4.7 Article

DeepDFML-NILM: A New CNN-Based Architecture for Detection, Feature Extraction and Multi-Label Classification in NILM Signals

Related references

Note: Only part of the references are listed.
Article Energy & Fuels

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

Veronica Piccialli et al.

Summary: The paper proposes a deep neural network to solve NILM problem, which outperforms existing techniques in the experiments. By incorporating a specific attention mechanism, the network is able to correctly detect the states of appliances and locate sections with high power consumption.

ENERGIES (2021)

Article Energy & Fuels

Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network

Jiateng Song et al.

Summary: This paper proposes a load identification method based on deep learning, combining the advantages of LSTM and seq2point for better accuracy and generalization. Experimental results on three datasets show that this method can significantly reduce identification errors.

ENERGIES (2021)

Article Engineering, Electrical & Electronic

Sequence to point learning based on bidirectional dilated residual network for non-intrusive load monitoring

Ziyue Jia et al.

Summary: Recent studies have shown that deep learning is widely applied to NILM problem, and the introduction of Bi-TCN residual block and bidirectional dilated convolution can effectively address the training difficulties of deep neural networks. Experiments have demonstrated the superiority of BitcnNILM on low-frequency data.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

Article Engineering, Electrical & Electronic

A Novel Transfer Learning-Based Intelligent Nonintrusive Load-Monitoring With Limited Measurements

Zejian Zhou et al.

Summary: This article investigates the real-time nonintrusive load-monitoring problem with limited measurements, specifically low sampling rate data, and develops an online learning-based intelligent NILM algorithm that can infer and classify different appliances between appliances using transfer learning technique and deep neural networks.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Article Engineering, Electrical & Electronic

Transfer Learning for Non-Intrusive Load Monitoring

Michele D'Incecco et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

Article Computer Science, Artificial Intelligence

On time series representations for multi-label NILM

Christoforos Nalmpantis et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Energy & Fuels

A Dataset for Non-Intrusive Load Monitoring: Design and Implementation

Douglas Paulo Bertrand Renaux et al.

ENERGIES (2020)

Article Energy & Fuels

A Multi-Agent NILM Architecture for Event Detection and Load Classification

Andre Eugenio Lazzaretti et al.

ENERGIES (2020)

Article Engineering, Electrical & Electronic

A critical review of state-of-the-art non-intrusive load monitoring datasets

Hafiz Khurram Iqbal et al.

ELECTRIC POWER SYSTEMS RESEARCH (2020)

Article Construction & Building Technology

Energy management using non-intrusive load monitoring techniques - State-of-the-art and future research directions

R. Gopinath et al.

SUSTAINABLE CITIES AND SOCIETY (2020)

Article Automation & Control Systems

Semisupervised Multilabel Deep Learning Based Nonintrusive Load Monitoring in Smart Grids

Yandong Yang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Computer Science, Information Systems

A Load Identification Method Based on Active Deep Learning and Discrete Wavelet Transform

Luyang Guo et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

PB-NILM: Pinball Guided Deep Non-Intrusive Load Monitoring

Eduardo Gomes et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation

Christoforos Nalmpantis et al.

ARTIFICIAL INTELLIGENCE REVIEW (2019)

Article Engineering, Electrical & Electronic

Non-Intrusive Load Monitoring by Voltage-Current Trajectory Enabled Transfer Learning

Yanchi Liu et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

Article Engineering, Electrical & Electronic

Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks

Leen De Baets et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2019)

Article Engineering, Electrical & Electronic

Non-intrusive load monitoring algorithm based on features of V-I trajectory

A. Longjun Wang et al.

ELECTRIC POWER SYSTEMS RESEARCH (2018)

Article Construction & Building Technology

Appliance classification using VI trajectories and convolutional neural networks

Leen De Baets et al.

ENERGY AND BUILDINGS (2018)

Article Engineering, Electrical & Electronic

Toward Non-Intrusive Load Monitoring via Multi-Label Classification

Seyed Mostafa Tabatabaei et al.

IEEE TRANSACTIONS ON SMART GRID (2017)

Article Computer Science, Artificial Intelligence

Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems

Marisa Figueiredo et al.

NEUROCOMPUTING (2012)