A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

• URL: http://arxiv.org/abs/2102.12578v1
• Date: Sat, 20 Feb 2021 23:53:57 GMT
• Title: A Comprehensive Review on the NILM Algorithms for Energy Disaggregation
• Authors: Akriti Verma, Adnan Anwar
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The housing structures have changed with urbanization and the growth due to the construction of high-rise buildings all around the world requires end-use appliance energy conservation and management in real-time. This shift also came along with smart-meters which enabled the estimation of appliance-specific power consumption from the buildings aggregate power consumption reading. Non-intrusive load monitoring (NILM) or energy disaggregation is aimed at separating the household energy measured at the aggregate level into constituent appliances. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Incredible research and publications have been conducted on energy disaggregation, non-intrusive load monitoring, home energy management and appliance classification. There exists an API, NILMTK, a reproducible benchmark algorithm for the same. Many other approaches to perform energy disaggregation has been adapted such as deep neural network architectures and big data approach for household energy disaggregation. This paper provides a survey of the effective NILM system frameworks and reviews the performance of the benchmark algorithms in a comprehensive manner. This paper also summarizes the wide application scope and the effectiveness of the algorithmic performance on three publicly available data sets.

Related papers

• State and Action Factorization in Power Grids [47.65236082304256]
  We propose a domain-agnostic algorithm that estimates correlations between state and action components entirely based on data. The algorithm is validated on a power grid benchmark obtained with the Grid2Op simulator.
  arXiv  Detail & Related papers  (2024-09-03T15:00:58Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances [0.16777183511743468]
  Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point. We introduce a novel DL model aimed at enhanced multi-label classification of NILM with improved computation and energy efficiency. Compared to the state-of-the-art, the proposed model has its energy consumption reduced by more than 23%.
  arXiv  Detail & Related papers  (2023-07-18T13:23:23Z)
• Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review [0.0]
  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.
  arXiv  Detail & Related papers  (2023-06-08T08:11:21Z)
• 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)
• A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks [0.0]
  Energy disaggregation or nonintrusive load monitoring (NILM), is a single-input blind source discrimination problem. This article presents a new approach for power disaggregation by using a deep recurrent long short term memory (LSTM) network combined with convolutional neural networks (CNN)
  arXiv  Detail & Related papers  (2021-04-15T22:34:20Z)
• Energy Disaggregation using Variational Autoencoders [11.940343835617046]
  Non-intrusive load monitoring (NILM) is a technique that uses a single sensor to measure the total power consumption of a building. Recent disaggregation algorithms have significantly improved the performance of NILM systems. We propose an energy disaggregation approach based on the variational autoencoders (VAE) framework.
  arXiv  Detail & Related papers  (2021-03-22T20:53:36Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications [77.00418462388525]
  This paper presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection.
  arXiv  Detail & Related papers  (2020-11-26T00:32:07Z)
• Energy Disaggregation with Semi-supervised Sparse Coding [0.0]
  Energy disaggregation research aims to decompose the aggregated energy consumption data into its component appliances. In this paper, a discriminative disaggregation model based on sparse coding has been evaluated on large-scale household power usage dataset for energy conservation.
  arXiv  Detail & Related papers  (2020-04-20T21:05:25Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

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.