Paper Citation Record · LEDGER

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2505.06330.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.06330 v3

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:51:41.450033Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:27:04.918722Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T18:27:05.643751Z

Reference resolution

38 of 38 outbound references displayed

verified exact2
verified fuzzy26
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bc29d172-129d-4002-9e8c-671b78386049 · outbound

This paper cites Nonintrusive appliance load monitoring.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Nonintrusive appliance load monitoring

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.155746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3951ec77-8c95-486e-873d-e39f6ad8f707 · outbound

This paper cites Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.141026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d56d3a9a-92bb-440a-914e-f67a5cd756b3 · outbound

This paper cites Redd: A public data set for energy disag- gregation research.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Redd: A public data set for energy disag- gregation research

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.125322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e5a95f9d-eb18-4d00-85df-e787489b908f · outbound

This paper cites Nonintrusive load monitoring (nilm) performance evaluation: A unified approach for accuracy reporting.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Nonintrusive load monitoring (nilm) performance evaluation: A unified approach for accuracy reporting

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.113036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08e5aa60-26b6-4a76-92b9-5cfd71da4e82 · outbound

This paper cites Deep learning.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Deep learning

Reference 5

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Source-reported events for the cited work

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Observation d1701f82-7b27-4cc4-b009-63d6a21128f1 · outbound

This paper cites Neural nilm: Deep neural networks applied to energy disaggregation.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Neural nilm: Deep neural networks applied to energy disaggregation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.090397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9e5f451e-ffc1-4c43-84cc-5465b7cc3b9b · outbound

This paper cites A regression approach to single-channel speech separation via high-resolution deep neural networks.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring A regression approach to single-channel speech separation via high-resolution deep neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.076760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4d8dc962-521d-4512-9da4-8507cb2a5352 · outbound

This paper cites Sequence-to-point learning with neural networks for non-intrusive load monitoring.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Sequence-to-point learning with neural networks for non-intrusive load monitoring

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.060574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3f647488-b91b-433c-9c19-be419ca35e58 · outbound

This paper cites Bert4nilm: A bidirectional transformer model for non-intrusive load monitoring.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Bert4nilm: A bidirectional transformer model for non-intrusive load monitoring

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.046438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 79286026-3604-4b18-8d83-a115d9e1dc76 · outbound

This paper cites Auglpn-nilm: Augmented light- weight parallel network for nilm embedding attention module over sequence to point.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Auglpn-nilm: Augmented light- weight parallel network for nilm embedding attention module over sequence to point

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.033397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0c773e49-a657-4a7d-abd7-a125d701bf13 · outbound

This paper cites Towards real-world deployment of nilm systems: Challenges and practices.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Towards real-world deployment of nilm systems: Challenges and practices

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:42.010157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9ca1165-f46b-4d61-b4c9-eba726279c7d · outbound

This paper cites A Survey of Large Language Models.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring A Survey of Large Language Models

Reference 12

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Source-reported events for the cited work

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Observation 3d3736df-3b96-4608-a37d-adccf270dc82 · outbound

This paper cites A survey on in-context learning.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring A survey on in-context learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.986623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d0da119-7fbb-43a5-80c9-30fc854ccad8 · outbound

This paper cites A survey of zero-shot learning: Settings, methods, and applications.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring A survey of zero-shot learning: Settings, methods, and applications

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.971961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e188abaa-57f9-4780-9f6e-22e9b1dbdbd2 · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Generalizing from a few examples: A survey on few-shot learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:51:41.331102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40ffe675-468e-43e0-8592-a1eb5c91cfa8 · outbound

This paper cites The uk-dale dataset, domestic appliance- level electricity demand and whole-house demand from five uk homes.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring The uk-dale dataset, domestic appliance- level electricity demand and whole-house demand from five uk homes

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.947834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 512e8737-b5b3-4410-ac2d-9e613d8f3acb · outbound

This paper cites Approximate inference in additive factorial hmms with application to energy disaggregation.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Approximate inference in additive factorial hmms with application to energy disaggregation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.930905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 205d26a2-b649-4fe3-a740-16d801b40ad3 · outbound

This paper cites Gradient-based learning applied to document recognition.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Gradient-based learning applied to document recognition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T22:51:41.347343Z

Source-reported events for the cited work

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Observation 8fc816d7-dc3b-42b6-a8fd-8a9daa3d9514 · outbound

This paper cites Long short-term memory.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Long short-term memory

