Pith. sign in

REVIEW 3 major objections 1 minor 59 references

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling

T0 review · 3 major / 1 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Point cloud video action recognition via semantic-aware state space scanning

desk verdict The submission's full text is a different paper (DualNILM); the UST-SSM claims are unsupported, so this version should be returned, not peer-reviewed. read the letter →

arxiv 2508.14604 v1 pith:IKL4COMU submitted 2025-08-20 cs.CV cs.AI

Peiming Li , Ziyi Wang , Yulin Yuan , Hong Liu , Xiangming Meng , Junsong Yuan , Mengyuan Liu This is my paper · ORCID
classification cs.CVcs.AI
keywords pointcloudvideostatespacemodelsselectivescanningactionrecognitionprompt-guidedclusteringspatio-temporalmodelinglinearcomplexity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that selective state space models (SSMs), which handle long sequences at linear cost, can be made to work on point cloud videos. The obstacle is that point clouds are unordered in both space and time, so a unidirectional scan sees little structure. The paper's answer is to reorder points into semantic-aware sequences through prompt-guided clustering, then add modules that restore missing geometry and motion details and sharpen temporal links. If correct, action recognition on 4D point cloud video becomes a linear-complexity sequence modeling problem rather than a dense 3D one.

What carries the argument

Spatial-Temporal Selection Scanning (STSS) is the load-bearing component: it reorganizes unordered point cloud video frames into a 1D sequence through prompt-guided clustering, so that a selective SSM can treat the video as a sequence. STSA aggregates spatio-temporal features to compensate for missing 4D geometry and motion; TIS enhances temporal interaction using non-anchor frames and expanded receptive fields.

What would settle it

Run UST-SSM on any of the three datasets with STSS replaced by (a) frame-by-frame raw order and (b) a fixed random permutation of points; if accuracy stays about the same, the semantic ordering is not what drives the result. Also, evaluate on action classes not seen during training to check for prompt overfitting.

Watch

Extended reading notes

Core claim

The central claim is that the spatio-temporal disorder of point cloud videos—not the lack of temporal information—is what blocks SSMs from modeling them. UST-SSM removes that block with Spatial-Temporal Selection Scanning (STSS), which uses prompt-guided clustering to arrange unordered points so that similar points that are far apart in space or time become neighbors in the 1D scan. With that ordering, the SSM's unidirectional state can propagate information between those distant-but-similar points. Two supporting components, Spatio-Temporal Structure Aggregation (STSA) and Temporal Interaction Sampling (TIS), recover 4D geometric and motion detail and improve fine-grained temporal dependenc

Load-bearing premise

The method's advantage depends on prompt-guided clustering really putting spatially and temporally distant but similar points next to each other in the scan; if the ordering is no better than a fixed or random order, the SSM gains nothing.

Editorial extensions

If this is right

  • Action recognition from point cloud video can be formulated as linear-complexity sequence modeling rather than dense 3D convolution.
  • Semantic reordering of points can make a unidirectional state machine reach spatially and temporally distant but similar points.
  • Compensating missing 4D details through aggregation improves recognition when geometry or motion is sparse.
  • Sampling non-anchor frames strengthens fine-grained temporal dependencies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the prompts used for clustering are learned from action categories, the ordering may be biased toward training classes; testing on unseen actions would reveal whether the semantic ordering generalizes.
  • The same reordering idea could transfer to other unordered sequence problems (LiDAR sweeps, unordered graph node sets) that are currently fed to SSMs in raw order.
  • A head-to-head with transformer-based 4D action recognition on the same datasets would show whether the linear-cost claim costs accuracy.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 1 minor

Summary. The submission carries the title and abstract of a paper titled "UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling," which claims a selective state space model for point cloud video action recognition with components STSS, STSA, TIS, prompt-guided clustering, and experiments on MSR-Action3D, NTU RGB+D, and Synthia 4D. However, the full text supplied is a different paper, "Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning" (DualNILM), a NILM paper with a Transformer-based multi-task architecture, equations for aggregate power with behind-the-meter injection, and experiments on laboratory/REDD/UK-DALE datasets. None of the UST-SSM components, the point cloud video formulation, the prompt-guided clustering mechanism, the 1D-scan reordering, or the claimed benchmarks appear anywhere in the supplied manuscript.

