ENP Engineering Science Journal
Volume 1, Numéro 2, Pages 50-54
2021-12-31
Authors : Sahrane Selim . Haddadi Mourad .
Device-level power consumption information can lead to considerable energy savings. Smart meters are being adopted in several countries, but they are only capable of measuring the total power consumption. NonIntrusive Load Monitoring (NILM) aims to infer the power consumption of individual electrical loads by analyzing the aggregate power signal taken from a single-point measurement. Most existing NILM solutions are offline methods that do not allow the end-user to get real-time feedback on his energy consumption. In this paper, we present a near real-time NILM solution based on multi-label classification and multi-output regression. We use the multi-label classifier to predict the state of each load and use the multi-output regressor to estimate the disaggregated active power consumptions. We test our method using a publically available dataset of real power measurements. Performance results show that the proposed near real-time method can accurately estimate the energy consumption of the targeted loads with an average relative energy error of 1.55 %.
NILM, Load Disaggregation, Multi-label Classification, Multi-output Regression, Energy Estimation, Smart Meters
Aitouche Moh-amokrane
.
Djeddi Mounir
.
Djeddi Mabrouk
.
Mihoubi Abdelhafid
.
pages 119-138.
Ali Abderrazak Tadjeddine
.
Iliace Arbaoui
.
Abdelkader Harrouz
.
Hichem Hamiani
.
Cherif Benoudjafer
.
pages 34-41.
Salah Dhahri
.
Abdelkrim Zitouni
.
pages 924-930.
Mathurin Gogom
.
Tchoffo Moffo
.
Elvis Tokoh
.
John Ngundam
.
pages 01-08.
Meslem Mohamed
.
Bouhal Faycel
.
pages 673-687.