Novel Approaches for Detecting Frauds in Energy Consumption

The classification problem is recurrent in the context of supervised learning. A classification problem is a class of computational task in which labels must be assigned to object instances using information acquired from labeled instances of the same type of objects. When these objects contain time sensitive data, special classification methods could be used to take ad- vantage of the inherent extra information. As far as this paper is concerned, the time sensitive data are sequences of values that represent the measured energy consumption of residential clients in a given month. Traditional classifiers do not take temporal features into account, interpreting them as a series of unrelated static information. The proposed method is to develop methods of classification to be applied in a real time-series problem that somehow consider the time series as being the same value being repeatedly measured. Two new approaches are suggested to deal with this problem: the first is a Hybrid classifier that uses clustering, DTW (Dynamic Time Warp) and Euclidean distance to label a given instance. The second is a Weighted Curve Comparison Algorithm that creates consumption profiles and compares them with the unknown instance to classify it.