Non-Intrusive Load Monitoring

Also known as NILM

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Overview

Non-intrusive load monitoring (NILM) uses whole-home smart-meter readings to infer which appliances are running and how much energy they use, without installing a sensor on every device. Common approaches detect appliance events or predict appliance power directly, using sequence-to-point and sequence-to-sequence neural networks, Transformers, and state-segmentation methods. Current work also explores transfer learning and promptable models for new homes, lightweight classifiers for microcontrollers, and tests on datasets such as REDD, UK-DALE, and REFIT. A central challenge is making predictions accurate across different homes, appliances, and meter sampling rates while limiting the need for appliance-level labels, computation, and sensitive household data.

Latest papers

CardsList

12 papers2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

  1. Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data

    Oct 1, 2026Achal Nanjundamurthy, Rupam Misra, Suzanne Little +1Non-Intrusive Load MonitoringBuilding Energy Management

    School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

  2. ReCo: When to Relocate Sensor Kits under Deployment Constraints -- A NILM Case Study

    Sep 28, 2026Haokun Chen, Yu Tong, Yehai ChenNon-Intrusive Load MonitoringOnline Resource Allocation

    McMaster University Hamilton, ON, Canada · Shanghai Eneintel Technology Co., Ltd. Shanghai, China

  3. Uncovering Residential PV-EV Co-Adoption from Smart-Meter Data: Load Archetypes and Detection for Demand-Side Planning

    Sep 23, 2026Jack Zheng, Hao WangNon-Intrusive Load MonitoringElectric Vehicle Charging

    Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia · Monash Energy Institute, Monash University, Melbourne, Victoria, Australia

  4. Learning Defensive Policies against Diverse Inference Attacks for Smart Meter Privacy

    Sep 21, 2026Ruichang Zhang, Mustafa A. MustafaNon-Intrusive Load MonitoringInternet of Things

    Department of Computer Science, The University of Manchester, Manchester, UK · COSIC, KU Leuven, Leuven, Belgium

  5. Ask for Any Appliance: A Prompt-Programmable Foundation Model for Non-Intrusive Load Monitoring

    Sep 19, 2026Xudong Wang, Jiacheng Cui, Junyu Xue +2Non-Intrusive Load MonitoringTime-Series Foundation Models

    The Chinese University of Hong Kong, Shenzhen · The Hong Kong University of Science and Technology (Guangzhou) · Southern University of Science and Technology

  6. Multi-Appliance Non-Intrusive Load Monitoring via Label-Preserving Aggregate Recomposition and Prediction Consistency

    Sep 16, 2026Jiangfeng Liu, Yanfang FanNon-Intrusive Load MonitoringData Augmentation

    School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China

  7. A Real-Time Tsetlin Machine-based Non-intrusive Load Monitoring System on MCUs

    Aug 19, 2026Han Wu, Tianhang Tan, Shengyu Duan +3Non-Intrusive Load MonitoringOn-Device Inference

    School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom

  8. BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series

    Aug 1, 2026Zhenya Zhang, Wendi Zhu, Ping Wang +2Non-Intrusive Load MonitoringChange-Point Detection

    Anhui Province Key Laboratory of Intelligent Building & Building Energy Saving, Anhui Jianzhu University, Hefei 230022, China · School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230022, China · School of Economics and Management, Anhui Jianzhu University, Hefei 230022, China +1

  9. SEDR-Seq2P: A Lightweight Dilated Residual Sequence-to-Point Network for Multi-Task Industrial NILM

    Jul 30, 2026Hatem Haddad, Feres Jerbi, Issam SmaaliNon-Intrusive Load MonitoringEfficient Neural Network Inference

    Wattnow Tunis, Tunisia

  10. Lightweight and Scalable Transfer Learning Framework for Load Disaggregation

    Mar 5, 2026L. E. Garcia-Marrero, G. Petrone, E. MonmassonNon-Intrusive Load MonitoringTransfer Learning

    Systèmes et Applications des Technologies de l’Information et de l’Energie laboratory, Cergy-Pontoise · Dipartimento di Ingegneria dell’Informazione ed Elettrica e Matematica Applicata/DIEM, University of Salerno, 84084 Fisciano

  11. Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning

    Aug 20, 2025Xudong Wang, Guoming Tang, Junyu Xue +3Non-Intrusive Load MonitoringMulti-Task Learning

    The Chinese University of Hong Kong, Shenzhen · The Hong Kong University of Science and Technology (Guangzhou) · Southern University of Science and Technology +1

  12. Evolutionary Deep Nets for Non-Intrusive Load Monitoring

    Mar 6, 2023Jinsong Wang, Kenneth A. LoparoNon-Intrusive Load MonitoringSparse Neural Networks