Non-Intrusive Load Monitoring of Residential Loads via Laplacian Eigenmaps and Hybrid Deep Learning Procedures

Abstract

Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE), a convolutional neural network (CNN), and a recurrent neural network (RNN), called LE-CRNN. In the proposed model architecture, LE, with its high ability in dimensional reduction, transfers the salient features and specific values of power consumption curves (PCCs) of household electrical appliances (HEAs) to a low-dimensional space. Then, the combined model of CRNN significantly improves the structure of CNN in fully connected layers so that the process of identification and separation of the HEA type can be performed without overfitting problems and with very high accuracy. In order to implement the suggested model, two real-world databases have been used. In a separate scenario, a conventional CNN is applied to the data for comparing the performance of the suggested model with the CNN. The designed networks are trained and validated using the PCCs of HEAs. Then, the whole energy consumption of the building obtained from the smart meter is used for load disaggregation. The trained networks, which contain features extracted from PCCs of HEAs, prove that they can disaggregate the total power consumption for houses intended for the Reference Energy Disaggregation Data Set (REDD) and Almanac of Minutely Power Dataset (AMPds) with average accuracies (Acc) of 97.59% and 97.03%, respectively. Finally, in order to show the accuracy of the developed hybrid model, the obtained results in this study are compared with the results of similar works for the same datasets.

AttG-BDGNets: Attention-Guided Bidirectional Dynamic Graph IndRNN for Non-Intrusive Load Monitoring

“…Neural network models also devise an evaluation method that relies on the device-predicted total and ground-truth energies to provide details on the algorithmically predicted total overlapping energies, missing energies, and extra energies. Moradzadeh A. et al [25] combined the Laplacian feature map (LE), convolutional neural network (CNN), and recurrent neural network (RNN) to transfer the significant features and specific values of the energy consumption curve of household appliances to a lowdimensional space and use the recurrent convolutional network to improve the structure of the fully connected layer significantly CNN, so that there is no over-fitting problem in the identification and separation of HEA types, and it has high accuracy. Nie Z. et al [26] used a sequential point deep neural network and constructed a comprehensive strategy non-intrusive load monitoring technology based entirely on the deep feature-guided attention mechanism.…”

A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images

“…Since then, the development of NILM has been evolving rapidly. However, in recent years, NILM research has entered a new era of development, especially due to the exponential growth of deep learning models [6]. A common deep learning model for load disaggregation by NILM is the convolutional neural network (CNN).…”