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Deep Learning Based Non-Intrusive Load Monitoring for Smart Energy Management in Households

Authors:
Parvaneh Zavareh
Warunee Soythong
Basirah Noor
Volker Hoffmann
Huamin Ren

Keywords: Non-Intrusive Load Monitoring (NILM); Convolutional Neural Networks; Seq2Seq; Seq2Point; Smart Home; Energy Management.

Abstract:
The increasing digitalization inside the energy sector is transforming the fact that how residential electricity consumption is analysed and optimized. This study aims to propose and evaluate a Deep learning-based model for Non-Intrusive Load Monitoring (NILM), also called energy disaggregation, designed for the real-world applications in smart homes energy management systems. The framework employs Convolutional Neural Network (CNN) based sequence-to-point (Seq2Point) and sequence-to-sequence (Seq2Seq) model to isolate appliance level consumption from the aggregate power, while addressing generalization challenges across diverse households and appliances type. The results suggest that model choice (Seq2Point vs Seq2Seq)effects the results, for example Seq2Point shows better results in detecting high power, short duration events, highlighting the importance of selecting architecture based on appliance-specific characteristics for optimal NILM performance. The practical implications suggest that the proposed study can enable realtime appliance monitoring, adaptive demand-side management, and sustainable energy optimization in future smart grid environments.

Pages: 25 to 31

Copyright: Copyright (c) IARIA, 2026

Publication date: March 8, 2026

Published in: conference

ISSN: 2308-412X

ISBN: 978-1-68558-357-6

Location: Valencia, Spain

Dates: from March 8, 2026 to March 12, 2026