Robust energy disaggregation using appliance-specific temporal contextual information

Pascal Alexander Schirmer,Iosif Mporas,Akbar Sheikh-Akbari

doi:10.1186/s13634-020-0664-y

Abstract

An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.

Highlights

In the last decades, rising energy consumption needs within residential and industrial environments have become a crucial issue with nowadays consumer households accounting for approximately 40% of the total worldwide consumed energy [1, 2]
We propose the integration of temporal contextual information for each electrical appliance in the form of concatenation of adjacent feature vectors within a devicedependent time window to improve device detection performance in non-intrusive load monitoring (NILM)
The best performing length of the temporal contextual window w for each of the evaluated datasets is indicated in italics

Summary

Introduction

In the last decades, rising energy consumption needs within residential and industrial environments have become a crucial issue with nowadays consumer households accounting for approximately 40% of the total worldwide consumed energy [1, 2]. A smart meter, referred to as a smart plug, is a device used to measure electrical power/energy consumption with resolution in the order of seconds to minutes. Smart meters measure the voltage drop over the device/ circuit and the current flowing through the device/circuit with an arbitrary sampling frequency fs, which usually varies from 1/60 Hz to 30 kHz [11]. Higher sampling frequencies are usually preferred, since they contain more detailed information about the energy consumption; they increase linearly the amount of acquired data

Methods

Results

Conclusion