基于Bagging异质集成学习的窃电检测

针对传统窃电检测中单一分类方法的不足,提出一种基于Bagging异质集成学习的窃电检测方法.考虑不同个体学习器在数据集上的表现以及各学习器之间的多样性,构建多种个体学习器嵌入的Bagging异质集成学习的窃电检测模型,模型的个体学习器包含k最近邻、误差反向传播神经网络、梯度提升树和随机森林,通过引入改进加权投票策略将其输出进行结合.使用爱尔兰智能电表数据集对算法有效性进行验证.结果表明,与传统单一学习器和同质集成学习检测相比,基于Bagging异质集成学习的窃电检测方法的准确率、命中率、误检率等检测指标更好,灵敏性分析验证了基于Bagging异质集成学习的窃电检测方法的有效性.

Electricity Theft Detection Based on Bagging Heterogeneous Ensemble Learning

Aiming at the deficiency of the single classification method in traditional electricity theft detection, this paper proposes an electricity theft detection method based on Bagging heterogeneous ensemble learning. Considering the performance of different individual learners on the data sets and the diversity between various learners, an electricity theft detection model based on Bagging heterogeneous ensemble learning with combination of various individual learners is developed. The individual learners of the model include the k-nearest neighbors, the error back propagation network, the gradient boosting decision tree and random forest. The outputs of the individual learners are combined by an improved weighted voting strategy. The Irish smart meter data set is used to verify the feasibility of the algorithm. The results show that, compared with the traditional single model, the electricity theft detection based on Bagging ensemble learning has better performance in accuracy, true positive rate and false positive rate. The sensitivity analysis shows the validity of the electricity theft detection method based on Bagging heterogeneous ensemble learning.