当前分布式光伏系统短期发电功率预测结构多设定为目标式,预测范围在实际发电环境下受限,导致平均绝对预测误差增加。为此设计基于利用层次方法的平衡迭代规约和聚类(Balanced Iterative Reducing and Clustering Using Hierarchies,BIR...当前分布式光伏系统短期发电功率预测结构多设定为目标式,预测范围在实际发电环境下受限,导致平均绝对预测误差增加。为此设计基于利用层次方法的平衡迭代规约和聚类(Balanced Iterative Reducing and Clustering Using Hierarchies,BIRCH)的分布式光伏系统短期发电功率预测方法。首先,明确预测指标,采用多层级的方式设计预测结构;其次,结合BIRCH原理,设计发电功率预测模型;最后,采用梯度回归处理的方式来实现最终预测。测试结果表明,对比传统变分模态分解-麻雀搜索算法-反向传播(Variational Mode Decomposition-Sparrow Search Algorithm-Back Propagation,VMD-SSA-BP)光伏系统短期发电功率预测小组、传统时序动态回归光伏系统短期发电功率预测小组,此次所设计的方法得出的平均绝对预测误差被较好地控制在2.1以下,预测效果更佳,针对性更强,误差可控,具有实际的应用价值。展开更多
Clustering is an unsupervised learning method used to organize raw data in such a way that those with the same (similar) characteristics are found in the same class and those that are dissimilar are found in different...Clustering is an unsupervised learning method used to organize raw data in such a way that those with the same (similar) characteristics are found in the same class and those that are dissimilar are found in different classes. In this day and age, the very rapid increase in the amount of data being produced brings new challenges in the analysis and storage of this data. Recently, there is a growing interest in key areas such as real-time data mining, which reveal an urgent need to process very large data under strict performance constraints. The objective of this paper is to survey four algorithms including K-Means algorithm, FCM algorithm, EM algorithm and BIRCH, used for data clustering and then show their strengths and weaknesses. Another task is to compare the results obtained by applying each of these algorithms to the same data and to give a conclusion based on these results.展开更多
岩相及孔隙度预测在油气勘探中非常重要,为此,提出一种基于多阈值BIRCH(Balanced Iterative Reducing and Clustering Using Hierarchies)聚类的岩相预测方法,并以此为基础利用岭回归算法预测孔隙度。首先,根据地震波阻抗数据分布规律...岩相及孔隙度预测在油气勘探中非常重要,为此,提出一种基于多阈值BIRCH(Balanced Iterative Reducing and Clustering Using Hierarchies)聚类的岩相预测方法,并以此为基础利用岭回归算法预测孔隙度。首先,根据地震波阻抗数据分布规律启发式设定初始阈值,根据簇之间体积的不一致性,动态增加阈值,使用Agglomerative算法进行全局聚类以划分岩相;然后,以井点处孔隙度和地震波阻抗数据为输入,在同一岩相内采用改进的岭回归方法预测孔隙度。模型实验表明,多阈值BIRCH聚类方法具有良好的稳定性和较高的计算效率,岩相划分准确。实际数据结果表明,该方法能够准确预测孔隙度。展开更多
文摘Clustering is an unsupervised learning method used to organize raw data in such a way that those with the same (similar) characteristics are found in the same class and those that are dissimilar are found in different classes. In this day and age, the very rapid increase in the amount of data being produced brings new challenges in the analysis and storage of this data. Recently, there is a growing interest in key areas such as real-time data mining, which reveal an urgent need to process very large data under strict performance constraints. The objective of this paper is to survey four algorithms including K-Means algorithm, FCM algorithm, EM algorithm and BIRCH, used for data clustering and then show their strengths and weaknesses. Another task is to compare the results obtained by applying each of these algorithms to the same data and to give a conclusion based on these results.
文摘岩相及孔隙度预测在油气勘探中非常重要,为此,提出一种基于多阈值BIRCH(Balanced Iterative Reducing and Clustering Using Hierarchies)聚类的岩相预测方法,并以此为基础利用岭回归算法预测孔隙度。首先,根据地震波阻抗数据分布规律启发式设定初始阈值,根据簇之间体积的不一致性,动态增加阈值,使用Agglomerative算法进行全局聚类以划分岩相;然后,以井点处孔隙度和地震波阻抗数据为输入,在同一岩相内采用改进的岭回归方法预测孔隙度。模型实验表明,多阈值BIRCH聚类方法具有良好的稳定性和较高的计算效率,岩相划分准确。实际数据结果表明,该方法能够准确预测孔隙度。
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60273043)安徽省自然科学基金(the Natural Science Foundation of Anhui Province of China under Grant No.050460402)
