Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle

TL;DR

This paper introduces a multi-timescale event detection method for non-intrusive load monitoring using the MDL principle, improving detection accuracy across various appliances and scenarios by addressing load fluctuations and multi-scale characteristics.

Contribution

It extends a two-stage event detection framework with a novel multi-timescale approach based on MDL, incorporating motif discovery and VAD for enhanced accuracy and robustness.

Findings

Achieves higher detection accuracy on public and private datasets.

Effectively handles load fluctuations and multi-timescale events.

Demonstrates improved detection integrity across different appliances.

Abstract

Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM). However, existing event detection methods with fixed parameters may fail in coping with the inherent multi-timescale characteristics of events and their event detection accuracy is easily affected by the load fluctuation. In this regard, this paper extends our previously designed two-stage event detection framework, and proposes a novel multi-timescale event detection method based on the principle of minimum description length (MDL). Following the completion of step-like event detection in the first stage, a long-transient event detection scheme with variable-length sliding window is designed for the second stage, which is intended to provide the observation and characterization of the same event at different time scales. In that, the context information in the aggregated load data is mined by motif discovery, and then based on the MDL principle, the proper observation scales are selected for different events and the corresponding detection results are determined. In the post-processing step, a load fluctuation location method based on voice activity detection (VAD) is proposed to identify and remove the unreasonable events caused by fluctuations. Based on newly proposed evaluation metrics, the comparison tests on public and private datasets demonstrate that our method achieves higher detection accuracy and integrity for events of various appliances across different scenarios.