On the public citation graph

Transferable Tree-Based Ensemble Model for Non-Intrusive Load Monitoring

https://doi.org/10.1109/tsusc.2022.3175941
0 signed · 51 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.apenergy.2017.08.203openalex-snapshot
Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models
inferredcites →https://doi.org/10.1038/s41597-019-0212-5openalex-snapshot
The ENERTALK dataset, 15 Hz electricity consumption data from 22 houses in Korea
inferredcites →https://doi.org/10.1038/sdata.2015.7openalex-snapshot
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
inferredcites →https://doi.org/10.1109/bigdata47090.2019.9006028openalex-snapshot
Adapted Tree Boosting for Transfer Learning
inferredcites →https://doi.org/10.1109/dasc/picom/datacom/cyberscitec.2018.00-19openalex-snapshot
Edge-based Energy Management for Smart Homes
inferredcites →https://doi.org/10.1109/globalsip.2015.7418187openalex-snapshot
Dataport and NILMTK: A building data set designed for non-intrusive load monitoring
inferredcites →https://doi.org/10.1109/icassp.2016.7472104openalex-snapshot
A novel DNN-HMM-based approach for extracting single loads from aggregate power signals
inferredcites →https://doi.org/10.1109/icassp40776.2020.9054088openalex-snapshot
Interpretable Machine Learning In Sustainable Edge Computing: A Case Study of Short-Term Photovoltaic Power Output Prediction
inferredcites →https://doi.org/10.1109/icpr.2016.7899859openalex-snapshot
Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning
inferredcites →https://doi.org/10.1109/smartgridcomm.2015.7436411openalex-snapshot
A fully unsupervised non-intrusive load monitoring framework
inferredcites →https://doi.org/10.1109/smartgridcomm47815.2020.9302933openalex-snapshot
Generative Adversarial Networks and Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids
inferredcites →https://doi.org/10.1109/tcst.2015.2476777openalex-snapshot
Sparse Optimization for Automated Energy End Use Disaggregation
inferredcites →https://doi.org/10.1109/tgcn.2020.2996234openalex-snapshot
A Lightweight Short-Term Photovoltaic Power Prediction for Edge Computing
inferredcites →https://doi.org/10.1109/tii.2021.3065934openalex-snapshot
Unsupervised Domain Adaptation for Nonintrusive Load Monitoring Via Adversarial and Joint Adaptation Network
inferredcites →https://doi.org/10.1109/tim.2020.3011335openalex-snapshot
A Novel Transfer Learning-Based Intelligent Nonintrusive Load-Monitoring With Limited Measurements
inferredcites →https://doi.org/10.1109/tkde.2009.191openalex-snapshot
A Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/tsg.2015.2494592openalex-snapshot
Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring
inferredcites →https://doi.org/10.1109/tsg.2016.2631238openalex-snapshot
An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data
inferredcites →https://doi.org/10.1109/tsg.2019.2938068openalex-snapshot
Transfer Learning for Non-Intrusive Load Monitoring
inferredcites →https://doi.org/10.1109/tsg.2021.3082622openalex-snapshot
Generalizability Improvement of Deep Learning-Based Non-Intrusive Load Monitoring System Using Data Augmentation
inferredcites →https://doi.org/10.1145/2046707.2046720openalex-snapshot
Protecting consumer privacy from electric load monitoring
inferredcites →https://doi.org/10.1145/2821650.2821672openalex-snapshot
Neural NILM
inferredcites →https://doi.org/10.1145/3320075openalex-snapshot
A Sustainable and User-Behavior-Aware Cyber-Physical System for Home Energy Management
inferredcites →https://doi.org/10.1145/3427771.3427855openalex-snapshot
Explainable NILM Networks
inferredcites →https://doi.org/10.1145/3427771.3427859openalex-snapshot
UNet-NILM
inferredcites →https://doi.org/10.1609/aaai.v26i1.8162openalex-snapshot
Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types
inferredcites →https://doi.org/10.1609/aaai.v32i1.11873openalex-snapshot
Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring
inferredcites →https://doi.org/10.1609/aaai.v33i01.3301850openalex-snapshot
Deep Latent Generative Models for Energy Disaggregation
inferredcites →https://doi.org/10.3233/faia200471openalex-snapshot
Deep Learning Applied to NILM: Is Data Augmentation Worth for Energy Disaggregation?
inferredcites →https://doi.org/10.3390/data3010008openalex-snapshot
RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis
inferredcites →https://doi.org/10.3390/s121216838openalex-snapshot
Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey
inferredcites →https://doi.org/10.3390/s19040947openalex-snapshot
A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM
inferredcites →https://doi.org/10.48550/arxiv.1611.05803openalex-snapshot
COOLL: Controlled On/Off Loads Library, a Public Dataset of High-Sampled Electrical Signals for Appliance Identification
inferredcites →https://doi.org/10.48550/arxiv.1905.04610openalex-snapshot
Explainable AI for Trees: From Local Explanations to Global Understanding

