基于AdaBoost-DT算法的电力市场串谋行为识别研究

针对电力市场中购电商串谋的识别方法定性分析居多,实时性不高的问题,文中提出基于AdaBoost-DT算法的串谋行为智能识别方法,将AdaBoost-DT集成分类算法用于串谋识别中,解决了串谋行为难以量化识别的问题。从串谋机理出发,设计了一套基于任意2个购电商之间的串谋识别指标体系。面对数据不均衡问题,采用过采样法对训练数据集进行增广,利用AdaBoost-DT分类算法训练串谋行为智能识别模型。最后,以月度交易数据为支撑进行算例分析,采用接收者操作特性曲线(ROC)和接收者操作特性曲线下的面积(AUC值)评价模型的识别效果。实验结果表明,该串谋行为识别方法的准确率较高且实时性较好,充分验证了算法的有效性。

Collusive behavior recognition in electricity market based on AdaBoost-DT algorithm

In order to solve the problem of qualitative analysis and low real-time performance of collusion identification methods in power market, this paper proposes an intelligent identification method of collusion behavior based on AdaBoost-DT algorithm, which uses AdaBoost-DT integrated classification algorithm to identify collusion be-havior, and solves the problem that collusion behavior is difficult to identify quantitatively. Firstly, based on the mechanism of collusion, a set of collusion identification index system is designed. In the face of the problem of data imbalance, the oversampling method is used to expand the training data set, and the AdaBoost-DT classifica-tion algorithm is used to train the collusion behavior intelligent identification model. Finally, based on the monthly transaction data, an example is analyzed, and the receiver operating characteristic curve (ROC curve) and the area under the receiver operating characteristic curve (AUC value) are used to evaluate the recognition effect of the model. The experimental results show that the proposed method has good accuracy and real-time perfor-mance, which fully verifies the effectiveness of the algorithm.