Bridging the climate to energy data gap: simulated annealing for representative climate year selection

Bram van Duinen; Jean Thorey; Karin van der Wiel; Laurens Stoop

arxiv: 2605.15958 · v1 · pith:CBL5RDXPnew · submitted 2026-05-15 · ⚛️ physics.ao-ph · cs.CY· physics.soc-ph

Bridging the climate to energy data gap: simulated annealing for representative climate year selection

Bram van Duinen , Karin van der Wiel , Jean Thorey , Laurens Stoop This is my paper

Pith reviewed 2026-05-19 18:12 UTC · model grok-4.3

classification ⚛️ physics.ao-ph cs.CYphysics.soc-ph

keywords simulated annealingrepresentative climate yearsenergy system modelssliced Wasserstein distanceclimate ensemble selectionoptimal transportpower system planningclimate data gap

0 comments

The pith

Simulated annealing selects better climate year subsets for energy models

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish that simulated annealing can be used to choose small groups of climate years that accurately represent a much larger set of years for use in energy system models. This matters because energy models are limited in the number of years they can process, and poor choices can lead to biased results in planning for power systems. The method uses an optimization technique with a specific distance measure to find the best subsets and shows it works better than other approaches in tests for the Netherlands and Europe. If true, it would allow for more reliable climate inputs without extra computation.

Core claim

This study proposes simulated annealing as an optimisation method for selecting representative subsets of complete climate years from large climate ensembles. Representativeness is quantified using the seasonal sliced Wasserstein distance, a metric from optimal transport theory that captures representativeness on marginal distributions, inter-variable correlations, and seasonal structure simultaneously. We evaluate simulated annealing against the alternative methods random search, filtered random search, and K-Medoids clustering across three test cases spanning the Netherlands and Europe, using 180 climate years from the Pan-European Climate Database as a reference. Simulated annealing cons

What carries the argument

Simulated annealing optimization that minimizes the seasonal sliced Wasserstein distance between the selected subset and the full climate ensemble to achieve representativeness across multiple statistical properties.

If this is right

Simulated annealing outperforms random search, filtered random search, and K-Medoids clustering in producing representative climate year subsets.
The method achieves an effective sample size four to five times the actual subset size.
Subsets are 2.5 to 3.5 times more representative than current ENTSO-E practice.
The output serves as validated climate data input for any energy impact study.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Adopting this could lead to better informed energy investment decisions by capturing a wider range of weather conditions.
The approach might be adapted for selecting representative days or months instead of full years in other modeling contexts.
Direct validation with energy model runs would test if the distance metric predicts real-world impacts accurately.
This technique could apply to climate data selection in fields like agriculture or water management.

Load-bearing premise

Assuming that a small set of climate years matching the full set on weather distributions, variable correlations, and seasonal patterns will produce similar results in energy system models is the main premise.

What would settle it

If energy system models run with the selected years do not show better matching to full-ensemble results than current practice selections, the advantage would not hold.

read the original abstract

Energy system models are increasingly dependent on representative climate input. Yet, a fundamental mismatch persists between the hundreds of simulated years often used in climate science and the handful of years that computationally demanding power system models can process. Current practice, including ENTSO-E's European Resource Adequacy Assessment, relies on climate year selections that have not been validated against explicit representativeness criteria. This risks biased investment decisions and blind spots for plausible weather conditions. This study proposes simulated annealing as an optimisation method for selecting representative subsets of complete climate years from large climate ensembles. Representativeness is quantified using the seasonal sliced Wasserstein distance, a metric from optimal transport theory that captures representativeness on marginal distributions, inter-variable correlations, and seasonal structure simultaneously. We evaluate simulated annealing against the alternative methods random search, filtered random search, and K-Medoids clustering across three test cases spanning the Netherlands and Europe, using 180 climate years from the Pan-European Climate Database as a reference. Simulated annealing consistently produces the most representative subsets and outperforms all compared methods. Simulated annealing achieves an effective sample size four to five times the actual subset size. The resulting subsets are roughly 2.5--3.5 times more representative than current ENTSO-E practice. The method is application-agnostic and its output can serve as a validated climate data input to any subsequent (energy) impact study.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Simulated annealing with seasonal sliced Wasserstein distance selects better climate year subsets than baselines on that metric, but the work stays inside the proxy without testing real energy model outputs.