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4604190openalex-extracted
Sequence-to-Point Learning Based on Spatio-Temporal Attention Fusion Network for Non-Intrusive Load Monitoring
inferred← citeshttps://doi.org/10.2139/ssrn.4743904openalex-extracted
Sequence-to-Point Learning Based on Spatio-Temporal Attention Fusion Network for Non-Intrusive Load Monitoring
inferred← citeshttps://doi.org/10.1007/978-3-031-60347-1_26openalex-snapshot
High-Frequency Non-intrusive Load Monitoring System Based on KNN and QDA Ensemble Learning Algorithm
inferred← citeshttps://doi.org/10.1007/s10489-025-06921-4openalex-snapshot
A comprehensive review of machine learning and deep learning models for non-intrusive load monitoring: performance, analyses, practical insights, and emerging trends
inferred← citeshttps://doi.org/10.1007/s40747-025-01803-1openalex-snapshot
Sequence-to-point learning based on spatio-temporal attention fusion network for non-intrusive load monitoring
inferred← citeshttps://doi.org/10.1016/j.apenergy.2025.126462openalex-snapshot
Review of advances in scaling non-intrusive load monitoring for real-world applications
inferred← citeshttps://doi.org/10.1038/s41598-023-33131-0openalex-snapshot
On enabling collaborative non-intrusive load monitoring for sustainable smart cities
inferred← citeshttps://doi.org/10.1109/tii.2023.3280445openalex-snapshot
Self-Supervised Feature Learning for Appliance Recognition in Nonintrusive Load Monitoring
inferred← citeshttps://doi.org/10.1109/tim.2024.3406779openalex-snapshot
Synthetic-to-Real Domain Adaptation for Nonintrusive Load Monitoring via Reconstruction-Based Transfer Learning
inferred← citeshttps://doi.org/10.1109/tim.2024.3413200openalex-snapshot
A Nonintrusive Load Identification Method Based on Dual-Branch Attention GRU Fusion Network
inferred← citeshttps://doi.org/10.1109/tsusc.2024.3370837openalex-snapshot
A Robust and Privacy-Aware Federated Learning Framework for Non-Intrusive Load Monitoring
inferred← citeshttps://doi.org/10.1145/3580305.3599910openalex-snapshot
Taming the Domain Shift in Multi-source Learning for Energy Disaggregation
inferred← citeshttps://doi.org/10.1145/3671127.3699532openalex-snapshot
Investigating Domain Bias in NILM
inferred← citeshttps://doi.org/10.1145/3711896.3737251openalex-snapshot
NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity
inferred← citeshttps://doi.org/10.1145/3727200.3727204openalex-snapshot
Advanced Filtering of Unknown Devices in Event-Based NILM
inferred← citeshttps://doi.org/10.32604/cmc.2024.051289openalex-snapshot
A Review of NILM Applications with Machine Learning Approaches
inferred← citeshttps://doi.org/10.3390/app13126937openalex-snapshot
Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.