基于条件分类与证据理论的短期风电功率非参数概率预测方法Nonparametric Approach for Short-Term Probabilistic Wind Generation Forecast Based on Conditional Classification and Evidence Theory
林优;杨明;韩学山;安滨;
摘要(Abstract):
提出了一种基于稀疏贝叶斯分类与Dempster-Shafer(D-S)证据理论的短期风电功率概率分布非参数估计方法,预测时间尺度为48 h。该方法首先通过支持向量机(support vector machine,SVM)对风电功率进行点预测;进而将SVM预测误差的范围离散为多个区间,通过建立稀疏贝叶斯分类器对SVM预测误差落入各预定区间的概率进行估计。然后应用D-S证据理论对所有区间对应的概率估计结果进行整合,得到SVM预测误差的整体概率分布。最后叠加误差分布与SVM预测的风电功率值,得到风电功率的概率分布结果。该方法基于稀疏贝叶斯架构构建,具有高稀疏性,确保了模型的泛化能力与计算速度。该方法还系统地计及了风电场输出功率必须满足在[0,GN](GN为风电场装机容量)内取值的边界约束,使预测结果更加符合实际。以某74 MW的风电场为例对上述方法进行了验证,结果表明了该方法的有效性。
关键词(KeyWords): 风电功率概率预测;非参数估计;支持向量机;稀疏贝叶斯分类;D-S证据理论
基金项目(Foundation): 国家重点基础研究发展计划项目(973项目)(2013CB228205);; 国家自然科学基金项目(51007047,51477091)~~
作者(Author): 林优;杨明;韩学山;安滨;
Email:
DOI: 10.13335/j.1000-3673.pst.2016.04.020
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