基于半监督支持向量机的电力现货市场串谋识别

Collusion Identification of Electricity Spot Market Based on Semi-Supervised Support Vector Machine

针对现货市场中有标签的串谋数据稀缺的问题,设计了基于半监督支持向量机的串谋识别模型。首先,设计一套机组对的串谋识别指标体系并介绍其详细的计算方法。其次,利用Delphi法修正后的Topsis模型对机组进行初步判定,构建串谋识别训练集,将机组串谋的可能性分为“高”、“中等”和“低”3种可能。然后,提出了基于半监督支持向量机的串谋识别模型,利用有标签的串谋识别样本训练支持向量机模型,并对无标签样本进行标记,选择使分类间隔最大的那组标签作为最终标签,将原有标签样本和新增有标签样本混合,重新训练串谋识别模型。最后,以某地区现货市场数据进行算例分析,验证了半监督支持向量机算法的有效性。

In order to solve the problem of scarcity of labeled collusion data in the spot market,a collusion recognition model based on semi-supervised support vector machine is designed.Firstly,a set of collusion identification index system is designed and its detailed calculation method is introduced.Secondly,the Topsis model modified by Delphi method is used to make a preliminary judgment of the unit,and the collusion recognition training set is constructed.The possibilities of collusion of the unit are divided into“high”,“medium”and“low”.Then,a collusion recognition model based on semi-supervised support vector machine is proposed.The collusion recognition model is trained by using labeled collusion recognition samples,and the unlabeled samples are labeled.The group of labels with the largest classification interval is selected as the final label.The original label samples and the new labeled samples are mixed to retrain the collusion recognition model.Finally,the effectiveness of semi-supervised support vector machine algorithm is verified by an example of spot market data in a certain region.