NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Abstract

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

“…The NILM technique was introduced by Hart's pioneering work in the mid-1980s when he was the first to use active and reactive power transient analysis to detect when household appliances were turned on and off. Since then, many research papers have been published as well as several excellent state of the art reviews, such as [5][6][7] and, more recently [8].…”

“…Following the approach described in [8], we can describe the disaggregation process through three steps, the identification of events, the synthesis of necessary and optimal features for classification, and the actual load classification and disaggregation. The events correspond to changes in the state of household appliances, and are used to synthesize features that are then used for load classification.…”

“…The events correspond to changes in the state of household appliances, and are used to synthesize features that are then used for load classification. In [8], a characterization is proposed on the basis of the sampling rate of the features themselves, which are divided into very slow (less than 1 min), slow (between 1 min and 1 s), medium (higher than 1 Hz but slower than the fundamental frequency), high (up to 2 kHz) and very high (between 2 and 40 KHz). The features of the slow and very slow category can be used directly, as in [9,10], through statistical characterization of time-series sub-sequences as well as with signal processing [11][12][13].…”

The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

“…The review article "NILM techniques for intelligent home energy management and ambient assisted living: A review" authored by A. Ruano, A. Hernandez, J. Ureña, M. Ruano, and J. Garcia provided a constructive review on home energy management and ambient assisted living [4]. The focus was on NILM, which aimed at producing a breakdown of the energy profile in equipment level based on the total energy profile of the apartment.…”

“…In [7], T. T. H. Le and H. Kim presented an article "Non-intrusive load monitoring based on novel transient signal in household appliances with low sampling rate". Differing from [4], the authors divided the NILM framework into three parts: data acquisition, feature extraction, and classification model. The feature vector was constructed by transient signal.…”