A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring

Author:

Pujić Dea1ORCID,Tomašević Nikola1,Batić Marko1ORCID

Affiliation:

1. Institute Mihajlo Pupin, University of Belgrade, Volgina 15, 11060 Belgrade, Serbia

Abstract

Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.

Funder

Science Fund of the Republic of Serbia

European Union

Ministry of Education, Science, and Technological Development

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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1. Low-Frequency Non-intrusive Load Identification Based on Two-Stage Event Detection Method;Arabian Journal for Science and Engineering;2024-07-23

2. Semi-supervised learning with flexible threshold for non-intrusive load monitoring;Heliyon;2024-07

3. Abnormal Operations Detection of Residential Electric Appliances using Non-Intrusive Load Monitoring;2024 IEEE 9th International Conference for Convergence in Technology (I2CT);2024-04-05

4. Low Complexity Energy Disaggregation Algorithm for Non-intrusive Load Monitoring;Electric Power Components and Systems;2024-03-22

5. Real-Time Non-Intrusive Load Monitoring System for Residential Appliance Identification;2024 4th International Conference on Advanced Research in Computing (ICARC);2024-02-21

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