Paper Citation Record · LEDGER

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 2 inbound Pith citation observations for arXiv:2505.17488.

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

pith.paper-citation-record.v1
2505.17488 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:49:47.975340Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:53:13.404111Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:53:15.182214Z

Reference resolution

30 of 30 outbound references displayed

verified exact0
verified fuzzy22
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7545e459-2832-43ae-93db-739c79833e5d · outbound

This paper cites Continuous-time stochastic state-space modeling of non- stationary power system uncertainty: A data-driven systematic realiza- tion method,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Continuous-time stochastic state-space modeling of non- stationary power system uncertainty: A data-driven systematic realiza- tion method,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:52.104361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1de50f94-e9ba-4636-9c67-02efb0807d28 · outbound

This paper cites A review on the selected applications of forecasting models in renewable power systems,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics A review on the selected applications of forecasting models in renewable power systems,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:51.917013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 23d5e867-51ca-4236-949c-48255f8d97b7 · outbound

This paper cites Graph mining for classifying and localizing solar panels in distribution grids,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Graph mining for classifying and localizing solar panels in distribution grids,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:51.768842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2cd67cd4-5b5e-48ed-bb97-c4bb8fe00755 · outbound

This paper cites Temperature scenario generation for probabilistic load forecasting,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Temperature scenario generation for probabilistic load forecasting,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:51.551030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f77a32f5-c512-4d47-88cf-76ec38b83e6b · outbound

This paper cites Load forecasting techniques for power system: Research challenges and survey,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Load forecasting techniques for power system: Research challenges and survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:51.327590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bfda9a1c-4bbc-4a10-aee4-31abb0e440be · outbound

This paper cites Detailed hourly weather measurements for power system applications,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Detailed hourly weather measurements for power system applications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:51.168060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1544402d-f8b3-4a23-98d1-0465c1c1a9d4 · outbound

This paper cites Low-dimensional ode embedding to convert low-resolution meters into “virtual.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Low-dimensional ode embedding to convert low-resolution meters into “virtual

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:50.957461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9472392a-c8db-49c5-a4f3-a4de251212d9 · outbound

This paper cites Pix-gan: Enhance physics-informed estimation via generative adversarial network,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Pix-gan: Enhance physics-informed estimation via generative adversarial network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:50.748640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b54c29e-f18f-4d0b-9040-31d597b7346e · outbound

This paper cites Hyndman, Forecasting: principles and practice.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Hyndman, Forecasting: principles and practice

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:50.555615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5191a83d-8f27-476d-878a-b901ad327fce · outbound

This paper cites Short- term electricity demand forecasting with mars, svr and arima models using aggregated demand data in queensland, australia,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Short- term electricity demand forecasting with mars, svr and arima models using aggregated demand data in queensland, australia,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:50.338840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e12137d3-6a4a-4617-a304-f7839c9d910b · outbound

This paper cites Hd-deep-em: Deep expectation maximization for dynamic hidden state recovery using heterogeneous data,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Hd-deep-em: Deep expectation maximization for dynamic hidden state recovery using heterogeneous data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:50.162194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b66cec45-ecb1-4785-bf9e-19e1862be9a3 · outbound

This paper cites Short-term load forecasting based on a semi-parametric additive model,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Short-term load forecasting based on a semi-parametric additive model,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:49.973499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7595b8e6-a55a-4ffe-9a31-6dbeeee52bf0 · outbound

This paper cites Sig2vec: Dictionary design for incipient faults in distribution systems,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Sig2vec: Dictionary design for incipient faults in distribution systems,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:49.816686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2d82f8d1-40cf-42c4-b794-3f8581e85d74 · outbound

This paper cites Msq-biobert: Ambiguity resolution to enhance biobert medical question-answering,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Msq-biobert: Ambiguity resolution to enhance biobert medical question-answering,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:49.690843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a4be5d41-6fa7-4735-a193-d0234d8155e2 · outbound

This paper cites Bayesian iterative prediction and lexical- based interpretation for disturbed chinese sentence pair matching,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Bayesian iterative prediction and lexical- based interpretation for disturbed chinese sentence pair matching,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:49.524987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff37fbf0-918e-4200-835a-227129925909 · outbound

This paper cites Application of support vector machine models for forecasting solar and wind energy resources: A review,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Application of support vector machine models for forecasting solar and wind energy resources: A review,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:49.375171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dfbdddab-6fbe-4201-889e-f7a9bb0d8227 · outbound

This paper cites Short-term residential load forecasting based on lstm recurrent neural network,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Short-term residential load forecasting based on lstm recurrent neural network,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:49:46.584232Z

Source-reported events for the cited work

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Observation 59e1af21-9ba9-46f9-8285-0becdd1c4208 · outbound

This paper cites Structural tensor learning for event identification with limited labels,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Structural tensor learning for event identification with limited labels,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:49:46.707027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f938b60d-64e6-49f4-822b-d321944f95cc · outbound

This paper cites Convolutional lstm network: A machine learning approach for precipitation nowcasting,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Convolutional lstm network: A machine learning approach for precipitation nowcasting,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:49:46.826281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25585f72-f9e4-4dba-a23e-3a185c5a0e2a · outbound

This paper cites Electricity price forecasting: A review of the state-of-the-art with a look into the future,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Electricity price forecasting: A review of the state-of-the-art with a look into the future,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:49.215294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c30a00de-6036-4663-a19e-fb9c1eb2746d · outbound

This paper cites Distribution grid topology and parameter estimation using deep-shallow neural network with physical consistency,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Distribution grid topology and parameter estimation using deep-shallow neural network with physical consistency,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:49.021914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2a7f7ad-19b3-4ac3-a16b-9a7c7f28acd3 · outbound

This paper cites Handling renewable energy variability and uncertainty in power system operation,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Handling renewable energy variability and uncertainty in power system operation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:48.858925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3ec9b927-68ec-4529-b8fa-62197a6683ac · outbound

This paper cites HyperNetworks.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics HyperNetworks

Reference 23

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Source-reported events for the cited work

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Observation d556afb0-11b1-47c7-8b58-1dc5275bb06e · outbound

This paper cites Neural controlled differential equations for irregular time series,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Neural controlled differential equations for irregular time series,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:48.726133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d3839f4-15ee-4a5d-ad2a-e27f6610831d · outbound

This paper cites Neural ordinary differential equations,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Neural ordinary differential equations,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:49:47.524776Z

Source-reported events for the cited work

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Observation c72bd7c1-4148-45ed-8491-e3647a6563be · outbound

This paper cites Deep residual learning for image recognition,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Deep residual learning for image recognition,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:49:47.595702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b9a1e02-64db-4d11-9a95-8c876830e8e6 · outbound

This paper cites an unresolved cited work.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:49:48.511411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6087a1c5-6b3f-4c14-8f24-b8ae50c3878a · outbound

This paper cites Tackling climate change with machine learning,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Tackling climate change with machine learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:48.356524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 26c99a2f-5a0b-4d15-9a37-33757ef8e72b · outbound

This paper cites Recurrent neural networks for multivariate time series with missing values,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Recurrent neural networks for multivariate time series with missing values,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:49:47.897147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c53c2cf1-c407-45da-9eae-64700c48a3e9 · outbound

This paper cites Latent ordinary differential equations for irregularly-sampled time series,.

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics Latent ordinary differential equations for irregularly-sampled time series,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:48.179053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 32eb1796-3da2-4a46-a800-c3666e510b99 · inbound

External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting cites this paper.

External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:53:15.242570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e0b988be-f85c-47ec-b1d4-97562f49740d · inbound

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference cites this paper.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.733395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.