Significance. If the UST-SSM method as described in the abstract were fully developed and validated, it could be a meaningful contribution: extending selective SSMs to point cloud videos with linear complexity is an interesting direction, and the proposed components address a real difficulty (the spatio-temporal disorder of point clouds for unidirectional scanning). However, the submitted manuscript contains no technical exposition, no equations, no algorithm, no experimental protocol, no results, and no ablations for UST-SSM. The full text is entirely a separate NILM paper. Consequently, the significance of the claimed contribution cannot be assessed: there is no verifiable content to support the abstract's claims. No strengths such as machine-checked proofs, reproducible code, or parameter-free derivations for UST-SSM are present in the submission.

major comments (3)
  1. [Abstract vs. Full Text (whole manuscript)] The central claim of the paper, as stated in the abstract, is that UST-SSM achieves strong action recognition on point cloud videos via Spatial-Temporal Selection Scanning, Spatio-Temporal Structure Aggregation, and Temporal Interaction Sampling. The full text, however, is a different paper titled 'Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning' (DualNILM). The full text contains no mention of STSS, STSA, TIS, prompt-guided clustering, 1D scanning, MSR-Action3D, NTU RGB+D, or Synthia 4D. The method, equations (e.g., Eqs. 1-20), experiments (e.g., Tables 3-17), and ablation studies all pertain to NILM. The submitted content therefore provides no evidence whatsoever for the abstract's claims. This is a load-bearing evidentiary absence: the manuscript does not contain the paper it purports to be, so the claimed contribution is entirely unsupported.
  2. [Abstract (experimental claim)] Even taking the abstract in isolation, the sentence 'Experimental results on the MSR-Action3D, NTU RGB+D, and Synthia 4D datasets validate the effectiveness of our method' is made without any accompanying protocol, statistics, baselines, or ablations. No numbers are reported, no evaluation metric is defined, and no comparison methods are listed. Because the full text does not elaborate on these experiments, the claim is not checkable. The reader cannot determine whether the reported validation would be significant, whether the method outperforms existing state of the art, or whether the experiments are conducted under standard protocols for these datasets.
  3. [Section 4 (architecture) and Section 5 (experiments)] The full text's architecture and experiments are entirely for DualNILM. Section 4 describes CNN encoders, Transformer encoders/decoders, and task-specific projections for appliance state recognition and energy injection disaggregation. Section 5 describes NILM datasets and PV simulation. None of these sections correspond to the UST-SSM components named in the abstract. Since the abstract's mechanism (prompt-guided clustering to reorganize unordered points into semantic-aware sequences) is the load-bearing idea that would justify the SSM's unidirectional scanning, its complete absence from the manuscript means the core technical proposal cannot be evaluated.
minor comments (1)
  1. [Abstract (code link)] The abstract states 'Our code is available at https://github.com/wangzy01/UST-SSM.' The full text does not mention this repository or provide any code listings. If the submission is intended to be the UST-SSM paper, the repository link should be supplemented with a version of the code or a detailed appendix; as submitted, the link is not verifiable.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identifiable: the abstract claims empirical validation on external benchmarks; the attached full text is an unrelated NILM paper, so no derivation chain exists to audit.

full rationale

The claimed UST-SSM derivation chain exists only in the abstract, which names three components (Spatial-Temporal Selection Scanning, Spatio-Temporal Structure Aggregation, Temporal Interaction Sampling) and asserts that experiments on MSR-Action3D, NTU RGB+D, and Synthia 4D validate effectiveness. The full text supplied is a different paper, 'Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning' (DualNILM), with different authors, equations, experiments, and benchmarks. None of the UST-SSM components, the prompt-guided clustering mechanism, the 1D-scan reordering, or the cited action-recognition datasets appear anywhere in the submitted full text. There is therefore no equation or derivation to compare with its inputs, no fitted parameter renamed as a prediction, and no self-citation chain invoked as a load-bearing premise. The only evidentiary issue is absence of the supporting manuscript, which is a completeness/integrity concern rather than circularity. Per the rule that circularity must be exhibited by quoting the paper and showing a specific reduction, no circular step can be identified here. The empirical claim targets external benchmarks and is thus in the non-circular direction; unverifiability from the provided text does not constitute circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

