Enhancing neural non-intrusive load monitoring with generative adversarial networks

Kaibin Bao; Kanan Ibrahimov; Martin Wagner; Hartmut Schmeck

doi:10.1186/s42162-018-0038-y

ScienceGate Book Chapters

JOURNAL ARTICLE

Enhancing neural non-intrusive load monitoring with generative adversarial networks

Kaibin Bao Kanan Ibrahimov Martin Wagner Hartmut Schmeck

Year: 2018 Journal: Energy Informatics Vol: 1 (S1) Publisher: Springer Nature

DOI: 10.1186/s42162-018-0038-y

Get Full-Text PDF Get Analytical Report

Keywords:

Generative grammar Artificial neural network Computer science Adversarial system Confusion Artificial intelligence Machine learning Confusion matrix Energy (signal processing) Sequence (biology) Mathematics Psychology

Metrics

Cited By

2.33

FWCI (Field Weighted Citation Impact)

Refs

0.90

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Smart Grid Energy Management

Physical Sciences → Engineering → Electrical and Electronic Engineering

Smart Grid Security and Resilience

Physical Sciences → Engineering → Control and Systems Engineering

Energy Load and Power Forecasting

Physical Sciences → Engineering → Electrical and Electronic Engineering

Enhancing neural non-intrusive load monitoring with generative adversarial networks

Metrics

Citation History

Topics

Related Documents

Pre-Trained Models and Generative Adversarial Networks for Non-Intrusive Load Monitoring

Unknown Appliances Detection for Non-Intrusive Load Monitoring Based on Conditional Generative Adversarial Networks

Multi-Length Dynamic Shapelets Approach for Non-Intrusive Load Monitoring via Generative Adversarial Networks

Generative Adversarial Networks and Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids

Non-intrusive Load Monitoring Based on Deep Convolutional Generative Adversarial Network Prediction