Non-intrusive load monitoring (NILM) for energy disaggregation using soft computing techniques

C. Puente, R. Palacios, Y. González-Arechavala, E.F. Sánchez-Úbeda

Energies Vol. 13, nº. 12, pp. 3117-1 - 3117-20

Resumen:

Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters.

Resumen divulgativo:

El análisis de las señales de medidores inteligentes de consumo eléctrico, permite identificar patrones de consumo de los usuarios. La metodología propuesta es capaz de terminar el comportamiento de cada dispositivo eléctrico a partir del análisis de las series de consumo agregado de la casa. Los algoritmos se ha probado con datos de Francia y UK.

Palabras Clave: NILM; disaggregation methods; non-intrusive load monitoring; appliance consumptions; soft computing

Índice de impacto JCR y cuartil WoS: 3,004 - Q3 (2020); 3,200 - Q3 (2022)

Referencia DOI: https://doi.org/10.3390/en13123117

Publicado en papel: Junio 2020.

Publicado on-line: Junio 2020.

Cita:
C. Puente, R. Palacios, Y. González-Arechavala, E.F. Sánchez-Úbeda, Non-intrusive load monitoring (NILM) for energy disaggregation using soft computing techniques. Energies. Vol. 13, nº. 12, pp. 3117-1 - 3117-20, Junio 2020. [Online: Junio 2020]