NILM for Commercial Buildings: Deep Neural Networks Tackling Nonlinear and Multi-Phase Loads

M. J. S. Kulathilaka, S. Saravanan, H. D. H. P. Kumarasiri, V. Logeeshan (), S. Kumarawadu and Chathura Wanigasekara ()
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M. J. S. Kulathilaka: Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka
S. Saravanan: Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka
H. D. H. P. Kumarasiri: Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka
V. Logeeshan: Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka
S. Kumarawadu: Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka
Chathura Wanigasekara: Institute for the Protection of Maritime Infrastructure, German Aerospace Centre (DLR), 27572 Bremerhaven, Germany

Energies, 2024, vol. 17, issue 15, 1-21

Abstract: As energy demand and electricity costs continue to rise, consumers are increasingly adopting energy-efficient practices and appliances, underscoring the need for detailed metering options like appliance-level load monitoring. Non-intrusive load monitoring (NILM) is particularly favored for its minimal hardware requirements and enhanced customer experience, especially in residential settings. However, commercial power systems present significant challenges due to greater load diversity and imbalance. To address these challenges, we introduce a novel neural network architecture that combines sequence-to-sequence, WaveNet, and ensembling techniques to identify and classify single-phase and three-phase loads using appliance power signatures in commercial power systems. Our approach, validated over four months, achieved an overall accuracy exceeding 93% for nine devices, including six single-phase and four three-phase loads. The study also highlights the importance of incorporating nonlinear loads, such as two different inverter-type air conditioners, within NILM frameworks to ensure accurate energy monitoring. Additionally, we developed a web-based NILM energy dashboard application that enables users to monitor and evaluate load performance, recognize usage patterns, and receive real-time alerts for potential faults. Our findings demonstrate the significant potential of our approach to enhance energy management and conservation efforts in commercial buildings with diverse and complex load profiles, contributing to more efficient energy use and addressing climate change challenges.

Keywords: non-intrusive load monitoring; deep neural network; WaveNet; ensemble learning; nonlinear loads (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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