read the letter

The paper shows simulated annealing guided by the seasonal sliced Wasserstein distance produces subsets that score higher on representativeness than random search, filtered random search, K-Medoids, or ENTSO-E practice. Across the three test cases with the 180-year Pan-European database, it reports effective sample sizes four to five times the subset size and 2.5 to 3.5 times better performance than current practice. That is the core result worth noting first.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes simulated annealing to select representative subsets of complete climate years from large ensembles (e.g., 180 years from the Pan-European Climate Database) for use in energy system models. Representativeness is defined and quantified via the seasonal sliced Wasserstein distance, which incorporates marginal distributions, inter-variable correlations, and seasonal structure. The method is tested against random search, filtered random search, and K-Medoids clustering on three cases spanning the Netherlands and Europe; the paper reports that simulated annealing consistently yields the most representative subsets, with effective sample sizes 4–5 times the subset size and subsets 2.5–3.5 times more representative than ENTSO-E practice.

Significance. If the chosen metric proves a reliable proxy, the work supplies a systematic, optimization-based alternative to current ad-hoc or clustering-based climate-year selections, directly addressing the computational gap between climate ensembles and energy models. The comparison against multiple independent baselines and the application-agnostic framing are positive features that could support broader adoption in impact studies.

major comments (2)

[Abstract] Abstract: The central performance claims (effective sample size 4–5× subset size; 2.5–3.5× improvement over ENTSO-E) rest exclusively on the seasonal sliced Wasserstein distance serving as both the optimization objective and the sole evaluation criterion. No experiment is described that inserts the selected subsets into an energy-system model and verifies that key outputs (LOLE, total system cost, or capacity requirements) are closer to the full-ensemble statistics than those obtained from ENTSO-E or K-Medoids selections. Because the metric is both the target and the judge, superiority on the proxy does not automatically establish superiority for the stated downstream use case.
[Methods/Results] Methods/Results: The manuscript does not report error bars, run-to-run variability, or sensitivity tests with respect to the annealing schedule parameters or the sliced-Wasserstein hyperparameters (number of slices, seasonal partitioning). These omissions make it difficult to assess whether the reported outperformance is robust or could be altered by modest changes in the metric definition.

minor comments (2)

[Abstract] The abstract and introduction would benefit from a concise statement of how the effective sample size is formally defined and computed from the Wasserstein distances.
[Introduction] A short discussion of how the seasonal sliced Wasserstein distance relates to (or differs from) other representativeness metrics already used in the energy-climate literature would help readers place the contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. We address each major comment below and indicate the revisions we will make to improve the manuscript.

read point-by-point responses

Referee: The central performance claims (effective sample size 4–5× subset size; 2.5–3.5× improvement over ENTSO-E) rest exclusively on the seasonal sliced Wasserstein distance serving as both the optimization objective and the sole evaluation criterion. No experiment is described that inserts the selected subsets into an energy-system model and verifies that key outputs (LOLE, total system cost, or capacity requirements) are closer to the full-ensemble statistics than those obtained from ENTSO-E or K-Medoids selections. Because the metric is both the target and the judge, superiority on the proxy does not automatically establish superiority for the stated downstream use case.

Authors: We agree that the evaluation is performed using the same metric employed in the optimization objective. The seasonal sliced Wasserstein distance was chosen specifically because it jointly accounts for marginal distributions, inter-variable correlations, and seasonal structure—properties we consider directly relevant to energy-system performance. The manuscript presents the method as application-agnostic, supplying representative year subsets that any subsequent energy impact study can adopt. We acknowledge that this does not constitute direct verification of improved LOLE, costs, or capacity outcomes. In revision we will add a discussion paragraph justifying the metric as a proxy and explicitly state that end-to-end validation in a full energy-system model remains future work; we will also moderate the abstract wording to avoid implying such validation has been performed. revision: partial
Referee: The manuscript does not report error bars, run-to-run variability, or sensitivity tests with respect to the annealing schedule parameters or the sliced-Wasserstein hyperparameters (number of slices, seasonal partitioning). These omissions make it difficult to assess whether the reported outperformance is robust or could be altered by modest changes in the metric definition.

