Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review-Reference-Cited by-同舟云学术

Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

Published:2023-06-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
language:
Short-container-title:

Author:

Azad Mohammad Irani¹,Rajabi Roozbeh¹,Estebsari Abouzar²

Affiliation:

1. Qom University of Technology,Faculty of ECE,Qom,Iran

2. School of the Built Environment and Architecture, London South Bank University,London,United Kingdom

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10194576/10194600/10194770.pdf?arnumber=10194770

Reference37 articles.

1. An Efficient Approach to Short-Term Load Forecasting at the Distribution Level

2. Approximate inference in additive factorial hmms with application to energy disaggregation;kolter;Artificial Intelligence and Statistics,0

3. AMPds: A public dataset for load disaggregation and eco-feedback research

4. Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network;zheng;2017 51st Annual Conference on Information Sciences and Systems (CISS) CISS,2017

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on non-intrusive load decomposition model based on parallel multi-scale attention mechanism and its application in smart grid;Energy and Buildings;2024-06

2. Transient dataset of household appliances with Intensive switching events;Scientific Data;2024-05-14

3. Perfednilm: a practical personalized federated learning-based non-intrusive load monitoring;Industrial Artificial Intelligence;2024-04-16

4. Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling;Electronics;2024-01-18