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

TL;DR

This paper introduces an unsupervised functional data analysis method to detect evolution outliers in smart meter data, effectively identifying abnormal patterns in large, grouped time series data from smart grids.

Contribution

It presents a novel FDA-based approach using functional depth to detect evolution outliers in grouped smart meter data, outperforming existing methods.

Findings

Successfully detects outliers in simulated data under various scenarios.

Effectively identifies abnormal patterns in real smart meter data.

Outperforms existing approaches in capturing subtle abnormalities.

Abstract

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