A Scoping Review of Energy Load Disaggregation
- URL: http://arxiv.org/abs/2402.01654v1
- Date: Wed, 10 Jan 2024 09:59:12 GMT
- Title: A Scoping Review of Energy Load Disaggregation
- Authors: Bal\'azs Andr\'as Tolnai and Zheng Ma and Bo N{\o}rregaard
J{\o}rgensen
- Abstract summary: Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management.
The field currently lacks a comprehensive overview.
Domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed.
- Score: 1.6783315930924723
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Energy load disaggregation can contribute to balancing power grids by
enhancing the effectiveness of demand-side management and promoting
electricity-saving behavior through increased consumer awareness. However, the
field currently lacks a comprehensive overview. To address this gap, this paper
con-ducts a scoping review of load disaggregation domains, data types, and
methods, by assessing 72 full-text journal articles. The findings reveal that
domestic electricity consumption is the most researched area, while others,
such as industrial load disaggregation, are rarely discussed. The majority of
research uses relatively low-frequency data, sampled between 1 and 60 seconds.
A wide variety of methods are used, and artificial neural networks are the most
common, followed by optimization strategies, Hidden Markov Models, and Graph
Signal Processing approaches.
Related papers
- Occupancy Detection Based on Electricity Consumption [0.0]
This article presents a new methodology for extracting intervals when a home is vacant from low-frequency electricity consumption data.
It shows encouraging results on both simulated and real consumption curves.
arXiv Detail & Related papers (2023-12-13T21:49:09Z) - Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning [51.02352381270177]
Split Federated Learning (SFL) has recently emerged as a promising distributed learning technology.
The choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy.
This article provides a comprehensive overview of the SFL process and thoroughly analyze energy consumption and privacy.
arXiv Detail & Related papers (2023-11-15T23:23:42Z) - Endogenous Macrodynamics in Algorithmic Recourse [52.87956177581998]
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment.
We show that many of the existing methodologies can be collectively described by a generalized framework.
We then argue that the existing framework does not account for a hidden external cost of recourse, that only reveals itself when studying the endogenous dynamics of recourse at the group level.
arXiv Detail & Related papers (2023-08-16T07:36:58Z) - 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) - Towards Sequence Utility Maximization under Utility Occupancy Measure [53.234101208024335]
In the database, although utility is a flexible criterion for each pattern, it is a more absolute criterion due to neglect of utility sharing.
We first define utility occupancy on sequence data and raise the problem of High Utility-Occupancy Sequential Pattern Mining.
An algorithm called Sequence Utility Maximization with Utility occupancy measure (SUMU) is proposed.
arXiv Detail & Related papers (2022-12-20T17:28:53Z) - Machine learning applications for electricity market agent-based models:
A systematic literature review [68.8204255655161]
Agent-based simulations are used to better understand the dynamics of the electricity market.
Agent-based models provide the opportunity to integrate machine learning and artificial intelligence.
We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
arXiv Detail & Related papers (2022-06-05T14:52:26Z) - Machine learning methods for modelling and analysis of time series
signals in geoinformatics [2.193013035690221]
This dissertation evaluates the performance of several deep learning (DL) architectures on a large number of time series datasets of different nature and for different applications.
The first problem is related to ionospheric Total Content (TEC) modeling which is an important issue in many real time Global Navigation System Satellites (GNSS) applications.
The next problem is energy disaggregation which is an important issue for energy efficiency and energy consumption awareness.
arXiv Detail & Related papers (2021-09-16T16:18:13Z) - 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 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) - Activity Detection And Modeling Using Smart Meter Data: Concept And Case
Studies [6.7336801526732755]
This paper proposes a new and more effective approach, i.e., activity disaggregation.
We develop a framework by leverage machine learning for activity detection based on residential load data and features.
We show through numerical case studies to demonstrate the effectiveness of the activity detection method.
arXiv Detail & Related papers (2020-10-26T02:36:35Z) - Weight-Sharing Neural Architecture Search: A Battle to Shrink the
Optimization Gap [90.93522795555724]
Neural architecture search (NAS) has attracted increasing attentions in both academia and industry.
Weight-sharing methods were proposed in which exponentially many architectures share weights in the same super-network.
This paper provides a literature review on NAS, in particular the weight-sharing methods.
arXiv Detail & Related papers (2020-08-04T11:57:03Z)
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.