**1. Introduction**

Recently, most of the world has witnessed a rapid increase in energy use in buildings (residential and commercial). Residential and commercial buildings consume approximately 60% of the world's electricity (The United Nation's Environment Programme's Sustainable Building and Climate Initiative (UNEP-SBCI)). Energy efficiency and conservation in buildings can be generally achieved through replacing devices with more efficient ones, improving the efficiency of the building (for example, using better insulation), or optimizing energy usage through behavior changes and application of cost-effective technologies [1]. Unlike other strategies for building energy saving, optimizing energy use through behavior changes is very fast and highly profitable. With the application of cost-effective technologies, this strategy can also provide end-use appliances consumption to households that give insight into what appliances are used, when they are used, how much power they consume, and why such consumption [2]. The end-use appliances consumption is also useful for estimating the amount of energy demand at consumer premises [3]. It further increases the awareness about the energy consumption behavior of consumers.

The recent advance in energy-sensing and smart-meter technologies has led to the rise of Non-Intrusive Load Monitoring (NILM) [4,5]. NILM is a computational technique that uses aggregate power data monitored from a single point source such as a smart meter or current or voltage sensor-plug to infer the end-appliances running in the building and estimate their respective power consumption [6]. It relies on signal processing and machine learning techniques that analyze appliance patterns from aggregate power measurements. NILM provides households with cost-effective monitoring of appliance-specific energy consumption, and it can be easily integrated into existing buildings without causing any inconvenience to inhabitants. Several machine learning techniques have been proposed to address the energy-disaggregation [7–12].

Recognizing appliances from aggregated power measurements is one of the vital sub-tasks of NILM [11,13]. It uses machine learning techniques to analyze the pattern of the electrical features vector extracted from aggregated measurements and classifies them into the respective appliance category. Feature vectors are obtained after the state-transitions of appliances have been detected. These features are extracted at different sampling rates (high-frequency or low-frequency) depending on the measurements and electrical characteristics needed by the NILM algorithm [14]. The high sampling frequency offers the possibility to consider fine-grained features such as voltage-current (V-I) trajectory, harmonics, wavelet coefficients from steady-state, and transient behavior. As a result, several techniques for appliance recognition applying high-frequency features such as V-I trajectory have been proposed [15,16].

It has been demonstrated that transforming the V-I trajectory into image representation and feeding it as the input to machine learning classifiers improves classification performance [11,14,17–21]. However, the presented works use single-label learning, thus assuming that only one appliance is active at a time. This strategy ignores the fact that multiple devices can be active simultaneously as well as dependencies between appliance usage. It further requires a perfect event detector to extract the appliance features just after an event has been detected, particularly in aggregated measurements [19]. In contrast to single-label learning, multi-label learning links multiple appliances to an observed aggregate power signal [22,23].

Several studies have demonstrated that multi-label learning represents a viable alternative to conventional NILM approaches [23–27]. For example, the work by [25], investigated the possibility of applying a temporal multi-label classification approach in non-event based NILM where a novel set of meta-features was proposed. In [26] an extensive survey for the multi-label classification and the multi-label meta-classification framework for low-sampling power measurements is presented. Recent studies have explored deep neural networks for multi-label appliance recognition in NILM [23,24], ye<sup>t</sup> these approaches also rely on low-frequency data.

Instead, this paper presents a CNN-based multi-label appliance classification approach. The proposed method uses the current waveform generated from the aggregated measurements, taken in brief windows of time containing one or more than one event. The underlying assumption of this method is that the extracted aggregated current will be the summation of active appliances. Therefore, by training a classification model to learn the patterns of different combinations, it is possible to successfully identify such appliances when these appear in a future time window.

To improve the discriminating power of our method, we apply the *Fryze power theory*, which enables the decomposition of the current waveform into active and non-active components in time-domain [20,28]. Our research hypothesis is that the sum of the active and non-active components will exhibit unique and consistent characteristics based on the appliances that are running simultaneously, hence providing a distinctive feature for multi-label classification.

The decomposed current is then transformed into an image representation using the Euclidean-distance-similarity matrix [29] and fed into the CNN for multi-label classification. The proposed approach is evaluated against the PLAID dataset [30], which consists of aggregated voltage and current measurements at a 30 kHz sampling rate. The source code used in our experiments is publicly available on a GitHub repository (https://github.com/sambaiga/MLCFCD).

The main contribution of this paper is a multi-label learning strategy for appliance recognition in NILM. The proposed approach associates multiple appliances to an observed aggregated current signal. Overall, this contribution folds into four sub-contributions


The remainder of this paper is organized as follows: Section 2 summarizes related works while Section 3 introduces the methods utilized in this work. Section 4 describes the experimental design. Section 5 presents the results and discussion of the performed evaluations. Finally, Section 6 summarises the contributions of this paper and suggests future research direction.

## **2. Related Works**

The concept of multi-label classification for NILM has gained momentum recently, as the systematic review finds in [26]. Besides an extensive survey of the topic, the authors present the multi-label meta-classification framework (RA*k*EL) and the bespoke multi-label classification algorithm (ML*k*NN), where both employ time-domain and wavelet-domain feature sets. Other approaches to multi-label NILM comprise restricted Boltzmann machines [31], and multi-target classification [32].