Abstract-only audit forced by the full-text mismatch. No free parameters or invented entities are disclosed at the abstract level; the assumptions listed are the premises the abstract states or implicitly relies on, and none can be verified because the supporting text is a different paper.

assumptions (3)
  • domain assumption Selective state space models provide effective sequence modeling with linear complexity for video-style inputs.
    Abstract: 'Selective State Space Models (SSMs) have shown good performance in sequence modeling with linear complexity.' Borrowed from the SSM/Mamba literature, not re-derived here.
  • domain assumption Prompt-guided clustering can reorganize unordered points into semantic-aware sequences sufficient to make unidirectional SSM scanning effective on 4D point cloud video.
    Central method premise (STSS, described only in the abstract). No theoretical or empirical support is available in the supplied text.
  • domain assumption MSR-Action3D, NTU RGB+D, and Synthia 4D are valid benchmarks for point cloud video action recognition and the claimed results are computed under standard protocols.
    Abstract: 'Experimental results ... validate the effectiveness of our method.' With the full text absent, the protocol and baselines cannot be checked.

how reviews work

0 comments
Cite this review

Pith. "Pith review of UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling." pith.science (2026). https://pith.science/paper/IKL4COMU

@misc{pith2026250814604,
  author       = {Pith},
  title        = {Pith review of: UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IKL4COMU}},
  note         = {Machine review of arXiv:2508.14604}
}
read the original abstract

Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous human actions. Although Selective State Space Models (SSMs) have shown good performance in sequence modeling with linear complexity, the spatio-temporal disorder of point cloud videos hinders their unidirectional modeling when directly unfolding the point cloud video into a 1D sequence through temporally sequential scanning. To address this challenge, we propose the Unified Spatio-Temporal State Space Model (UST-SSM), which extends the latest advancements in SSMs to point cloud videos. Specifically, we introduce Spatial-Temporal Selection Scanning (STSS), which reorganizes unordered points into semantic-aware sequences through prompt-guided clustering, thereby enabling the effective utilization of points that are spatially and temporally distant yet similar within the sequence. For missing 4D geometric and motion details, Spatio-Temporal Structure Aggregation (STSA) aggregates spatio-temporal features and compensates. To improve temporal interaction within the sampled sequence, Temporal Interaction Sampling (TIS) enhances fine-grained temporal dependencies through non-anchor frame utilization and expanded receptive fields. Experimental results on the MSR-Action3D, NTU RGB+D, and Synthia 4D datasets validate the effectiveness of our method. Our code is available at https://github.com/wangzy01/UST-SSM.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

59 extracted references · 50 canonical work pages

  1. [1]

    IEEE Standard Definitions for the Measurement of Electric Power Quantities Under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions

    2010. IEEE Standard Definitions for the Measurement of Electric Power Quantities Under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions. https: //ieeexplore.ieee.org/document/5439063

    work page arXiv 2010

  2. [2]

    Photovoltaics in buildings

    2016. Photovoltaics in buildings

    work page 2016

  3. [3]

    Rotating electrical machines – Part 1: Rating and performance

    2022. Rotating electrical machines – Part 1: Rating and performance. https: //webstore.iec.ch/publication/65446

    work page 2022

  4. [4]

    SM Abu Adnan Abir, Adnan Anwar, Jinho Choi, and ASM Kayes. 2021. Iot- enabled smart energy grid: Applications and challenges.IEEE access9 (2021), 50961–50981

    work page 2021

  5. [5]

    Shun-ichi Amari. 1993. Backpropagation and stochastic gradient descent method. Neurocomputing5, 4-5 (1993), 185–196

    1993

  6. [6]

    M. S. Aygen and Mustafa Inci. 2020. Zero-Sequence Current Injection Based Power Flow Control Strategy for Grid Inverter Interfaced Renewable Energy Systems.Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 44 (2020), 7782–7803. doi:10.1080/15567036.2020.1834029 Preprint, Accepted to e-Energy ’26, June 23–25, 2026, Banff, Canada X. W...