Authors: We accept this observation. The revised manuscript will report standard deviations and error bars obtained from at least ten independent runs of simulated annealing for each test case. We will also add a sensitivity section that varies the number of slices (e.g., 10, 50, 100) and the seasonal partitioning scheme, showing that the relative ranking of methods remains stable under these changes. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses external metric and independent baselines

full rationale

The paper defines representativeness using the seasonal sliced Wasserstein distance from optimal transport theory as an external proxy capturing marginals, correlations, and seasonality. It applies simulated annealing to minimize this metric and evaluates the resulting subsets against independent methods (random search, filtered random search, K-Medoids) and ENTSO-E practice using the identical metric on the 180-year Pan-European Climate Database. No equations reduce reported improvements (e.g., effective sample size or 2.5–3.5× better representativeness) to a fitted parameter or input by construction, and no load-bearing self-citations or uniqueness theorems are invoked. The central claims rest on empirical optimization performance and external benchmarks, rendering the derivation self-contained against the stated criteria.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Only the abstract is available; the ledger is therefore populated at the level of stated assumptions rather than detailed derivations.

free parameters (1)

annealing schedule parameters
Temperature cooling rate and iteration count are required for simulated annealing but not numerically specified in the abstract.

axioms (1)

domain assumption The seasonal sliced Wasserstein distance adequately captures the statistical properties relevant to energy system performance.
Invoked when the abstract states that representativeness is quantified using this metric on marginals, correlations, and seasonal structure.

pith-pipeline@v0.9.0 · 5790 in / 1332 out tokens · 39736 ms · 2026-05-19T18:12:16.861732+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

57 extracted references · 57 canonical work pages

[1]

Intergovernmental Panel on Climate Change (IPCC). inClimate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth As- sessment Report of the Intergovernmental Panel on Climate Change(ed Inter- governmental Panel on Climate Change (IPCC)) 613–746 (Cambridge Univer- sity Press, Cambridge, 2023). doi:10.1017/9781009157926.008

work page doi:10.1017/9781009157926.008 2022
[2]

H., Virgüez, E., Reich, N., Dowling, J., Bloomfield, H., Antonini, E

Ruggles, T. H., Virgüez, E., Reich, N., Dowling, J., Bloomfield, H., Antonini, E. G. A., Davis, S. J., Lewis, N. S. & Caldeira, K. Planning reliable wind- and solar-based electricity systems.Advances in Applied Energy15,100185. doi:10 . 1016 / j . adapen.2024.100185. (2024)

work page arXiv 2024
[3]

Grochowicz, A., van Greevenbroek, K., Benth, F. E. & Zeyringer, M. Intersecting near-optimal spaces: European power systems with more resilience to weather variability.Energy Economics118,106496. doi:10.1016/j.eneco.2022.106496. (2023)

work page doi:10.1016/j.eneco.2022.106496 2022
[4]

K., Andresen, G

Gøtske, E. K., Andresen, G. B., Neumann, F. & Victoria, M. Designing a sector- coupled European energy system robust to 60 years of historical weather data. Nature Communications15,10680. doi:10.1038/s41467-024-54853-3. (2024)

work page doi:10.1038/s41467-024-54853-3 2024
[5]

Pecora, B., Rhodes, J. D. & Webber, M. E. Quantifying the impacts of weather year selection on power sector capacity expansion models.Energy340,138979. doi:10.1016/j.energy.2025.138979. (2025)

work page doi:10.1016/j.energy.2025.138979 2025
[6]

Pfenninger, S. Dealing with multiple decades of hourly wind and PV time se- ries in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability.Applied Energy197,1–13. doi:10.1016/j.apenergy.2017.03.051. (2017). REFERENCES 18

work page doi:10.1016/j.apenergy.2017.03.051 2017
[7]

Kelder, T., Heinrich, D., Klok, L., Thompson, V., Goulart, H. M. D., Hawkins, E., Slater, L. J., Suarez-Gutierrez, L., Wilby, R. L., Coughlan de Perez, E., Stephens, E. M., Burt, S., van den Hurk, B., de Vries, H., van der Wiel, K., Schipper, E. L. F., Carmona Baéz, A., van Bueren, E. & Fischer, E. M. How to stop being surprised by unprecedented weather.N...

work page doi:10.1038/s41467- 2025
[8]

& Odenberger, M

Öberg, S., Johnsson, F. & Odenberger, M. The impact of inter-annual weather variations on energy storage and flexible generation – a UK case study.Energy 335,137780. doi:10.1016/j.energy.2025.137780. (2025)

work page doi:10.1016/j.energy.2025.137780 2025
[9]

