Energy Disaggregation for SMEs using Recurrence Quantification Analysis

TL;DR

This paper presents a novel approach using recurrence quantification analysis to disaggregate energy consumption data for SMEs, enabling detection of faults and unauthorized device use to improve energy efficiency.

Contribution

It introduces a recurrence analysis-based method tailored for SMEs to monitor appliance-specific energy usage and identify anomalies or faults.

Findings

Effective detection of faulty appliances

Identification of unexpected device usage

Improved energy management for SMEs

Abstract

Energy disaggregation determines the energy consumption of individual appliances from the total demand signal, which is recorded using a single monitoring device. There are varied approaches to this problem, which are applied to different settings. Here, we focus on small and medium enterprises (SMEs) and explore useful applications for energy disaggregation from the perspective of SMEs. More precisely, we use recurrence quantification analysis (RQA) of the aggregate and the individual device signals to create a two-dimensional map, which is an outlined region in a reduced information space that corresponds to 'normal' energy demand. Then, this map is used to monitor and control future energy consumption within the example business so to improve their energy efficiency practices. In particular, our proposed method is shown to detect when an appliance may be faulty and if an unexpected, additional device is in use.