    arXiv 2020

  7. [7]

    Mohammed Ayub and El-Sayed M El-Alfy. 2023. Contextual sequence-to-point deep learning for household energy disaggregation.IEEE Access(2023)

    work page 2023

  8. [8]

    Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer Normaliza- tion. arXiv:1607.06450 [stat.ML] https://arxiv.org/abs/1607.06450

    arXiv 2016

Show all 59 references
  1. [9]

    Barbose, Samantha Weaver, and Naïm R

    Galen L. Barbose, Samantha Weaver, and Naïm R. Darghouth. 2014.Tracking the Sun VII: An Historical Summary of the Installed Price of Photovoltaics in the United States from 1998–2013. Technical Report LBNL-6808E. Lawrence Berkeley National Laboratory, Berkeley, CA. https://emp...

    2014

  2. [10]

    Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, and Mani Srivastava. 2014. NILMTK: An open source toolkit for non-intrusive load monitoring. InProceedings of the 5th international conference on Future energy systems. 265–276

    2014

  3. [11]

    2013.New Solar Homes Partnership (NSHP) Guide- book(seventh ed.)

    California Energy Commission. 2013.New Solar Homes Partnership (NSHP) Guide- book(seventh ed.). Technical Report CEC-300-2013-009-ED7-CMF. California En- ergy Commission, Sacramento, CA. https://efiling.energy.ca.gov/GetDocument. aspx?tn=72451

    2013

  4. [12]

    Yifei Cao, Dongxu Chang, Huachen Liu, Jiaxiang Cui, and Chu Ba. 2023. Multi- task Learning for Non-Intrusive Load Monitoring. In2023 5th International Con- ference on Frontiers Technology of Information and Computer (ICFTIC). 259–267. doi:10.1109/ICFTIC59930.2023.10456139

    2023

  5. [13]

    İsmail Hakkı Çavdar and Vahit Feryad. 2021. Efficient design of energy disaggre- gation model with bert-nilm trained by adax optimization method for smart grid. Energies14, 15 (2021), 4649

    2021

  6. [14]

    Dong Chen and David Irwin. 2017. Sundance: Black-box behind-the-meter solar disaggregation. InProceedings of the eighth international conference on future energy systems. 45–55

    2017

  7. [15]

    Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A scalable tree boosting system. InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794

    2016

  8. [16]

    Xinlei Chen and Omid Ardakanian. 2020. Solar Disaggregation: State of the Art and Open Challenges. InProceedings of the 5th International Workshop on Non-Intrusive Load Monitoring(Virtual Event, Japan)(NILM’20). Association for Computing Machinery, New York, NY, USA, 6–10. doi...

    2020

  9. [17]

    Jun Du, Yanhui Tu, Li-Rong Dai, and Chin-Hui Lee. 2016. A regression approach to single-channel speech separation via high-resolution deep neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing24, 8 (2016), 1424–1437

    2016

  10. [18]

    2013.Solar engineering of thermal processes

    John A Duffie and William A Beckman. 2013.Solar engineering of thermal processes. John Wiley & Sons

    2013

  11. [19]

    Marc Escoto, Antoni Guerrero, Elnaz Ghorbani, and Angel A Juan. 2024. Opti- mization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods.Applied Sciences14, 12 (2024), 5211

    2024

  12. [20]

    Anthony Faustine, Lucas Pereira, Hafsa Bousbiat, and Shridhar Kulkarni. 2020. UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM. InProceedings of the 5th International Workshop on Non-Intrusive Load Monitoring. 84–88

    2020

  13. [21]

    2016.Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment

    Pieter Gagnon, Robert Margolis, Jennifer Melius, Caleb Phillips, and Ryan Elmore. 2016.Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment. Technical Report NREL/TP-6A20-65298. National Renewable Energy Laboratory, Golden, CO. doi:10.2172/1236153

    2016 doi

  14. [22]

    2012.Residential, Commer- cial, and Utility-Scale Photovoltaic (PV) System Prices in the United States: Current Drivers and Cost-Reduction Opportunities

    Alan Goodrich, Ted James, and Michael Woodhouse. 2012.Residential, Commer- cial, and Utility-Scale Photovoltaic (PV) System Prices in the United States: Current Drivers and Cost-Reduction Opportunities. Technical Report NREL/TP-6A20-53347. National Renewable Energy Laboratory,...

