A time-series treatment method to obtain electrical consumption patterns for anomalies detection improvement in electrical consumption profiles

Xavier Serrano-Guerrero, Guillermo Escrivá-Escrivá, Santiago Luna-Romero, Jean Michel Clairand

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

3 Citas (Scopus)

Resumen

Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.

Idioma originalInglés
Número de artículo1046
PublicaciónEnergies
Volumen13
N.º5
DOI
EstadoPublicada - 1 ene 2020

Nota bibliográfica

Publisher Copyright:
© 2020 by the authors.

Huella

Profundice en los temas de investigación de 'A time-series treatment method to obtain electrical consumption patterns for anomalies detection improvement in electrical consumption profiles'. En conjunto forman una huella única.

Citar esto