S., Psarros, G

Chatzistylianos, E. S., Psarros, G. N., Dratsas, P. A. & Papathanassiou, S. A. Assess- ing the Impact of Climate Conditions Within CEP Models: A Comparative Study of Scenario Reduction Methods.IEEE Access14,37245–37264. doi:10.1109/ ACCESS.2026.3671115. (2026)

work page arXiv 2026
[10]

ENTSO-E.TYNDP 2024 Scenarios - Methodology Report(ENTSO-E, Brussels, 2025).https://2024.entsos-tyndp-scenarios.eu/wp-content/uploads/2025/01/ TYNDP_2024_Scenarios_Methodology_Report_Final_Version_250128.pdf

work page 2024
[11]

ENTSO-E.European Resource Adequacy Assessment 2025(ENTSO-E, Brussels, 2025).https://eepublicdownloads.blob.core.windows.net/public-cdn-container/ clean-documents/sdc-documents/ERAA/ERAA_2025_ExecutiveReport_ENTSOEProposal_ Dec2025.pdf

work page 2025
[12]

Suarez-Gutierrez, L., Li, C., Müller, W. A. & Marotzke, J. Internal variability in Euro- pean summer temperatures at 1.5°C and 2°C of global warming.Environmen- tal Research Letters13,064026. doi:10.1088/1748-9326/aaba58. (2018)

work page doi:10.1088/1748-9326/aaba58 2018
[13]

Van Der Wiel, K., Wanders, N., Selten, F. M. & Bierkens, M. F. P. Added Value of Large Ensemble Simulations for Assessing Extreme River Discharge in a 2°C Warmer World.Geophysical Research Letters46,2093–2102. doi:10 . 1029 / 2019GL081967. (2019)

work page 2093
[14]

& Selten, F

Van Der Wiel, K., Stoop, L., Van Zuijlen, B., Blackport, R., Van Den Broek, M. & Selten, F. Meteorological conditions leading to extreme low variable renewable energy production and extreme high energy shortfall.Renewable and Sustain- able Energy Reviews111,261–275. doi:10.1016/j.rser.2019.04.065. (2019)

work page doi:10.1016/j.rser.2019.04.065 2019
[15]

& Zscheis- chler, J

Bevacqua, E., Suarez-Gutierrez, L., Jézéquel, A., Lehner, F., Vrac, M., Yiou, P. & Zscheis- chler, J. Advancing research on compound weather and climate events via large ensemble model simulations.Nature Communications14,2145. doi:10.1038/ s41467-023-37847-5. (2023)

work page 2023
[16]

& Van Der Wiel, K

Muntjewerf, L., Bintanja, R., Reerink, T. & Van Der Wiel, K. The KNMI Large En- semble Time Slice (KNMI–LENTIS).Geoscientific Model Development16,4581–

work page
[17]

doi:10.5194/gmd-16-4581-2023. (2023)

work page doi:10.5194/gmd-16-4581-2023 2023
[18]

C., Brayshaw, D

Bloomfield, H. C., Brayshaw, D. J., Shaffrey, L. C., Coker, P. J. & Thornton, H. E. Quan- tifying the increasing sensitivity of power systems to climate variability.Environ- mental Research Letters11,124025. doi:10 . 1088 / 1748 - 9326 / 11 / 12 / 124025. (2016)

work page 2016
[19]

T., Wohland, J., Stoop, L

Craig, M. T., Wohland, J., Stoop, L. P., Kies, A., Pickering, B., Bloomfield, H. C., Brow- ell, J., De Felice, M., Dent, C. J., Deroubaix, A., Frischmuth, F., Gonzalez, P. L., Gro- chowicz, A., Gruber, K., Härtel, P., Kittel, M., Kotzur, L., Labuhn, I., Lundquist, J. K., Pflugradt, N., Van Der Wiel, K., Zeyringer, M. & Brayshaw, D. J. Overcoming the dis- ...

work page doi:10.1016/j.joule.2022.05.010 2022
[20]

Van Duinen, B., van der Most, L., Baatsen, M. L. J. & van der Wiel, K. Meteorological drivers of co-occurring renewable energy droughts in Europe.Renewable and Sustainable Energy Reviews223,115993. doi:10 . 1016 / j . rser . 2025 . 115993. (2025)

work page 2025
[21]

