Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:26:17.845185Z
Paper Citation Record · LEDGER
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset
As of 15 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2505.24505.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
14 of 14 outbound references displayed
- verified exact0
- verified fuzzy14
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 62a0ab82-03f7-4451-bc36-998f9840b3ef · outbound
This paper cites An introduction to optimal power flow: Theory, formulation, and examples,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset An introduction to optimal power flow: Theory, formulation, and examples,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:20.600839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 480e2992-394f-45ab-a42b-3542ec9c88d3 · outbound
This paper cites Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:20.400069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d2925a32-ac15-4068-a12e-c20ef51f51d1 · outbound
This paper cites Learning to optimize: A primer and a benchmark,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Learning to optimize: A primer and a benchmark,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:20.247152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation efb45673-025e-45f6-b0ef-ad67af5548d1 · outbound
This paper cites Deep unfolding for commu- nications systems: A survey and some new directions,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Deep unfolding for commu- nications systems: A survey and some new directions,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:20.104444Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6ea08e2a-d46d-423a-8e35-917e63fbeee2 · outbound
This paper cites Learning to solve decision problems over two timescales: An application to 5g puncturing,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Learning to solve decision problems over two timescales: An application to 5g puncturing,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:19.912647Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 71b2708c-bfda-4629-b9d9-2bdf0f3852b3 · outbound
This paper cites Amp-inspired deep net- works for sparse linear inverse problems,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Amp-inspired deep net- works for sparse linear inverse problems,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:19.767782Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a2423f70-4b1d-4f8e-a9ff-d7194bb2da87 · outbound
This paper cites Optimal power flow using graph neural networks,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Optimal power flow using graph neural networks,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:19.592876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ae078853-d3ff-4732-9022-de0a43a7e4b0 · outbound
This paper cites Deepopf: A feasibility- optimized deep neural network approach for ac optimal power flow problems,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Deepopf: A feasibility- optimized deep neural network approach for ac optimal power flow problems,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:19.438497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a1a42c75-cb79-484c-9485-12237b0af9fb · outbound
This paper cites Unsupervised optimal power flow using graph neural networks,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Unsupervised optimal power flow using graph neural networks,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:19.219711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6fc929eb-ecb7-492a-9993-e1e2d85a9b68 · outbound
This paper cites Energ ´ıas renovables en uruguay.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Energ ´ıas renovables en uruguay
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:19.009162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0255e831-2405-44a9-a811-c6c99035af42 · outbound
This paper cites Graphs, convolutions, and neural networks: From graph filters to graph neural networks,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Graphs, convolutions, and neural networks: From graph filters to graph neural networks,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:18.782106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4bac01f4-1a75-49a9-824c-cd329b774a8e · outbound
This paper cites Stability properties of graph neural networks,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Stability properties of graph neural networks,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:18.603247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ad4cd271-09f2-412d-9950-657692cd6a6c · outbound
This paper cites pandapower — an open-source python tool for convenient modeling, analysis, and optimization of electric power systems,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset pandapower — an open-source python tool for convenient modeling, analysis, and optimization of electric power systems,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:18.343424Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a9017a66-6eea-4b7a-a83b-79e707c5f19e · outbound
This paper cites Optuna: A next-generation hyperparameter optimization framework,.
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset Optuna: A next-generation hyperparameter optimization framework,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:26:18.143167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
No inbound Pith citation observations are available.