Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Abstract

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

Domestic smart metering infrastructure and a method for home appliances identification using low‐rate power consumption data

“…In both [20,21], two event-based NILM classification algorithms using high sampled current data are presented. In both works, image-like representations of the signals are developed and then introduced to a CNN for the classification task.…”

“…In both works, image-like representations of the signals are developed and then introduced to a CNN for the classification task. Specifically, from the one-cycle activation current of each appliance a weighted recurrence graph is developed in [20] and the Fryze power theory is used in order to decompose it into its active and non-active components and, subsequently, the 2D Euclidean-distance similarity matrix is used to represent the decomposed current signal into an image [21]. The methods presented in [20,21] are evaluated using the PLAID dataset, which contains measurements sampled at 30 kHz and the method in [20] is also tested using LILACD which is an industrial dataset with three phase data sampled at 50 kHz.…”

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

“…In order to detect the active appliances at each time step, the idea is to associate each value of the main power to a vector of labels of length equal to the number of appliances, that are set to 1 if the appliance is active and 0 otherwise. The reformulated problem has been solved with different approaches [10][11][12]. However, there is no direct way to derive the power consumption for each appliance at that time step.…”

A Novel Method for Obtaining the Signature of Household Consumer Pairs

“…The CNN algorithm is used by Faustine in [14,15]. In both papers a weighted recurrent graph, based on Euclidean distance similarity function, is used to map one-cycle current into an 'colored' image.…”

“…In [14], the event detection underlies the classification. An interesting approach emerges in [15] where Faustine applies the CNN to one-cycle of nonactive current (Fryze-current decomposition) that contains more than one appliance.…”