In [33], the authors present an algorithm that uses Sparse Representation based classification for multi-label NILM. Furthermore, the authors compare their algorithm to other cutting edge multi-label NILM approaches such as classification based on extreme learning machines (ELM) [34], graph-based semi-supervised learning [35], and an approach based on deep dictionary learning and deep transform learning [36]. Nalmpantis and Vrakas [37] present a multi-label NILM based on the Signal2Vec algorithm that maps any time series into a vector space. A deep neural network (DNN) based multi-label NILM applying active power features at low-sampling frequency is proposed in [23,24]. In [23], the authors propose an approach that builds on Temporal Convolutional Networks (TCNN). At the same time, Massidda et al. [24] applied Fully Convolutional Networks (FCNN) for multi-label-learning in NILM, adopting some methods used in semantic segmentation.

Even though multi-label learning was found to be competitive with state-of-the-arts NILM algorithms, none of the previous works have considered the V-I trajectory-based features for multi-label-classification. Existing NILM methods that use V-I based features for appliance classification uses single-label learning [11,14–21,38]. The use of V-I based features for appliance classification was first introduced in [15], where shape-based features extracted from V-I (e.g., number of self-interceptions) were used as input to a machine learning classifier. A review and performance evaluation of the seven load wave-shape is presented in [39]. The shape-based feature was found to have a direct correspondence to operating characteristics of appliances as contained in the current wave-shape. Several other features such as asymmetry, mean line and self-intersection assessment extracted from V-I waveforms were used to classify appliances in [16]. However, this approach compresses the information in the V-I-trajectory into a limited amount of features extracted solely based on deep engineering knowledge.

Against this background, other researchers demonstrated that transforming the V-I trajectory into a binary image representation improves classification performance [17,18] by leveraging on

state-of-the-art deep-learning algorithms for image recognition. For example, De Baets et al. [14,19] transforms the V-I trajectory into weighted pixelated V-I images and uses a CNN classifier. In another work, a hardware implementation of the appliance recognition system based on V-I curves and a CNN classifier is also proposed [21]. The work by [20,28] demonstrated that applying the Fryze power theory to decompose the current into active and non-active components could enhance the uniqueness of the V-I binary image and consequently improve classification performance. The work by Teshome et al. [28] applied the non-active current and non-active voltage (V-*If*) for appliance recognition. Liu et al. [20] further demonstrated that the visual representation of (V-*If*) is robust enough to be used in Transfer learning. Recently it has been shown that transforming the V-I into compressed distance similarity matrix consistently improves the appliance classification performance compared to the commonly used V–I image representation [11,13].

Motivated by these two works, we apply decomposed currents as input features for recognizing multiple running appliances. Still, unlike Liu et al. [20], and Teshome et al. [28], we transform the decomposed current into a 2D Euclidean-distance similarity matrix, which is later used as the input to the CNN model.

## **3. Proposed Methods**

The goal of appliance recognition in NILM is to identify appliance states *s<sup>m</sup> t* from the aggregate measurements *xt* composed of individual appliance measurements *y<sup>m</sup> t* such that *m* = 1, 2, ... *M*, where *M* indicates the number of appliances such that

$$\mathbf{x}\_{t} = \sum\_{m=1}^{M} y\_{t}^{m} \cdot s\_{t}^{m} + \mathbf{c}\_{t} \tag{1}$$

where *t* represents both any contribution from appliances not accounted for and measurement noise [40]. We refer to this as a multi-label NILM problem, where given observed aggregate measurements, the unobserved appliance states **s***t* of electrical appliances are estimated.

Specifically, the problem is formulated as follows: Let **X** ∈ R*T*×*<sup>d</sup>* = {*<sup>x</sup>*1 ... *xT*} denote a set of input features derived from the aggregate measurements of *M* appliances and **Y** ∈ R*T*×*<sup>M</sup>* indicates the associated appliance measurements, where each appliance has *k* states denoted as **s***<sup>m</sup>* = {*s<sup>m</sup>* 1 , ...*s<sup>m</sup> k* } such that *s<sup>m</sup> k* ∈ {0, <sup>1</sup>}. The matrix **S** ∈ R*T*×*<sup>M</sup>* indicates the associated multi-label states. Thus, given *D* = {**<sup>x</sup>***t*, **<sup>s</sup>***t*|*<sup>t</sup>* = 1, ... , *T*} datasets, the goal is to learn a multi-label classifier that predicts the state vector **st** = {*s<sup>m</sup> t* ,...*s<sup>M</sup> t* } from the input aggregate power feature vector **x***t*.

The proposed approach is summarized in Figure 1.

```
voltage measurements
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#### *3.1. Feature Extraction from Aggregate Measurements*

In this work, we consider the appliance feature extracted from high-frequency aggregate voltage and current measurements in brief windows of time. This feature contains one or more than one event and allows us to distinguish multiple appliances running simultaneously, as illustrated in Figure 2a. We define an activation current **i** and voltage **v** to be a one-cycle steady-state signal extracted from the aggregate current and voltage waveform.

To obtain the activation waveforms from aggregate measurements we measure *Nc* = 20 cycles of current and voltage {**i**(*a*), **v**(*a*)} after an event. As depicted in Figure 2b, the *Nc* circles correspond to steady-state behavior and is equivalent to *Ts* × *Ns* samples where *Ts* = *fsf* , *fs* is sampling frequency and *f* is mains frequency. These cycles are aligned at the zero-crossing of the voltage and there-after one-cycle activation current **i** and voltage **v** with size *Ts* is extracted, as illustrated in Figure 2.

**Figure 2.** (**a**) Aggregated current signal for different events. (**b**) Twenty cycles of current after an event. (**c**) Extracted activation currents for different events. The activation current of the event at time **i** is the summation of the activation current for the all running appliances.
