随着集中式电力市场规模的扩大,需要设置电力枢纽节点作为现货市场上可以进行统一交易的聚合节点,其同时也是电力金融市场发展与稳定运行的基石,对构建统一电力市场体系、完善市场功能具有十分重要的意义。枢纽节点设计的难点在于枢纽...随着集中式电力市场规模的扩大,需要设置电力枢纽节点作为现货市场上可以进行统一交易的聚合节点,其同时也是电力金融市场发展与稳定运行的基石,对构建统一电力市场体系、完善市场功能具有十分重要的意义。枢纽节点设计的难点在于枢纽节点数量的确定,需要通过合适的数量选取以保证对电力市场定价节点的准确覆盖,体现电力空间价值。针对枢纽节点数量选取这一枢纽节点设计的关键问题,提出了一种基于t-SNE(t-distributedstochasticneighbor embedding)降维和DBSCAN(density-based spatial clustering of applications with noise)分类的枢纽节点数量确定方法。首先,通过与KPCA(kernelprincipalcomponentanalysis)、UMAP(uniform manifold approximation and projection)等典型降维方法的对比实验,证明t-SNE对数据拥挤的高维节点电价集有更好的降维效果,其数据可视化效果符合通过降维使得定价节点分成尽可能独立的类的预期。其次,应用DBSCAN算法在基于密度的基础上去除异常点与偏离点并进行分类,通过交叉熵有效选取DBSCAN最佳域值,确定最优分类数。最后,通过一系列分类的内部有效性评价指标,证明了该方法的准确性与有效性,为进一步的枢纽区域划分提供合理依据。展开更多
Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. ...Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.展开更多
文摘随着集中式电力市场规模的扩大,需要设置电力枢纽节点作为现货市场上可以进行统一交易的聚合节点,其同时也是电力金融市场发展与稳定运行的基石,对构建统一电力市场体系、完善市场功能具有十分重要的意义。枢纽节点设计的难点在于枢纽节点数量的确定,需要通过合适的数量选取以保证对电力市场定价节点的准确覆盖,体现电力空间价值。针对枢纽节点数量选取这一枢纽节点设计的关键问题,提出了一种基于t-SNE(t-distributedstochasticneighbor embedding)降维和DBSCAN(density-based spatial clustering of applications with noise)分类的枢纽节点数量确定方法。首先,通过与KPCA(kernelprincipalcomponentanalysis)、UMAP(uniform manifold approximation and projection)等典型降维方法的对比实验,证明t-SNE对数据拥挤的高维节点电价集有更好的降维效果,其数据可视化效果符合通过降维使得定价节点分成尽可能独立的类的预期。其次,应用DBSCAN算法在基于密度的基础上去除异常点与偏离点并进行分类,通过交叉熵有效选取DBSCAN最佳域值,确定最优分类数。最后,通过一系列分类的内部有效性评价指标,证明了该方法的准确性与有效性,为进一步的枢纽区域划分提供合理依据。
文摘Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.
