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Engineering Review : Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstva, Vol. 45 No. 2, 2025.

Izvorni znanstveni članak

https://doi.org/10.30765/er.2774

A deep learning approach to energy disaggregation using TCN layers for appliance and load insights

Sudhir Anakal orcid id orcid.org/0000-0003-0070-7738 ; Faculty of Computer Applications, Sharnbasva University, Kalaburagi, Karnataka 585105, India
B. Gireesha ; Dr. A P J Abdul Kalam School of Engineering, Garden City University, Bengaluru 560 049, India.
L. N. Sastry Varanasi ; Department of Electrical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem 534 101, Andhra Pradesh, India. *
V.B. Murali Krishna orcid id orcid.org/0000-0003-0643-8217 ; Department of Electrical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem 534 101, Andhra Pradesh, India. Department of Electrical and Electronics Engineering, R. V. R & J. C. College of Engineering, Guntur 522 019, India.

* Dopisni autor.

Puni tekst: engleski pdf 1.150 Kb

str. 102-113

preuzimanja: 89

citiraj

Sažetak

Non-Intrusive Load Monitoring (NILM) plays a vital role in energy efficiency by disaggregating appliance-level consumption from aggregated household energy data. This study explores the use of Temporal Convolutional Networks (TCNs) for parallel appliance classification and load prediction, addressing challenges like overlapping energy signatures and long-term temporal dependencies. TCNs, with their dilated causal convolutions and efficient parallel processing, are well-suited for NILM applications, offering improved scalability and accuracy over traditional machine learning and recurrent neural network (RNN) approaches. The proposed framework utilizes multi-task learning to classify active appliances and predict their energy consumption simultaneously, reducing computational overhead and enhancing system adaptability. Experiments on publicly available datasets REDD, UK-DALE, demonstrate the TCN model's superior performance, achieving higher classification accuracy, improved load prediction fidelity, and robustness under noisy conditions. The lightweight and scalable architecture ensures suitability for real-world deployment, including smart grid systems and residential monitoring. 

Ključne riječi

non-intrusive load monitoring (NILM); temporal convolutional networks (TCNs); energy disaggregation; appliance classification; load prediction; smart energy systems

Hrčak ID:

335594

URI

https://hrcak.srce.hr/335594

Datum izdavanja:

16.5.2025.

Posjeta: 574 *