Nik, V. M. Making energy simulation easier for future climate – Synthesizing typ- ical and extreme weather data sets out of regional climate models (RCMs).Ap- plied Energy177,204–226. doi:10.1016/j.apenergy.2016.05.107. (2016)

work page doi:10.1016/j.apenergy.2016.05.107 2016
[22]

& Maerefat, M

Ebrahimpour, A. & Maerefat, M. A method for generation of typical meteorolog- ical year.Energy Conversion and Management51,410–417. doi:10 . 1016 / j . enconman.2009.10.002. (2010)

work page 2009
[23]

D., Psiloglou, B

Kambezidis, H. D., Psiloglou, B. E., Kaskaoutis, D. G., Karagiannis, D., Petrinoli, K., Gavriil, A. & Kavadias, K. Generation of typical meteorological years for 33 loca- tions in Greece: adaptation to the needs of various applications.Theoretical and Applied Climatology141,1313–1330. doi:10.1007/s00704-020-03264-7. (2020)

work page doi:10.1007/s00704-020-03264-7 2020
[24]

P., Brayshaw, D

Hilbers, A. P., Brayshaw, D. J. & Gandy, A. Importance subsampling: improving power system planning under climate-based uncertainty.Applied Energy251, 113114. doi:10.1016/j.apenergy.2019.04.110. (2019)

work page doi:10.1016/j.apenergy.2019.04.110 2019
[25]

& Knopf, B

Nahmmacher, P., Schmid, E., Hirth, L. & Knopf, B. Carpe diem: A novel approach to select representative days for long-term power system modeling.Energy112, 430–442. doi:10.1016/j.energy.2016.06.081. (2016)

work page doi:10.1016/j.energy.2016.06.081 2016
[26]

& Brandt, A

Teichgraeber, H. & Brandt, A. R. Clustering methods to find representative peri- ods for the optimization of energy systems: An initial framework and compar- ison.Applied Energy239,1283–1293. doi:10 . 1016 / j . apenergy . 2019 . 02 . 012. (2019)

work page 2019
[27]

& Robinius, M

Hoffmann, M., Kotzur, L., Stolten, D. & Robinius, M. A Review on Time Series Ag- gregation Methods for Energy System Models.Energies13,641. doi:10.3390/ en13030641. (2020)

work page 2020
[28]

Hoffman, K. L. & Padberg, M. inEncyclopedia of Operations Research and Man- agement Science(eds Gass, S. I. & Harris, C. M.) 849–853 (Springer US, New York, NY, 2001). doi:10.1007/1-4020-0611-X_1068

work page doi:10.1007/1-4020-0611-x_1068 2001
[29]

7th International Conference Energy & Meteorology (ICEM) (2023)

Thorey, J.Sampling representative years for a TSO in a climate simulation of 200 yearsin. 7th International Conference Energy & Meteorology (ICEM) (2023)

work page 2023
[30]

Van Laarhoven, P. J. M. & Aarts, E. H. L.Simulated Annealing: Theory and Ap- plicationsdoi:10 . 1007 / 978 - 94 - 015 - 7744 - 1. (Springer Netherlands, Dordrecht, 1987)

work page 1987
[31]

F., Overbeek, B., Reerink, T., Selten, F., Severijns, C., Siegmund, P., Sterl, A., de Valk, C., van Velthoven, P., de Vries, H., van Weele, M

Van Dorland, R., Beersma, J., Bessembinder, J., Bloemendaal, N., Drijfhout, S., Groenland, R., Haarsma, R., Homan, C., Keizer, I., Krikken, F., van Meijgaard, E., Meirink, J. F., Overbeek, B., Reerink, T., Selten, F., Severijns, C., Siegmund, P., Sterl, A., de Valk, C., van Velthoven, P., de Vries, H., van Weele, M. & Schreur, B. W.KNMI National Climate S...

work page 2023
[32]

The Wasserstein distances, pages 93--111

Villani, C. inOptimal Transport: Old and New(ed Villani, C.) 93–111 (Springer, Berlin, Heidelberg, 2009). doi:10.1007/978-3-540-71050-9_6

work page doi:10.1007/978-3-540-71050-9_6 2009
[33]