    2012 doi

  15. [23]

    Gholizade-Narm

    Seyed Seraj Hamidi and H. Gholizade-Narm. 2016. Power Injection of Renew- able Energy Sources Using Modified Model Predictive Control.Environmental Engineering Science4 (2016), 215–224. doi:10.22059/EES.2016.59557

    2016

  16. [24]

    George W Hart. 1992. Nonintrusive appliance load monitoring.Proc. IEEE80, 12 (1992), 1870–1891

    1992

  17. [25]

    Yizhe Jia, Shuangxia Huang, Kai Sun, and Zheng O’Neill. 2019. Matrix profile and denoising autoencoder for power disaggregation.IEEE Transactions on Smart Grid10, 6 (2019), 6367–6377

    2019

  18. [26]

    Maria Kaselimi, Alexandros Arsalis, Nikolaos Doulamis, Anastasios Doulamis, and Eleni Kaldoudi. 2020. Multi-channel recurrent convolutional neural networks for energy disaggregation.Energy189 (2020), 116129

    2020

  19. [27]

    Maria Kaselimi, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, and Anastasios Doulamis. 2019. Multi-channel recurrent convolutional neural networks for energy disaggregation.IEEE Access7 (2019), 81047–81056

    2019

  20. [28]

    Jack Kelly and William Knottenbelt. 2015. Neural NILM: Deep neural networks applied to energy disaggregation.In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments(2015), 55–64

    2015

  21. [29]

    Jack Kelly and William Knottenbelt. 2015. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific data2, 1 (2015), 1–14

    2015

  22. [30]

    Hyunsoo Kim, Manas Marwah, Martin Arlitt, Nathan Lyon, and Jiawei Han

  23. [31]

    Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic opti- mization.arXiv preprint arXiv:1412.6980(2014)

    2014 arXiv

  24. [32]

    Zico Kolter and Tommi Jaakkola

    J. Zico Kolter and Tommi Jaakkola. 2012. Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. InProceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol...

    2012

  25. [33]

    J Zico Kolter and Matthew J Johnson. 2011. REDD: A public data set for energy disaggregation research. InWorkshop on data mining applications in sustainability (SIGKDD), San Diego, CA, Vol. 25. Citeseer, 59–62

    2011

  26. [34]

    Daniel D Lee and H Sebastian Seung. 1999. Learning the parts of objects by non-negative matrix factorization.nature401, 6755 (1999), 788–791

    1999

  27. [35]

    Xiangrui Li, Yunqi Wang, Yang Gao, and Bo Jie. 2024. A bert-based method for residential behind-the-meter pv monitoring with nilm. In2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 4964–4969

    2024

  28. [36]

    Rehan Liaqat and I. A. Sajjad. 2022. An Event Matching Energy Disaggre- gation Algorithm Using Smart Meter Data.Electronics(2022). doi:10.3390/ electronics11213596

    2022

  29. [37]

    Wei Liu, Tobias Placke, and KT Chau. 2022. Overview of batteries and battery management for electric vehicles.Energy Reports8 (2022), 4058–4084

    2022

  30. [38]

    Mengqi Lu and Zuyi Li. 2019. A hybrid event detection approach for non-intrusive load monitoring.IEEE Transactions on Smart Grid11, 1 (2019), 528–540

    2019

  31. [39]

    Stephen Makonin and Fred Popowich. 2015. Nonintrusive load monitoring (NILM) performance evaluation: A unified approach for accuracy reporting. Energy Efficiency8 (2015), 809–814

    2015

  32. [40]

    2002.PVW ATTS version 2–enhanced spatial resolution for calculating grid-connected PV performance

    Bill Marion, Marty Anderberg, Pat Gray-Hann, Richard George, Donna Heimiller, and Christian Gueymard. 2002.PVW ATTS version 2–enhanced spatial resolution for calculating grid-connected PV performance. Technical Report. National Renewable Energy Lab., Golden, CO (US)

    2002

  33. [41]

    M. A. S. Masoum and E. F. Fuchs. 2015.Power Quality in Power Systems and Electrical Machines. Academic Press

    2015

  34. [42]

    Mohamed Nait Meziane, Philippe Ravier, Guy Lamarque, Jean-Luc Bunetel, and Yannick Raingeaud. 2017. High Accuracy Event Detection for Non-Intrusive Load Monitoring. In2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2452–2456. doi:10.1109...

