Paper Citation Record · LEDGER

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting

As of 20 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2501.16591.

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

pith.paper-citation-record.v1
2501.16591 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T12:06:18.992112Z

measured 26 of 26 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy16
unresolved10
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External citation measurements

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Outbound references

Observation 99e7ff1f-88cb-45c2-bd8e-ac86a3d6cf35 · outbound

This paper cites The WPF problem remains unresolved due to numerous influencing variables, such as wind speed, temperature, latitude, and longitude.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting The WPF problem remains unresolved due to numerous influencing variables, such as wind speed, temperature, latitude, and longitude

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.493442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6483a056-89cc-4540-932e-3f9ad1a3b640 · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.262673Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e1984c00-c74a-4a2b-8169-b2792752e36a · outbound

This paper cites #!=𝑃! (1) where the wind power 𝑃!.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting #!=𝑃! (1) where the wind power 𝑃!

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.456766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 58b843c6-8380-49d6-b1fb-bbf8138da2a2 · outbound

This paper cites The hyperparameters of the SVM are optimized using the JAYA optimization algorithm using the most representative features in the input data.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting The hyperparameters of the SVM are optimized using the JAYA optimization algorithm using the most representative features in the input data

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.364933Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f74d2ac0-3e9b-422f-9d7f-be474a6464de · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.210274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ae6edc61-0d6b-4f55-907d-f4ac30942c6a · outbound

This paper cites The first part involves using real-time wavelet packet decomposition enhanced deep echo state network to construct the basic model with different vanishing moments.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting The first part involves using real-time wavelet packet decomposition enhanced deep echo state network to construct the basic model with different vanishing moments

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.296567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9eaf3446-aa4a-44b0-bbaa-db9c6a2c5ef5 · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.279195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3584ff08-6ce6-41db-b7d2-811558a6aeda · outbound

This paper cites 5.1.2 GEFC Dataset The second Dataset we use is from Global Energy Forecasting Competition (GEFC) 2012 - Wind Forecasting.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting 5.1.2 GEFC Dataset The second Dataset we use is from Global Energy Forecasting Competition (GEFC) 2012 - Wind Forecasting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.246168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d0ef0ea8-9a4a-4761-9dda-170589b8fd38 · outbound

This paper cites These findings again demonstrate the superiority of our model, which performs well across multiple datasets.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting These findings again demonstrate the superiority of our model, which performs well across multiple datasets

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.192696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5f351c9e-1123-476b-8799-aed35a60a1e1 · outbound

This paper cites Dayan, P., & Watkins, C.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Dayan, P., & Watkins, C

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.152213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b034a226-15f5-4c9f-8b11-b14148029f3b · outbound

This paper cites Continuous control with deep reinforcement learning.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Continuous control with deep reinforcement learning

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a60a206f-3c1e-4195-aad1-352cedb79e9b · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.094508Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ae7a3de3-43b2-4d70-aca9-f1a8f185c184 · outbound

This paper cites Wu, Q., Guan, F., Lv, C., & Huang, Y.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Wu, Q., Guan, F., Lv, C., & Huang, Y

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.057975Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5bb9c5dc-7e5f-4fd2-a026-5d3a45bdd9b4 · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 161

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.172544Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0dc924f9-f149-4f09-a664-0a60d5311677 · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 208

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.076175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3aa4653b-1baf-4bb9-8030-2170398e255f · outbound

This paper cites S., Maragatham, G., Boopathi, K., & Rangaraj, A.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting S., Maragatham, G., Boopathi, K., & Rangaraj, A

Reference 273

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.131933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a51f3f0f-056f-4ec6-b2a0-1e11f74d8ec7 · outbound

This paper cites To address the low performance of the NWP prediction algorithm, Wang et al.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting To address the low performance of the NWP prediction algorithm, Wang et al

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.438388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b3d6556-d9a5-4750-9ac8-5799fff153ab · outbound

This paper cites Although current methods have achieved significant success for WPF, numerous variables (temperature, altitude, position, humidity, pressure, etc.) could influence the results.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Although current methods have achieved significant success for WPF, numerous variables (temperature, altitude, position, humidity, pressure, etc.) could influence the results

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.475360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b3ccd547-d0d0-496c-98b1-96131d7767bb · outbound

This paper cites In this dataset, the period from July 1, 2009 to December 31, 2010, is used for model training, and the period from January 1, 2011 to June 28, 2012 is used for model testing.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting In this dataset, the period from July 1, 2009 to December 31, 2010, is used for model training, and the period from January 1, 2011 to June 28, 2012 is used for model testing

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.228793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 18ee1053-72e4-4fbb-a776-31bd0eb9e62e · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 2017

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.383210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 11f2d260-de37-4d55-8a89-b1afe7b92d3c · outbound

This paper cites However, traditional machine learning methods require manual extraction and cleaning of data, followed by feature engineering.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting However, traditional machine learning methods require manual extraction and cleaning of data, followed by feature engineering

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.330779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b96998f-f83e-49d3-a970-def577bdb3b7 · outbound

This paper cites This algorithm demonstrated excellent generalization ability for a variety of models, proving the physical method's applicability.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting This algorithm demonstrated excellent generalization ability for a variety of models, proving the physical method's applicability

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.420432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 37a3fec4-5138-49cc-898d-ecf4571be68b · outbound

This paper cites The ARIMA technique is commonly utilized in time series forecasting tasks, including wind power forecasting.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting The ARIMA technique is commonly utilized in time series forecasting tasks, including wind power forecasting

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.401027Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cd5abf43-9086-4020-a858-42c79b52c7f6 · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.313640Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8c00328a-c864-4197-bf04-4a8f86ceaba6 · outbound

This paper cites an unresolved cited work.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:06:19.347325Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e20e2e7f-58c9-4fc6-b902-7397c75ef955 · outbound

This paper cites , & R i o f l o r i d o , C.

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting , & R i o f l o r i d o , C

Reference 3764

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:06:19.112063Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

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