109 research outputs found
Conv-NILM-Net, a causal and multi-appliance model for energy source separation
Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating
individual appliance power usage from a single aggregate measurement. Deep
neural networks have become increasingly popular in attempting to solve NILM
problems. However most used models are used for Load Identification rather than
online Source Separation. Among source separation models, most use a
single-task learning approach in which a neural network is trained exclusively
for each appliance. This strategy is computationally expensive and ignores the
fact that multiple appliances can be active simultaneously and dependencies
between them. The rest of models are not causal, which is important for
real-time application. Inspired by Convtas-Net, a model for speech separation,
we propose Conv-NILM-net, a fully convolutional framework for end-to-end NILM.
Conv-NILM-net is a causal model for multi appliance source separation. Our
model is tested on two real datasets REDD and UK-DALE and clearly outperforms
the state of the art while keeping a significantly smaller size than the
competing models.Comment: Published in ECMLPKDD 2022, MLBEM worksho
A Scoping Review of Energy Load Disaggregation
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
ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series
Over the past decade, millions of smart meters have been installed by
electricity suppliers worldwide, allowing them to collect a large amount of
electricity consumption data, albeit sampled at a low frequency (one point
every 30min). One of the important challenges these suppliers face is how to
utilize these data to detect the presence/absence of different appliances in
the customers' households. This valuable information can help them provide
personalized offers and recommendations to help customers towards the energy
transition. Appliance detection can be cast as a time series classification
problem. However, the large amount of data combined with the long and variable
length of the consumption series pose challenges when training a classifier. In
this paper, we propose ADF, a framework that uses subsequences of a client
consumption series to detect the presence/absence of appliances. We also
introduce TransApp, a Transformer-based time series classifier that is first
pretrained in a self-supervised way to enhance its performance on appliance
detection tasks. We test our approach on two real datasets, including a
publicly available one. The experimental results with two large real datasets
show that the proposed approach outperforms current solutions, including
state-of-the-art time series classifiers applied to appliance detection. This
paper appeared in VLDB 2024.Comment: 10 pages, 7 figures. This paper appeared in VLDB 202
Deep learning applications in non-intrusive load monitoring
Non-Intrusive Load Monitoring (NILM) is a technique for inferring the power consumption of each appliance within a home from one central meter, aiding in energy conservation. In this thesis I present several Deep Learning solutions for NILM, starting with two preliminary works – A proof of concept project for multisensory NILM on a Raspberry Pi; and a fully developed NILM solution named WaveNILM. Despite their success, both methods struggled to generalize outside their training data, a common problem in NILM. To improve generalization, I designed a framework for synthesizing truly novel appliance level power signatures based on generative adversarial networks (GAN) – the main project of this thesis. This generator, named PowerGAN, is trained using a variety of GAN techniques. I present a comparison of PowerGAN to other data synthesis work in the context of NILM and demonstrate that PowerGAN is able to create truly synthetic, realistic, diverse, appliance power signatures
Data-driven analysis on the subbase strain prediction:a deep data augmentation-based study
The service quality of the subbase may affect the overall road performance during its service life. Thus, monitoring and prediction of subbase strain development are of great importance for civil engineers. In this paper, a method based on the time-series augmentation was employed to predict the subbase strain development. The time-series generative adversarial network (TimeGAN) model was implemented to perform the augmentation of time-series data based on the original monitored data. The augmented data was trained through deep learning network to learn the feature correlation of the subbase strain. The effectiveness of TimeGAN on the prediction accuracy was evaluated through the Attention-Sequence to Sequence (Attention-Seq2seq) model, and temporal convolution network-adaptively parametric rectifier linear units (TCN-APReLU) model. Results indicated that the TimeGAN network could capture sufficient information from the time-series monitored data of subbase strain development so that the corresponding augmented data matches well with the original data, which improves the prediction accuracy. It is also discovered that the combination of TimeGAN and TCN-APReLU appropriately predict the subbase strain development based on the original monitored data
Situation Awareness for Smart Distribution Systems
In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas
Residential Energy Management for Renewable Energy Systems Incorporating Data-Driven Unravelling of User Behavior
The penetration of distributed energy resources (DERs) such as photovoltaic (PV) at the residential
level has increased rapidly over the past year. It will inevitably induce a paradigm shift in end-user
and operations of local energy markets. The energy community with high integration of DERs
initiative allows its users to manage their generation (for prosumers) and consumption more
