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Smart metering infrastructures collect data almost continuously in the formof fine-grained long time series. These massive data series often have commondaily patterns that are repeated between similar days or seasons and sharedamong grouped meters. Within this context, we propose an unsupervised method tohighlight individuals with abnormal daily dependency patterns, which we termevolution outliers. To this end, we approach the problem from the standpoint ofFunctional Data Analysis (FDA) and we use the concept of functional depth toexploit the dynamic group structure and isolate individual meters with adifferent evolution. The performance of the proposal is first evaluatedempirically through a simulation exercise under different evolution scenarios.Subsequently, the importance and need for an evolution outlier detection methodis shown by using actual smart-metering data corresponding to photo-voltaicenergy generation and circuit voltage records. Here, our proposal detectsoutliers that might go unnoticed by other approaches of the literature thathave demonstrated to be effective capturing magnitude and shape abnormalities.

A Functional Data Analysis Approach to Evolution Outlier Detection for Grouped Smart Meters