Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

• URL: http://arxiv.org/abs/2306.05017v1
• Date: Thu, 8 Jun 2023 08:11:21 GMT
• Title: Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review
• Authors: Mohammad Irani Azad, Roozbeh Rajabi, Abouzar Estebsari
• Abstract summary: Non-intrusive load monitoring (NILM) is a method for decomposing the total energy consumption profile into individual appliance load profiles. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics.

Related papers

• Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters [99.90150979732641]
  We propose an innovative scheme based on adversarial attack in this paper. The scheme effectively prevents NILM models from violating appliance-level privacy, while also ensuring accurate billing calculation for users. Our solutions exhibit transferability, making the generated perturbation signal from one target model applicable to other diverse NILM models.
  arXiv  Detail & Related papers  (2024-12-22T07:06:46Z)
• Benchmarking Active Learning for NILM [2.896640219222859]
  Non-intrusive load monitoring (NILM) focuses on disaggregating total household power consumption into appliance-specific usage. Many advanced NILM methods are based on neural networks that typically require substantial amounts of labeled appliance data. We propose an active learning approach to selectively install appliance monitors in a limited number of houses.
  arXiv  Detail & Related papers  (2024-11-24T12:22:59Z)
• MoE-LLaVA: Mixture of Experts for Large Vision-Language Models [49.32669226551026]
  We propose a simple yet effective training strategy MoE-Tuning for LVLMs. MoE-LLaVA, a MoE-based sparse LVLM architecture, uniquely activates only the top-k experts through routers. Experiments show the significant performance of MoE-LLaVA in a variety of visual understanding and object hallucination benchmarks.
  arXiv  Detail & Related papers  (2024-01-29T08:13:40Z)
• Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach [58.911515417156174]
  We propose a new definition of Age of Information (AoI) and, based on the redefined AoI, we formulate an online AoI problem for MEC systems. We introduce Post-Decision States (PDSs) to exploit the partial knowledge of the system's dynamics. We also combine PDSs with deep RL to further improve the algorithm's applicability, scalability, and robustness.
  arXiv  Detail & Related papers  (2023-12-01T01:30:49Z)
• Evolutionary Deep Nets for Non-Intrusive Load Monitoring [5.415995239349699]
  Non-Intrusive Load Monitoring (NILM) is an energy efficiency technique to track electricity consumption of an individual appliance in a household by one aggregated single. Deep learning approaches are implemented to operate the desegregations.
  arXiv  Detail & Related papers  (2023-03-06T22:47:40Z)
• Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning [49.05174527668836]
  Imitation learning (IL) can generate computationally efficient sensorimotor policies from demonstrations provided by computationally expensive model-based sensing and control algorithms. In this work, we combine IL with an output feedback robust tube model predictive controller to co-generate demonstrations and a data augmentation strategy to efficiently learn neural network-based sensorimotor policies. We numerically demonstrate that our method can learn a robust visuomotor policy from a single demonstration--a two-orders of magnitude improvement in demonstration efficiency compared to existing IL methods.
  arXiv  Detail & Related papers  (2022-10-18T19:59:17Z)
• Learning Task-Aware Energy Disaggregation: a Federated Approach [1.52292571922932]
  Non-intrusive load monitoring (NILM) aims to find individual devices' power consumption profiles based on aggregated meter measurements. Yet collecting such residential load datasets require both huge efforts and customers' approval on sharing metering data. We propose a decentralized and task-adaptive learning scheme for NILM tasks, where nested meta learning and federated learning steps are designed for learning task-specific models collectively.
  arXiv  Detail & Related papers  (2022-04-14T05:53:41Z)
• FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning [8.230120882304723]
  This paper develops a distributed and privacy-preserving federated deep learning framework for NILM (FederatedNILM) FederatedNILM combines federated learning with a state-of-the-art deep learning architecture to conduct NILM for the classification of typical states of household appliances.
  arXiv  Detail & Related papers  (2021-08-08T08:56:40Z)
• Adversarial Energy Disaggregation for Non-intrusive Load Monitoring [78.47901044638525]
  Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task.
  arXiv  Detail & Related papers  (2021-08-02T03:56:35Z)
• Reinforcement Learning for Minimizing Age of Information in Real-time Internet of Things Systems with Realistic Physical Dynamics [158.67956699843168]
  This paper studies the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices. A distributed reinforcement learning approach is proposed to optimize the sampling policy. Simulations with real data of PM 2.5 pollution show that the proposed algorithm can reduce the sum of AoI by up to 17.8% and 33.9%.
  arXiv  Detail & Related papers  (2021-04-04T03:17:26Z)
• A Comprehensive Review on the NILM Algorithms for Energy Disaggregation [0.0]
  Non-intrusive load monitoring (NILM) or energy disaggregation is aimed at separating the household energy measured at the aggregate level into constituent appliances. This paper provides a survey of the effective NILM system frameworks and reviews the performance of the benchmark algorithms.
  arXiv  Detail & Related papers  (2021-02-20T23:53:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.