Autonomous Load Disaggregation Approach based on Active Power Measurements

TL;DR

This paper introduces an unsupervised, real-time load disaggregation method using active power measurements that autonomously learns appliance models without prior knowledge, improving household energy monitoring.

Contribution

It presents a novel unsupervised algorithm that detects and updates appliance states in real time based on active power data, without needing pre-labeled training.

Findings

Effective disaggregation on real-world data

Learns appliance models dynamically during operation

Handles multi-state appliances with 1s resolution

Abstract

With the help of smart metering valuable information of the appliance usage can be retrieved. In detail, non-intrusive load monitoring (NILM), also called load disaggregation, tries to identify appliances in the power draw of an household. In this paper an unsupervised load disaggregation approach is proposed that works without a priori knowledge about appliances. The proposed algorithm works autonomously in real time. The number of used appliances and the corresponding appliance models are learned in operation and are progressively updated. The proposed algorithm is considering each useful and suitable detected power state. The algorithm tries to detect power states corresponding to on/off appliances as well as to multi-state appliances based on active power measurements in 1s resolution. We evaluated the novel introduced load disaggregation approach on real world data by testing the possibility to disaggregate energy demand on appliance level.