A Novel Event-based Non-intrusive Load Monitoring Algorithm

TL;DR

This paper introduces an event-based NILM algorithm that improves appliance power profile inference by accurately detecting events, extracting features, and labeling transitions, addressing issues like noise and overlapping consumption.

Contribution

It presents a novel event-based NILM classification method that filters signals, detects events, extracts appliance features, and labels transitions with high accuracy, enhancing disaggregation performance.

Findings

Effective in accurately disaggregating low frequency data

Addresses noise and transient spike issues

Validated on REDD dataset with positive results

Abstract

Non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power consumption signals and accurately detects all events, (ii) extracts specific features of appliances, such as operation modes and their respective power consumption intervals, from their power consumption signals in the training dataset, and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low frequency measured data by existing smart meters.