A review of distribution network applications based on smart meter data analytics
CL Athanasiadis, TA Papadopoulos… - … and Sustainable Energy …, 2024 - Elsevier
The large-scale roll-out of smart meters allows the collection of a vast amount of fine-grained
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …
Multi-agent Systems for Resource Allocation and Scheduling in a smart grid
SS Binyamin, S Ben Slama - Sensors, 2022 - mdpi.com
Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil
engineering professionals to subdivide complex issues. Based on the assignment's history …
engineering professionals to subdivide complex issues. Based on the assignment's history …
[HTML][HTML] Leveraging sequence-to-sequence learning for online non-intrusive load monitoring in edge device
Non-intrusive load monitoring (NILM), extracting the appliances' usage profiles by
decomposing a household's aggregate electricity consumption, has become increasingly …
decomposing a household's aggregate electricity consumption, has become increasingly …
An ensemble-policy non-intrusive load monitoring technique based entirely on deep feature-guided attention mechanism
Z Nie, Y Yang, Q Xu - Energy and Buildings, 2022 - Elsevier
Non-intrusive load monitoring (NILM), as an important part of intelligent electricity
consumption, improves the cognitive level of the load by analyzing the bus power in a …
consumption, improves the cognitive level of the load by analyzing the bus power in a …
A non-intrusive load monitoring method based on feature fusion and SE-ResNet
T Chen, H Qin, X Li, W Wan, W Yan - Electronics, 2023 - mdpi.com
In the study of non-intrusive load monitoring, using a single feature for identification can lead
to insignificant differentiation of similar loads; however, multi-feature fusion can pool the …
to insignificant differentiation of similar loads; however, multi-feature fusion can pool the …
Neural Fourier energy disaggregation
Deploying energy disaggregation models in the real-world is a challenging task. These
models are usually deep neural networks and can be costly when running on a server or …
models are usually deep neural networks and can be costly when running on a server or …
Optimal management for EV charging stations: A win–win strategy for different stakeholders using constrained Deep Q-learning
A Paraskevas, D Aletras, A Chrysopoulos… - Energies, 2022 - mdpi.com
Given the additional awareness of the increasing energy demand and gas emissions'
effects, the decarbonization of the transportation sector is of great significance. In particular …
effects, the decarbonization of the transportation sector is of great significance. In particular …
An efficient hybrid model for appliances classification based on time series features
M Aslan, EN Zurel - Energy and Buildings, 2022 - Elsevier
Today, depending on the increasing population and technological developments,
household appliances have significantly increased. This situation causes an ever-increasing …
household appliances have significantly increased. This situation causes an ever-increasing …
Torch-nilm: An effective deep learning toolkit for non-intrusive load monitoring in pytorch
Non-intrusive load monitoring is a blind source separation task that has been attracting
significant interest from researchers working in the field of energy informatics. However …
significant interest from researchers working in the field of energy informatics. However …
Expanding variety of non-intrusive load monitoring training data: Introducing and benchmarking a novel data augmentation technique
Energy consumption monitoring is an important asset for demand side management
systems. Although smart meters provide high-sample-rated and accurate measurements of …
systems. Although smart meters provide high-sample-rated and accurate measurements of …