Paper Citation Record · LEDGER

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions

As of 11 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2604.23524.

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

pith.paper-citation-record.v1
2604.23524 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T05:43:36.135637Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy13
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be23b846-7da7-40b3-8190-e5e1b43b92e3 · outbound

This paper cites A novel approach to wind turbine blade icing detection with limited sensor data via spatiotemporal attention siamese network.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions A novel approach to wind turbine blade icing detection with limited sensor data via spatiotemporal attention siamese network

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.545521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f9ef10ec-f538-44d5-b07f-23f0dd09b53b · outbound

This paper cites Impacts of wind power uncertainty on grid vulnerability to cascading overload failures.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Impacts of wind power uncertainty on grid vulnerability to cascading overload failures

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.548489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1650fea1-6c18-4d69-a52a-9bc69aae079e · outbound

This paper cites Resilience of renewable power systems under climate risks.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Resilience of renewable power systems under climate risks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.562088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3172c7e7-fd20-47b2-97b6-3aa4b1b9d568 · outbound

This paper cites Review of wind power scenario generation methods for optimal operation of renewable energy systems.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Review of wind power scenario generation methods for optimal operation of renewable energy systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.559165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a5586da7-4ff1-4fb7-9aef-118c2ead3478 · outbound

This paper cites Stochastic optimization and markov chain-based scenario generation for exploiting the underlying flexibilities of an active distribution network.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Stochastic optimization and markov chain-based scenario generation for exploiting the underlying flexibilities of an active distribution network

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.565342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6e2e015-216c-4320-9439-071ba599b873 · outbound

This paper cites Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.551462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bf200694-cf35-4e3c-ae06-4c38d1f4c24a · outbound

This paper cites Probabilistic load flow method based on nataf transformation and latin hypercube sampling.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Probabilistic load flow method based on nataf transformation and latin hypercube sampling

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.555915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ac8a433e-88ad-4167-b300-d4d3de6cba9a · outbound

This paper cites Model-free renewable scenario generation using generative adversarial networks.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Model-free renewable scenario generation using generative adversarial networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.524559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation baed36dd-ae13-43d6-abd4-89e544f0de4a · outbound

This paper cites Conditional style-based generative adversarial networks for renewable scenario generation.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Conditional style-based generative adversarial networks for renewable scenario generation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.528107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 18cd6b19-9f25-4ba2-acb3-edaa1128b264 · outbound

This paper cites A novel scenario generation method of renewable energy using improved vaegan with controllable interpretable features.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions A novel scenario generation method of renewable energy using improved vaegan with controllable interpretable features

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.539323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 91908bc3-8290-4aab-99da-710f4ddbea3b · outbound

This paper cites Controllable renewable energy scenario generation based on pattern-guided diffusion models.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Controllable renewable energy scenario generation based on pattern-guided diffusion models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.532998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 212c2dcc-b1b7-4e01-ad15-52dabde336fc · outbound

This paper cites Wind turbine blade icing risk assessment considering power output predictions based on scso-ifcm clustering algorithm.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Wind turbine blade icing risk assessment considering power output predictions based on scso-ifcm clustering algorithm

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.542514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 93b77ff0-14f5-476c-a4ed-a2659876515d · outbound

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

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Large Language Models for Time Series: A Survey

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:26:12.636067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4133d828-3359-4a56-8779-93bb71f724c5 · outbound

This paper cites Leveraging turbine-level data for improved probabilistic wind power forecasting.

Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions Leveraging turbine-level data for improved probabilistic wind power forecasting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T19:37:54.536142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.