efficiently, resulting in various economic, social, and environmental benefits. Specifically, the local
energy communities and their members can legally engage in energy generation, distribution, supply,
consumption, storage, and sharing to increase levels of autonomy from the power grid, advance
energy efficiency, reduce energy costs, and decrease carbon emissions. Reducing energy
consumption costs is difficult for residential energy management without understanding the users'
preferences. The advanced measurement and communication technologies provide opportunities for
individual consumers/prosumers and local energy communities to adopt a more active role in
renewable-rich smart grids. Non-intrusive load monitoring (NILM) monitors the load activities from a
single point source, such as a smart meter, based on the assumption that different appliances have
different power consumption levels and features. NILM can extract the users' load consumption from
the smart meter to support the development of the smart grid for better energy management and
demand response (DR). Yet to date, how to design residential energy management, including home
energy management systems (HEMS) and community energy management systems (CEMS), with
an understanding of user preferences and willingness to participate in energy management, is still far
from being fully investigated. This thesis aims to develop methodologies for a resident energy
management system for renewable energy systems (RES) incorporating data-driven unravelling of
the user's energy consumption behaviour
Modelling of Electrical Appliance Signatures for Energy Disaggregation
The rapid development of technology in the electrical sector within the last 20 years has
led to growing electric power needs through the increased number of electrical appliances
and automation of tasks. In contrary, reduction of the overall energy consumption
as well as efficient energy management are needed, in order to reduce global warming
and meet the global climate protection goals. These requirements have led to the recent
adoption of smart-meters and smart-grids, as well as to the rise of Non-Intrusive Load
Monitoring.
Non-Intrusive Load Monitoring aims to extract the energy consumption of individual
electrical appliances through disaggregation of the total power consumption as
measured by a single smart meter at the inlet of a household. Therefore, Non-Intrusive
Load Monitoring is a highly under-determined problem which aims to estimate multiple
variables from a single observation, thus is impossible to be solved analytical. In
order to find accurate estimates of the unknown variables three fundamentally different
approaches, namely deep-learning, pattern matching and single-channel source separation,
have been investigated in the literature in order to solve the Non-Intrusive Load
Monitoring problem.
While Non-Intrusive Load Monitoring has multiple areas of application, including
energy reduction through consumer awareness, load scheduling for energy cost optimization
or reduction of peak demands, the focus of this thesis is especially on the performance
of the disaggregation algorithm, the key part of the Non-Intrusive Load Monitoring
architecture. In detail, optimizations are proposed for all three architectures, while
the focus lies on deep-learning based approaches. Furthermore, the transferability capability
of the deep-learning based approach is investigated and a NILM specific transfer
architecture is proposed. The main contribution of the thesis is threefold.
First, with Non-Intrusive Load Monitoring being a time-series problem incorporation
of temporal information is crucial for accurate modelling of the appliance signatures
and the change of signatures over time. Therefore, previously published architectures
based on deep-learning have focused on utilizing regression models which intrinsically
incorporating temporal information. In this work, the idea of incorporating temporal information
is extended especially through modelling temporal patterns of appliances not
only in the regression stage, but also in the input feature vector, i.e. by using fractional
calculus, feature concatenation or high-frequency double Fourier integral signatures. Additionally,
multi variance matching is utilized for Non-Intrusive Load Monitoring in order
to have additional degrees of freedom for a pattern matching based solution.
Second, with Non-Intrusive Load Monitoring systems expected to operate in realtime
as well as being low-cost applications, computational complexity as well as storage
limitations must be considered. Therefore, in this thesis an approximation for frequency
domain features is presented in order to account for a reduction in computational complexity.
Furthermore, investigations of reduced sampling frequencies and their impact on
disaggregation performance has been evaluated. Additionally, different elastic matching
techniques have been compared in order to account for reduction of training times and
utilization of models without trainable parameters.
Third, in order to fully utilize Non-Intrusive Load Monitoring techniques accurate
transfer models, i.e. models which are trained on one data domain and tested on a different
data domain, are needed. In this context it is crucial to transfer time-variant and
manufacturer dependent appliance signatures to manufacturer invariant signatures, in
order to assure accurate transfer modelling. Therefore, a transfer learning architecture
specifically adapted to the needs of Non-Intrusive Load Monitoring is presented.
Overall, this thesis contributes to the topic of Non-Intrusive Load Monitoring improving
the performance of the disaggregation stage while comparing three fundamentally
different approaches for the disaggregation problem
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