Comprehensive NILM Framework: Device Type Classification and Device Activity Status Monitoring Using Capsule Network

Abstract

Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications-device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution.

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

“…[19] designed a sequenceto-sequence Long-Short-Term Memory (LSTM) network for load recognition. The authors in [20] designed a capsule-network-based LRA, in which Convolutional Neural Network (CNN) extracted latent features from a set of non-overlapping energy measurement data segments. [21] proposed a dual-stream neural network to extract features from current signals.…”

Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

“…On the other hand, non-intrusive load monitoring (NILM) is one of the most promising options for energy disaggregation. It allows users to separate the usage of each device in the house by using the total aggregated power signals collected from a smart meter that is typically installed in a household, while protecting user privacy [11,17]. The goal of NILM is to estimate the specific consumption of each appliance in the house based on aggregated data collected by a smart meter.…”

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

“…The community consequently concentrated on supervised and unsupervised machine learning methods. Some methods of supervised learning with neural network (NN) architectures have been previously presented such as multilayer perceptron (MLP) [18], extreme learning machine [19], convolutional neural network (CNN) [20], and recurrent neural network (RNN) [21], as well as methods based on K-nearest neighbor (KNN) [22], support vector machine (SVM) [23], random forest classifier [24], naïve Bayes classifiers [25], and conditional random fields [26]. Unsupervised learning was principally based on the hidden Markov model (HMM) used in a related area [27]; however, clustering algorithms were also used [28].…”