& Poggio, T

Frogner, C., Zhang, C., Mobahi, H., Araya, M. & Poggio, T. A.Learning with a Wasser- stein LossinAdvances in Neural Information Processing Systems28(Curran Associates, Inc., 2015).https : / / proceedings . neurips . cc / paper _ files / paper / 2015/hash/a9eb812238f753132652ae09963a05e9-Abstract.html. REFERENCES 20

work page 2015
[34]

Kolouri, S., Nadjahi, K., Simsekli, U., Badeau, R. & Rohde, G.Generalized Sliced Wasserstein DistancesinAdvances in Neural Information Processing Systems 32(Curran Associates, Inc., 2019).https://proceedings.neurips.cc/paper_files/ paper/2019/hash/f0935e4cd5920aa6c7c996a5ee53a70f-Abstract.html

work page 2019
[35]

& Siddiqui, A

Condeixa, L., Oliveira, F. & Siddiqui, A. S.Wasserstein-Distance-Based Temporal Clustering for Capacity-Expansion Planning in Power Systemsin2020 Inter- national Conference on Smart Energy Systems and Technologies (SEST)(IEEE, Istanbul, Turkey, 2020), 1–6. doi:10.1109/SEST48500.2020.9203449

work page doi:10.1109/sest48500.2020.9203449 2020
[36]

M., Guibas, L

Solomon, J., Rustamov, R. M., Guibas, L. & Butscher, A.Wasserstein Propagation for Semi-Supervised LearninginProceedings of the 31st International Confer- ence on Machine Learning32(PMLR, 2014), 306–314.https://proceedings. mlr.press/v32/solomon14.html

work page 2014
[37]

& Karagöz, Y

Pusat, S. & Karagöz, Y. A new reference wind year approach to estimate long term wind characteristics.Advances in Mechanical Engineering13,16878140211021268. doi:10.1177/16878140211021268. (2021)

work page doi:10.1177/16878140211021268 2021
[38]

& Bosshard, T

Pechlivanidis, I., Gupta, H. & Bosshard, T. An Information Theory Approach to Identifying a Representative Subset of Hydro-Climatic Simulations for Impact Modeling Studies.Water Resources Research54,5422–5435. doi:10 . 1029 / 2017WR022035. (2018)

work page 2018
[39]

Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2024

Copernicus Climate Change Service.Climate and energy related variables from the Pan-European Climate Database derived from reanalysis and climate pro- jectionsversion 4.2. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2024. doi:10.24381/cds.f323c5ec

work page doi:10.24381/cds.f323c5ec 2024
[40]

J., Huertas-Hernando, D., Radu, D., Sharp, J., Zappa, W

Dubus, L., Brayshaw, D. J., Huertas-Hernando, D., Radu, D., Sharp, J., Zappa, W. & Stoop, L. P. Towards a future-proof climate database for European energy sys- tem studies.Environmental Research Letters17,121001. doi:10 . 1088 / 1748 - 9326/aca1d3. (2022)

work page 2022
[41]

T., Mur- cia, J

Koivisto, M., Kanellas, P., Troccoli, A., Aldrigo, G., Amaro e Silva, R., Olsen, B. T., Mur- cia, J. P., Angeloni, D., Borga, M., Campostrini, S., Cordeddu, S., Lusito, L., Saint- Drenan, Y. .-., Restivo, E. & Zaramella, M.Developing support service to ENTSO-E: including the impacts of climate change in the Pan-European Climate Database (PECD)in22nd Wind ...

work page arXiv 2023
[42]

P., Olsen, B

Nayak, S., Simutis, E., Murcia Leon, J. P., Olsen, B. T. & Koivisto, M. J. Validation of European wind generation time series simulation: Importance of wakes, micro- scale adjustments and stochastic simulations.Applied Energy402,126882. doi:10.1016/j.apenergy.2025.126882. (2025). 42.Regions in the European Union Nomenclature of territorial units for stati...

work page doi:10.1016/j.apenergy.2025.126882 2025
[43]

Sohoni, V., Gupta, S. C. & Nema, R. K. A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Sys- tems.Journal of Energy2016,8519785. doi:10.1155/2016/8519785. (2016)

work page doi:10.1155/2016/8519785 2016
[44]

& Fouquau, J

Bessec, M. & Fouquau, J. The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach.Energy Economics30, 2705–2721. doi:10.1016/j.eneco.2008.02.003. (2008). REFERENCES 21

work page doi:10.1016/j.eneco.2008.02.003 2008
[45]

