基于强化学习的异常用电判决方法

目前异常用电检测问题有许多基于分类的方法，但大多都是基于短期用电行为的判决来判断长期用电行为，判决阈值与比例难以确定，且在实际应用中，不同区域、时段的用户用电数据分布差异较大，比例与阈值也会有较大的不同，难以以固定的比例通用于所有的用户数据。针对此问题，文中提出一种基于强化学习的异常用电判决方法，创新地利用强化学习模型来动态生成阈值，以适应差异较大的不同数据集。首先获取分类器输出的数个用户短期行为的异常概率，然后输入到强化学习模型深度递归Q网络（DRQN）中，学习得到动态阈值即判决阈值与判决比例。试验结果表明，相比于人工调参的传统投票法，文中方法在评估指标上有明显提升，面对数据分布差异较大的数据集时也有较好的表现，说明文中方法具有较强的泛化能力，在数据类型复杂的现实环境中也有较好的应用场景。

Judgment method of abnormal electricity consumption based on reinforcement learning

There are many classification-based methods for abnormal electricity consumption detection now,but most of them are based on short-term electricity consumption to judge long-term electricity consumption behavior. It is difficult to determine the threshold and ratio of these methods. In engineering application,the distribution of power consumption data in different regions and time periods is quite different,so the proportion and threshold value are quite different. It is difficult to apply the fixed proportion to all user data. To solve this problem,a method for judging abnormal electricity consumption based on reinforcement learning is proposed,which innovatively uses reinforcement learning model to dynamically generate threshold for different data sets. Firstly,the abnormal probability of the short-term behavior of several users output by the classifier is obtained. Then,the dynamic threshold is obtained by inputing the probability into the deep recurrent Q network (DRQN)of the enhanced learning model,where,the dynamic threshold can be Judgment threshold and judgment ratio as well. The experimental results show that,compared with the traditional voting method of manual parameter adjustment,this method has a significant improvement in the evaluation index,and also has a good performance in data sets with large differences in data distribution. It shows that this method has strong generalization ability in the real environment with complex data types.