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 and Jean-Michel Clairand
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Xavier Serrano-Guerrero: Grupo de Investigación en Energías, Universidad Politécnica Salesiana, Cuenca 010103, Ecuador
Guillermo Escrivá-Escrivá: Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, s/n, edificio 8E, escalera F, 2a planta, 46022 Valencia, Spain
Santiago Luna-Romero: Grupo de Investigación en Energías, Universidad Politécnica Salesiana, Cuenca 010103, Ecuador
Jean-Michel Clairand: Facultad de Ingeniería y Ciencias Aplicadas, Universidad de las Américas—Ecuador, Quito 170122, Ecuador

Energies, 2020, vol. 13, issue 5, 1-23

Abstract: 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.

Keywords: electricity consumption profiles; electricity consumption patterns; building management systems; outlier detection; time-series treatment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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