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A comparative study of machine learning classifiers for electric load disaggregation based on an extended nilm dataset
(2021-08-15)
The appliance evaluation and the power consumption consciousness are becoming essential for improving demand management and power grid enhancement. Load disaggregation becomes a promising engine for this goal, and some ...
Load disaggregation using microscopic power features and pattern recognition
(2019-01-01)
A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may ...
Selection of features from power theories to compose NILM datasets
(2022-04-01)
The load disaggregation concept is gaining attention due to the increasing need for optimized energy utilization and detailed characterization of electricity consumption profiles, especially through Nonintrusive Load ...
NILM-based approach for energy efficiency assessment of household appliances
(2020-12-01)
This paper presents a novel Non-Intrusive Load Monitoring (NILM) approach focusing on the Energy Efficiency (EE) assessment of residential appliances. This approach (NILMEE) is able to identify the individual consumption ...
Electrical load forecasting in disaggregated levels using Fuzzy ARTMAP artificial neural network and noise removal by singular spectrum analysis
(2020-07-01)
Electrical load forecasting in disaggregated levels is a difficult task due to time series randomness, which leads to noise and consequently affects the quality of predictions. To mitigate this problem, noise removal using ...
NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives
(IEEE, 2023)
Due to its impact on household energy use and the adoption of renewable energies, the intelligent management of the power consumption of electric vehicles (EVs) is of great relevance. In the context of widespread clean ...
Feature extraction for nonintrusive load monitoring based on S-Transform
(2014-05-01)
The electric energy demand is dramatically growing worldwide and demand reduction emerges as an outstanding strategy; it implies detailed information about the electricity consumption, namely load disaggregation. Typical ...
Spatial disaggregation of traffic emission inventories in large cities using simplified top-down methods
(2009)
Simple, inexpensive and accurate methods for assessing the spatial distribution of traffic emissions are badly needed for the environmental management in South American cities. In this study, various spatial disaggregation ...
End-to-end NILM system using high frequency data and neural networks.
(arXiv, 2020)
Improving energy efficiency is a necessity in the fight against climate change. Non Intrusive Load Monitoring (NILM) systems give important information about the household consumption that can be used by the electric utility ...