Abstract
A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.
Highlights
Introduction for Nonintrusive Load MonitoringThe massive use of electrical appliances in industrial, residential, and commercial buildings continues to increase
We focus on targeted features based on the transient current signals as opposed to traditional approaches consisting of extracting descriptors massively [21] or new blind approaches based on deep learning [22,23,24]
Controlled On/Off Loads Library (COOLL) is a high-sampled current and voltage measurement dataset for individual appliance consumption
Summary
Introduction for Nonintrusive Load MonitoringThe massive use of electrical appliances in industrial, residential, and commercial buildings continues to increase. The diversity of the devices used has increased, especially with the remarkable evolution of technology, most notably in the last few decades This truth imposes an urgent need to control the energy consumption related to these loads in order to more closely monitor consumption and to act when necessary in the case of anomalies. According to [5], the best way to optimize energy savings is to monitor the consumption per device [1] This essentially requires load desegregation techniques that can be performed by intrusive or nonintrusive load monitoring methods (ILM or NILM) [6]. Nonintrusive load monitoring (NILM) consists of determining the individual energy consumption of appliances connected to the electrical grid [7] This can be done by measuring current and voltage signals from one measurement point in order to apply signal processing and machine learning methods to desegregate appliances. Beyond classical features used for NILM as the step changes in real power
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