RT Journal Article
T1 A high dimensional functional time series approach to evolution outlier detection for grouped smart meters
A1 Elías Fernández, Antonio
A1 Morales-González, Juan Miguel
A1 Pineda-Morente, Salvador
K1 Medición - Investigación
AB 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 High Dimensional Functional Time Series 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 are 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.
PB Taylor and Francis
YR 2022
FD 2022-01-01
LK https://hdl.handle.net/10630/25690
UL https://hdl.handle.net/10630/25690
LA eng
NO This work was supported in part by the Spanish Ministry of Science and Innovation through project PID2020-115460GB-I00, and in part by the Andalusian Regional Government through project P20-00153, and in part by the Research Program for Young Talented Reseachers of the University of Málaga under Project B1-2020-15. This project has also received funding from the European Social Fund and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 755705).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026