& Bintanja, R

Van Der Most, L., Van Der Wiel, K., Benders, R., Gerbens-Leenes, P., Kerkmans, P. & Bintanja, R. Extreme events in the European renewable power system: Vali- dation of a modeling framework to estimate renewable electricity production and demand from meteorological data.Renewable and Sustainable Energy Reviews170,112987. doi:10.1016/j.rser.2022.112987. (2022)

work page doi:10.1016/j.rser.2022.112987 2022
[46]

J., Robin, Y

François, B., Vrac, M., Cannon, A. J., Robin, Y. & Allard, D. Multivariate bias cor- rections of climate simulations: which benefits for which losses?Earth System Dynamics11,537–562. doi:10.5194/esd-11-537-2020. (2020)

work page doi:10.5194/esd-11-537-2020 2020
[47]

& Ghil, M

Vissio, G., Lembo, V., Lucarini, V. & Ghil, M. Evaluating the Performance of Cli- mate Models Based on Wasserstein Distance.Geophysical Research Letters47, e2020GL089385. doi:10.1029/2020GL089385. (2020)

work page doi:10.1029/2020gl089385 2020
[48]

N., Sile, T., Witha, B., Davis, N

Hahmann, A. N., Sile, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García- Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T. & Söderberg, S. The making of the New European Wind Atlas – Part 1: Model sensitivity.Geoscientific Model Development13,5053–5078. doi:10.5194/gmd-13-5053-2020. (2020)

work page doi:10.5194/gmd-13-5053-2020 2020
[49]

& Pfister, H

Bonneel, N., Rabin, J., Peyré, G. & Pfister, H. Sliced and Radon Wasserstein Barycen- ters of Measures.Journal of Mathematical Imaging and Vision51,22–45. doi:10. 1007/s10851-014-0506-3. (2015)

work page 2015
[50]

& Sriver, R.Quantifying uncertainty in climate projections with confor- mal ensemblesdoi:10.48550/arXiv.2408.06642(2024)

Harris, T. & Sriver, R.Quantifying uncertainty in climate projections with confor- mal ensemblesdoi:10.48550/arXiv.2408.06642(2024)

work page doi:10.48550/arxiv.2408.06642(2024 2024
[51]

& Fernandes Montesuma, E.POT python optimal transport (version 0.9.5)2024

Flamary, R., Vincent-Cuaz, C., Courty, N., Gramfort, A., Kachaiev, O., Quang Tran, H., David, L., Bonet, C., Cassereau, N., Gnassounou, T., Tanguy, E., Delon, J., Collas, A., Mazelet, S., Chapel, L., Kerdoncuff, T., Yu, X., Feickert, M., Krzakala, P., Liu, T. & Fernandes Montesuma, E.POT python optimal transport (version 0.9.5)2024. https://github.com/Pyt...

work page 2024
[52]

Székely, G. J. & Rizzo, M. L. Energy statistics: A class of statistics based on dis- tances.Journal of Statistical Planning and Inference143,1249–1272. doi:10. 1016/j.jspi.2013.03.018. (2013)

work page 2013
[53]

Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Optimization by Simulated Annealing. Science220,671–680. doi:10.1126/science.220.4598.671. (1983)

work page doi:10.1126/science.220.4598.671 1983
[54]

& Doorman, G

Wolfgang, O., Haugstad, A., Mo, B., Gjelsvik, A., Wangensteen, I. & Doorman, G. Hydro reservoir handling in Norway before and after deregulation.Energy. 11th Conference on Process Integration, Modelling and Optimisation for Energy Sav- ing and Pollution Reduction34,1642–1651. doi:10.1016/j.energy.2009.07.025. (2009)

work page doi:10.1016/j.energy.2009.07.025 2009
[55]

M., Bintanja, R., Blackport, R

Van Der Wiel, K., Selten, F. M., Bintanja, R., Blackport, R. & Screen, J. A. Ensem- ble climate-impact modelling: extreme impacts from moderate meteorologi- cal conditions.Environmental Research Letters15,034050. doi:10.1088/1748- 9326/ab7668(2020)

work page doi:10.1088/1748- 2020
[56]

