Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T17:52:51.895779Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 0 inbound Pith citation observations for arXiv:2508.15508.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T17:52:51.895779Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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
82 of 82 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation b6a0aede-ef2d-463b-975e-0be411be1f74 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Koyama, M
Reference 1
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Schmeits, M
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Baran, S
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Lerch, S
Reference 4
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods C., Cuevas, O., D\' az, M., Szab\'o, M., Nicolis, O
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Nemoda, D
Reference 6
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Hochberg, Y
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Tian, Q
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods B., Nipen, T
Reference 10
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2009) Reliability, sufficiency, and the decomposition of proper scores
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2012) Evaluating raw ensembles with the continuous ranked probability score
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Observation 727a5b75-739f-4bcc-b735-c22121d2fc84 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2018) Ensemble forecasting and the need for calibration
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Lerch, S
Reference 14
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Beckman, W
Reference 16
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Observation 251e3b50-eefb-4d03-a333-b664b893750d · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods P., Zhou, Y., Deng, X
Reference 17
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Vannitsem, S
Reference 18
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 19
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Harrison, S
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2011) Making and evaluating point forecasts
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Schulz, B
Reference 22
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Raftery, A
Reference 23
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods E., Westveld, A
Reference 24
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Ranjan, R
Reference 25
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Observation b91cda20-309c-4103-83a6-5ff98f73d929 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods I., Lerch, S., Phipps, K
Reference 26
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods E., Chapman, W., Adams, S
Reference 27
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Observation edc5cbf0-42ec-4671-9eed-e25c56b5bfd0 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 28
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Observation b6170027-350e-4ae7-88af-543a05dbece1 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2000) Decomposition of the continuous ranked probability score for ensemble prediction systems
Reference 29
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Zareipour, H
Reference 30
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Observation e89177d5-9ad5-4269-90da-2f20742d6c27 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Lerch, S
Reference 31
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Observation 9766085d-9d0a-4f28-bcc6-ef2abc97ec7a · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Lerch, S
Reference 32
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Observation 600ebb24-5bcf-4d30-9498-1dbd831bca45 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2005) Quantile Regression
Reference 33
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Observation ebed0cf3-2a9b-4782-938e-61e521682e45 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2020) Why the “best” point forecast depends on the error or accuracy measure
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Gneiting, T
Reference 35
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Battaglia, P
Reference 36
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Observation fcf355fb-74e8-4fbb-99f9-29d0f9925b86 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods AIFS -- ECMWF's data-driven forecasting system
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Observation 33f78a23-dd9c-43b4-ab3c-08568a3d9b8d · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Pinson, P
Reference 39
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Lauret, P
Reference 40
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 41
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Ruiz, J
Reference 42
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Poggi, P
Reference 43
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 44
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Observation 4e8c4b89-5755-4ece-84f8-f94249ca410a · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 45
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Observation b94391a1-e7c0-4c14-a3f6-562b94a5c981 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Applied Energy, 283, 116239
Reference 46
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Observation b55df5b6-0a84-462a-bc76-b6c9a755d0bf · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 47
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods International Journal of Forecasting, 39, 981--991
Reference 48
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Observation 2ab86ebf-4d4f-42bd-aef1-667e7d8d1949 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Applied Energy, 371, 123681
Reference 49
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Observation 3bb53e85-38f9-4ce9-8d43-fb1e57c56077 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods W., Mayr, G
Reference 50
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Observation 62d45c39-c4e7-4d5c-a856-f1c13713b8d4 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Monthly Weather Review, 145, 137--147
Reference 51
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Observation 08cb86e9-f04b-4254-985f-fd530aad3696 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Faganeli Pucer, J
Reference 52
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Stewart, R
Reference 53
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Ludwig, N
Reference 54
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 55
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R
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Observation 7ca2af4a-5aa7-4a78-9793-ddf503adff8a · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Lerch, S
Reference 57
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Andreas, A
Reference 58
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Observation f573a161-0176-42a6-bbee-4e3a20f3dfc9 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods J., Mendiburu Zevallos, A
Reference 59
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Observation 5d39fd39-c56e-420d-a980-a49cbcc286a1 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2014) Probabilistic quantitative precipitation forecasting using ensemble model output statistics
Reference 60
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Observation 55860af4-9b5e-4078-802d-d15b3148f800 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Baran, S
Reference 61
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Observation c2df90cf-44eb-4ef0-828c-a6fba71ddef3 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Aggregating distribution forecasts from deep ensembles
Reference 62
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Observation 4300ebfe-2df1-45c3-8794-3ca6319ccb45 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Lerch, S
Reference 63
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Observation 1d0ef948-8453-4c2a-a559-bafc2ea8661d · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods M., Bright, J
Reference 64
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Observation c4c9f599-36e6-4f4f-b1d4-8dae4b8aade0 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Naveau, P
Reference 65
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 66
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Observation e38c08e4-be7a-49a9-9d59-32d37d6f4b4f · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Georghiou, G
Reference 67
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Observation 3b96498e-4415-48d4-97cb-41db4aaf9a50 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Unresolved cited work
Reference 68
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Observation a199e7c3-51c8-4c56-9a77-921937e7c448 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Vannitsem, S
Reference 69
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Observation cc5521a5-4452-4d59-bde2-89af9ee27af9 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods B., Demaeyer, J., Evans, G
Reference 70
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Observation 300f6ae8-9faa-4f48-99a9-a09fe8914b10 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Kleissl, J
Reference 71
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Observation daeeae21-1af0-45e6-97d7-b0961e82a057 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods The Stippling Shows Statistically Significant Grid Points
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Observation 33e87881-1a20-4e37-88df-3ddfda34af8f · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Amsterdam: Elsevier, 4th edn
Reference 73
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Observation 07044f99-8116-4bef-988e-a5e593634670 · outbound
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods (2016) Solar radiation on inclined surfaces: Corrections and benchmarks
Reference 74
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Journal of Renewable and Sustainable Energy, 13, 056101
Reference 75
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods Renewable and Sustainable Energy Reviews, 159, 112195
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods J., Gueymard, C
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and van der Meer, D
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods A., Hong, T., Kleissl, J., Huang, J., Perez, M
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Reference 80
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods and Mayer, M
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Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
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