A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

Abstract

Nowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is affected by the network capacity strictly related to the transmission protocol constraints and the environmental conditions. All those limitations are in contrast with the need of exploiting all possible signal details for the task under study. In the specific application of interest, i.e., Non-Intrusive Load Monitoring (NILM), they could lead to low performance in the energy disaggregation process. To overcome these issues, an ad hoc data reduction policy needs to be adopted, in order to reduce the acquisition and elaboration burden of the device, and, at the same time, to ensure compliance with network bandwidth limits while maintaining a reliable signal representation. Moved by these motivations, an extended evaluation study concerning the application of data reduction strategy to the aggregate signal is presented in this work. In particular, a non-uniform subsampling (NUS) scheme is defined together with a uniform subsampling (US) strategy and compared, in terms of disaggregation performance, with the use of data at original sampling (OS) rate. A Deep Learning based technique is used for disaggregation, having the aggregate active power signal sampled according to diverse sampling schema mentioned above as input. The approaches are tested on the UK-DALE and REDD datasets, and the combination of US+NUS configurations allows for achieving a good performance in terms of F 1 -score, even superior than the one obtained with the OS rate, and a remarkable data reduction at the same time.

Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector

“…The accuracies of the NilmTK disaggregation of the test sample for each of the houses tested are generally moderate to low. They are, however, in line with the values found in the literature [25,41]. This uncertainty added to the already existing load forecast uncertainty, which may result in low overall accuracies and lead to failures in delivering committed power to a DR event, meaning penalties.…”

“…REDD and Refit are popular datasets and can be found in several studies [24][25][26]. REDD was released as the first publicly available data set, collected specifically to help researchers conduct NILM and household consumption studies.…”

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

“…The community has therefore focused on both supervised and unsupervised machine learning techniques. Among the supervised techniques, several neural network architectures have been proposed, such as Multi Layer Perceptron (MLP) [29], Convolutional Neural Network (CNN) [30][31][32][33][34][35][36], Recurrent Neural Network (RNN) [30,[37][38][39], Extreme Learning Machine [40], techniques based on Support Vector Machines (SVM) [16,41], K-Nearest Neighbors (kNN) [41,42] naive Bayes classifiers [15], Random Forest classifier [43] and Conditional Random Fields [44]. Among the unsupervised techniques, it was mainly those based on Hidden Markov Model that were used in this field [26,28,[45][46][47][48], although clustering techniques were also used [49,50].…”

Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

“…In this paper, the non-intrusive load identification process is studied. There are already various non-intrusive load identification methods [9][10][11]; however, most research works have been carried out based on a hypothetical condition that each electric appliance only has one running state. In fact, with the development of science and technology, not only the type of electric appliance but also the running state of each electric appliance is increased; that is, lots of electric appliances have multiple states.…”