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:51:41.352599Z

Source-reported events for the cited work

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Observation fd052635-1ce0-4a79-973b-e05c68b206ab · outbound

This paper cites Attention is all you need.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Attention is all you need

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.890276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5f06e083-aa84-4f84-a484-243550d6781b · outbound

This paper cites A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (nilm) in residential context.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (nilm) in residential context

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.871362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0427477-1bbd-4a69-8fac-2df87afd9613 · outbound

This paper cites Using explain- ability tools to inform nilm algorithm performance: a decision tree approach.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Using explain- ability tools to inform nilm algorithm performance: a decision tree approach

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.855417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f2a4f2c4-ba2a-45b3-b63d-0b7537c4bc0b · outbound

This paper cites Toward transparent load disaggregation—a framework for quantitative evaluation of explainability using explainable ai.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Toward transparent load disaggregation—a framework for quantitative evaluation of explainability using explainable ai

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.838678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 126c8337-fc27-44da-a449-687c38f5b4ce · outbound

This paper cites Toward explainable nilm: Real-time event-based nilm framework for high-frequency data.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Toward explainable nilm: Real-time event-based nilm framework for high-frequency data

Reference 24

Resolution: verified exact; raw_fallback, observed 2026-08-15T22:51:41.619198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13ce84aa-1438-4d96-8cef-78cb07b7603b · outbound

This paper cites Transfer learning for multi-objective non-intrusive load monitoring in smart building.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Transfer learning for multi-objective non-intrusive load monitoring in smart building

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.819205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8640997b-85c0-4720-b6c0-582690b8e879 · outbound

This paper cites Adaptive fusion feature transfer learning method for nilm.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Adaptive fusion feature transfer learning method for nilm

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.805516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d96bf346-28dc-4ed8-af9a-b2c66afd2d74 · outbound

This paper cites Evsense: A robust and scalable approach to non-intrusive ev charging detection.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Evsense: A robust and scalable approach to non-intrusive ev charging detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.791121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba1fe17d-c8ea-4551-acfe-69fb98975814 · outbound

This paper cites A semi-supervised load identification method with class incremental learning.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring A semi-supervised load identification method with class incremental learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.777765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 36c9c964-2579-47cf-8de3-57cdf0964b64 · outbound

This paper cites Fednilm: Applying federated learning to nilm applications at the edge.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Fednilm: Applying federated learning to nilm applications at the edge

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.762158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0df8501-427c-4302-98a9-d9bfe726dd4d · outbound

This paper cites Federated learning-based non-intrusive load monitoring adaptive to real-world heterogeneities.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Federated learning-based non-intrusive load monitoring adaptive to real-world heterogeneities

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.744881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f92b8ad5-9260-457a-af7d-e12209114670 · outbound

This paper cites Promptcast: A new prompt-based learning paradigm for time series forecasting.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Promptcast: A new prompt-based learning paradigm for time series forecasting

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:51:41.416745Z

Source-reported events for the cited work

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Observation ff572fa4-43c6-4936-b05a-a424f4579923 · outbound

This paper cites Large language models are zero-shot time series forecasters.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Large language models are zero-shot time series forecasters

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T22:51:41.422285Z

Source-reported events for the cited work

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Observation dc32949e-af1d-4e86-9fab-87ab7564f6b2 · outbound

This paper cites Large Language Models for Time Series: A Survey.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Large Language Models for Time Series: A Survey

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T22:51:41.426825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ace8ab12-3e0f-4758-83b2-62623c189d1c · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Chain-of-thought prompting elicits reasoning in large language models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T22:51:41.433558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f27865a-f51c-4e2d-aa32-ad385c61b2ad · outbound

This paper cites UniSep: Universal Target Audio Separation with Language Models at Scale.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring UniSep: Universal Target Audio Separation with Language Models at Scale

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:51:41.505995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 119672b5-c03e-4308-8530-220cce234ee8 · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Lisa: Reasoning segmentation via large language model

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T22:51:41.441891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dcb527a-40a4-4e3f-b370-a4e9b8e9237a · outbound

This paper cites Transfer learning for non-intrusive load monitoring.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring Transfer learning for non-intrusive load monitoring

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.679507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3681370f-0509-4631-bee8-0c25c8fa5ca3 · outbound

This paper cites APPLIANCE_NAMES[0]_status.

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring APPLIANCE_NAMES[0]_status

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:51:41.657940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 23f3ed0d-55a2-4341-9060-ef7f395656ef · inbound

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling cites this paper.

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:27:05.691573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.