利用支持向量机(SVM)对未知天气类型进行识别,最终采用径向基(RBF)神经网络建立光伏发电短期功率预测模型。仿真结果表明,该方法能有效提高气象类型的分类精度、加快寻优速度,提高离群样本点分离的鲁棒性,证明了其在小样本的情况下具有较高的精度,且在天气波动较大时仍能较好地实现功率值的预测。
[Abstract]:Aiming at the problem that traditional clustering algorithm is difficult to select initial clustering center, sensitive to noise value, slow convergence speed and easy to fall into local optimum, a hierarchical clustering algorithm based on peak density is proposed to cluster weather types. Firstly, the peak density parameters of meteorological data are determined, then the meteorological data are divided into different categories by hierarchical clustering algorithm, and then unknown weather types are identified by support vector machine (SVM). Finally, the short-term power prediction model of photovoltaic generation is established by radial basis function (RBF) neural network. The simulation results show that this method can effectively improve the classification accuracy of meteorological types, accelerate the speed of optimization, and improve the robustness of the separation of outlier samples. It is proved that this method has a high accuracy in the case of small samples. And the prediction of power value can be realized well when the weather fluctuates greatly.
【作者单位】: 上海电力学院自动化工程学院;同济大学电子与信息工程学院;
【基金】:国家自然科学基金(61573239) 上海市重点科技攻关计划(14110500700) 上海市电站自动化技术重点实验室项目(13DZ2273800) 上海市自然科学基金(15ZR1418600)~~
【分类号】:TM615


本文编号:2316878

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