scholarly journals Nonintrusive Load Monitoring Based on Deep Learning

2018 ◽  
pp. 137-147 ◽  
Author(s):  
Ke Wang ◽  
Haiwang Zhong ◽  
Nanpeng Yu ◽  
Qing Xia
Keyword(s):  
Deep Learning ◽  
Load Monitoring

CompactNet: A Light-Weight Deep Learning Framework for Smart Intrusive Load Monitoring

2021 ◽  
pp. 1-1
Author(s):  
Minh H. Phan ◽  
Queen Nguyen ◽  
Son L. Phung ◽  
Wei Emma Zhang ◽  
Trung D. Vo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Weight ◽  
Learning Framework ◽  
Load Monitoring

Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring

2020 ◽  
pp. 0-0
Author(s):  
Halil Cimen ◽  
Emilio Jose Palacios-Garcia ◽  
Morten Kolbaek ◽  
Nurettin Cetinkaya ◽  
Juan C. Vasquez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Smart Building ◽  
Load Monitoring

scholarly journals Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Jihyun Kim ◽  
Thi-Thu-Huong Le ◽  
Howon Kim
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Distribution System ◽  
Short Term Memory ◽  
Electrical Power ◽  
Electricity Consumption ◽  
Classification Performance ◽  
Robust Solution ◽  
Proposed Model ◽  
Load Monitoring

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

A Practical Solution for Non-Intrusive Type II Load Monitoring Based on Deep Learning and Post-Processing

2020 ◽  
Vol 11 (1) ◽  
pp. 148-160 ◽  
Author(s):  
Weicong Kong ◽  
Zhao Yang Dong ◽  
Bo Wang ◽  
Junhua Zhao ◽  
Jie Huang
Keyword(s):  
Deep Learning ◽  
Type Ii ◽  
Post Processing ◽  
Practical Solution ◽  
Load Monitoring

Generalization Capacity Analysis of Non- Intrusive Load Monitoring using Deep Learning

2020 ◽  
Author(s):  
Halil Cimen ◽  
Emilio J. Palacios-Garcia ◽  
Nurettin Cetinkaya ◽  
Morten Kolbak ◽  
Giuseppe Sciume ◽  
...  
Keyword(s):  
Deep Learning ◽  
Capacity Analysis ◽  
Load Monitoring

scholarly journals Corrigendum to “Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature”

2018 ◽  
Vol 2018 ◽  
pp. 1-1
Author(s):  
Jihyun Kim ◽  
Thi-Thu-Huong Le ◽  
Howon Kim
Keyword(s):  
Deep Learning ◽  
Load Monitoring

A Dual-input Multi-label Classification Approach for Non-Intrusive Load Monitoring via Deep Learning

2020 ◽  
Author(s):  
Halil Cimen ◽  
Emilio J. Palacios-Garcia ◽  
Nurettin Cetinkaya ◽  
Juan C. Vasquez ◽  
Josep M. Guerrero
Keyword(s):  
Deep Learning ◽  
Classification Approach ◽  
Load Monitoring

Non-intrusive Load Monitoring Using Inception Structure Deep Learning

2020 ◽  
Author(s):  
Yingjie Tian ◽  
Haojing Wang ◽  
An Li ◽  
Shanshan Shi ◽  
Jihai Wu
Keyword(s):  
Deep Learning ◽  
Load Monitoring
Sign in / Sign up

Export Citation Format

Share Document