# Appliance Event Detection -- A Multivariate, Supervised Classification Approach

## TL;DR

This paper presents a multivariate supervised classification approach for appliance event detection in NILM, effectively reducing false positives, especially for noisy appliances like SMPS, through adaptive boosting and feature optimization.

## Contribution

It introduces a boosting-oriented adaptive training method and evaluates multiple features and classifiers to improve event detection accuracy in NILM.

## Key findings

- False positives reduced by over eight times on SMPS appliances
- Adaptive training improves detection stability across datasets
- Optimal feature and classifier configurations enhance performance

## Abstract

Non-intrusive load monitoring (NILM) is a modern and still expanding technique, helping to understand fundamental energy consumption patterns and appliance characteristics. Appliance event detection is an elementary step in the NILM pipeline. Unfortunately, several types of appliances (e.g., switching mode power supply (SMPS) or multi-state) are known to challenge state-of-the-art event detection systems due to their noisy consumption profiles. Classical rule-based event detection system become infeasible and complex for these appliances. By stepping away from distinct event definitions, we can learn from a consumer-configured event model to differentiate between relevant and irrelevant event transients. We introduce a boosting oriented adaptive training, that uses false positives from the initial training area to reduce the number of false positives on the test area substantially. The results show a false positive decrease by more than a factor of eight on a dataset that has a strong focus on SMPS-driven appliances. To obtain a stable event detection system, we applied several experiments on different parameters to measure its performance. These experiments include the evaluation of six event features from the spectral and time domain, different types of feature space normalization to eliminate undesired feature weighting, the conventional and adaptive training, and two common classifiers with its optimal parameter settings. The evaluations are performed on two publicly available energy datasets with high sampling rates: BLUED and BLOND-50.