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

Arash Moradzadeh, Sahar Zakeri, Waleed A. Oraibi, Behnam Mohammadi-Ivatloo (), Zulkurnain Abdul-Malek and Reza Ghorbani
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Arash Moradzadeh: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666, Iran
Sahar Zakeri: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666, Iran
Waleed A. Oraibi: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666, Iran
Behnam Mohammadi-Ivatloo: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666, Iran
Zulkurnain Abdul-Malek: High Voltage and High Current Institute, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Reza Ghorbani: Renewable Energy Design Laboratory (REDLab), Department of Mechanical Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA

Sustainability, 2022, vol. 14, issue 22, 1-16

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.

Keywords: non-intrusive load monitoring; residential load disaggregation; Laplacian eigenmaps; convolutional neural network; bidirectional long short-term memory (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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