    2017

  35. [43]

    Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV). IEEE, 565–571

    2016

  36. [44]

    National Renewable Energy Laboratory (NREL). 2020. National Solar Radiation Database (NSRDB). https://nsrdb.nrel.gov/. Accessed: [Insert date accessed]

    2020

  37. [45]

    S Mahdi Noori RA, Masoume Mahmoodi, Ahmad Attarha, José Iria, Paul Scott, and Dan Gordon. 2023. Behind-the-Meter Solar Disaggregation: The Value of Information. In2023 IEEE PES 15th Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 1–6

    2023

  38. [46]

    Hasan Rafiq, Prajowal Manandhar, Edwin Rodriguez-Ubinas, Omer Ahmed Qureshi, and Themis Palpanas. 2024. A review of current methods and chal- lenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context.Energy and Buildings(2024), 113890

    2024

  39. [47]

    Habiballah Rahimi-Eichi, Unnati Ojha, Federico Baronti, and Mo-Yuen Chow

  40. [48]

    Saleem, Saumajit Saha, Umer Izhar, and L

    M. Saleem, Saumajit Saha, Umer Izhar, and L. Ang. 2022. Integration Challenges of Inverter Based Renewable Energy Sources in Weak Grids. In2022 IEEE Industry Applications Society Annual Meeting (IAS). 1–18. doi:10.1109/IAS54023.2022. 9939753

    2022

  41. [49]

    Rui Shang et al. 2024. GraphNILM: A Graph Neural Network for Energy Disag- gregation. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. IEEE, 234–242

    2024

  42. [50]

    Singh, B

    B. Singh, B. N. Singh, A. Chandra, K. Al-Haddad, A. Pandey, and D. P. Kothari

  43. [51]

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need.Advances in Neural Information Processing Systems30 (2017)

    2017

  44. [52]

    Xudong Wang, Guoming Tang, Yi Wang, Srinivasan Keshav, and Yu Zhang. 2022. Evsense: A robust and scalable approach to non-intrusive ev charging detection. InProceedings of the Thirteenth ACM International Conference on Future Energy Systems. 307–319

    2022

  45. [53]

    Junyu Xue, Xudong Wang, Xiaoling He, Shicheng Liu, Yi Wang, and Guoming Tang. 2025. Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring. arXiv:2505.06330 [cs.LG] https://arxiv.org/abs/2505.06330

    2025 arXiv

  46. [54]

    Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel Goddard, and Charles Sutton. 2018. Sequence-to-point learning with neural networks for non-intrusive Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning Preprint, Accepted to e-Energy ’26, June ...

    2018

  47. [55]

    Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models.arXiv preprint arXiv:2303.18223(2023)

    2023 arXiv

  48. [56]

    injection appliance

    Ahmed Zoha, Alexander Gluhak, Muhammad Ali Imran, and Sutharshan Ra- jasegarar. 2012. Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey.Sensors12, 12 (2012), 16838–16866. Preprint, Accepted to e-Energy ’26, June 23–25, 2026, Banff, Canada X. W...

    2012

  49. [2003]

    IEEE Transactions on Industrial Electronics50, 5 (2003), 962–981

    A Review of Single-Phase Improved Power Quality AC-DC Converters. IEEE Transactions on Industrial Electronics50, 5 (2003), 962–981. doi:10.1109/TIE. 2003.817609

    2003

  50. [2011]

    In Proceedings of the 2011 SIAM International Conference on Data Mining

    Unsupervised disaggregation of low frequency power measurements. In Proceedings of the 2011 SIAM International Conference on Data Mining. SIAM, 747–758

    2011

  51. [2013]

    Battery management system: An overview of its application in the smart grid and electric vehicles.IEEE industrial electronics magazine7, 2 (2013), 4–16

    2013

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.