稀疏编码模型在电力用户异常用电行为探测中的应用研究(英文)

Application of Sparse Coding in Detection for Abnormal Electricity Consumption Behaviors

针对电力用户用电异常行为的检测问题,提出一种新颖的使用稀疏编码的模型方法来挖掘用户的原始用电数据。结合字典学习的方法,将用户数据表示成其中部分特征的线性组合的形式,通过各个特征的使用频率来判断异常值,从而分辨出用户用电行为模式和异常行为。通过某城市9038户居民538天的实际用电数据,验证了所提方法的有效性和可行性。

A novel abnormality detection method for electricity consumption behaviors is proposed, where a sparse coding model is utilized to mine the raw energy data. With the use of a dictionary learning method, the energy consumption of each resident can be denoted as a linear combination of some patterns, from which outliers can be distinguished through the usage frequency of different patterns. The effectiveness and feasibility of our method is verified by the experiment with actual consumption data from 9038 residents in some cities in 538 days.