& Schlachtberger, D

Brown, T., Hörsch, J. & Schlachtberger, D. PyPSA: Python for Power System Anal- ysis.Journal of Open Research Software6.doi:10.5334/jors.188. (2018)

work page doi:10.5334/jors.188 2018
[57]

Ruane, A. C. & McDermid, S. P. Selection of a representative subset of global climate models that captures the profile of regional changes for integrated cli- mate impacts assessment.Earth Perspectives4,1. doi:10 . 1186 / s40322 - 017 - 0036-4. (2017). A ILLUSTRATION OF (SEASONAL) SLICED-WASSERSTEIN DISTANCE METRIC22 SUPPLEMENTARY INFORMATION A ILLUSTRATI...

work page arXiv 2017

[1] [1]

Intergovernmental Panel on Climate Change (IPCC). inClimate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth As- sessment Report of the Intergovernmental Panel on Climate Change(ed Inter- governmental Panel on Climate Change (IPCC)) 613–746 (Cambridge Univer- sity Press, Cambridge, 2023). doi:10.1017/9781009157926.008

work page doi:10.1017/9781009157926.008 2022

[2] [2]

H., Virgüez, E., Reich, N., Dowling, J., Bloomfield, H., Antonini, E

Ruggles, T. H., Virgüez, E., Reich, N., Dowling, J., Bloomfield, H., Antonini, E. G. A., Davis, S. J., Lewis, N. S. & Caldeira, K. Planning reliable wind- and solar-based electricity systems.Advances in Applied Energy15,100185. doi:10 . 1016 / j . adapen.2024.100185. (2024)

work page arXiv 2024

[3] [3]

Grochowicz, A., van Greevenbroek, K., Benth, F. E. & Zeyringer, M. Intersecting near-optimal spaces: European power systems with more resilience to weather variability.Energy Economics118,106496. doi:10.1016/j.eneco.2022.106496. (2023)

work page doi:10.1016/j.eneco.2022.106496 2022

[4] [4]

K., Andresen, G

Gøtske, E. K., Andresen, G. B., Neumann, F. & Victoria, M. Designing a sector- coupled European energy system robust to 60 years of historical weather data. Nature Communications15,10680. doi:10.1038/s41467-024-54853-3. (2024)

work page doi:10.1038/s41467-024-54853-3 2024

[5] [5]

Pecora, B., Rhodes, J. D. & Webber, M. E. Quantifying the impacts of weather year selection on power sector capacity expansion models.Energy340,138979. doi:10.1016/j.energy.2025.138979. (2025)

work page doi:10.1016/j.energy.2025.138979 2025

[6] [6]

Pfenninger, S. Dealing with multiple decades of hourly wind and PV time se- ries in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability.Applied Energy197,1–13. doi:10.1016/j.apenergy.2017.03.051. (2017). REFERENCES 18

work page doi:10.1016/j.apenergy.2017.03.051 2017

[7] [7]

Kelder, T., Heinrich, D., Klok, L., Thompson, V., Goulart, H. M. D., Hawkins, E., Slater, L. J., Suarez-Gutierrez, L., Wilby, R. L., Coughlan de Perez, E., Stephens, E. M., Burt, S., van den Hurk, B., de Vries, H., van der Wiel, K., Schipper, E. L. F., Carmona Baéz, A., van Bueren, E. & Fischer, E. M. How to stop being surprised by unprecedented weather.N...

work page doi:10.1038/s41467- 2025

[8] [8]

& Odenberger, M

Öberg, S., Johnsson, F. & Odenberger, M. The impact of inter-annual weather variations on energy storage and flexible generation – a UK case study.Energy 335,137780. doi:10.1016/j.energy.2025.137780. (2025)

work page doi:10.1016/j.energy.2025.137780 2025

[9] [9]

S., Psarros, G

Chatzistylianos, E. S., Psarros, G. N., Dratsas, P. A. & Papathanassiou, S. A. Assess- ing the Impact of Climate Conditions Within CEP Models: A Comparative Study of Scenario Reduction Methods.IEEE Access14,37245–37264. doi:10.1109/ ACCESS.2026.3671115. (2026)

work page arXiv 2026

[10] [10]

ENTSO-E.TYNDP 2024 Scenarios - Methodology Report(ENTSO-E, Brussels, 2025).https://2024.entsos-tyndp-scenarios.eu/wp-content/uploads/2025/01/ TYNDP_2024_Scenarios_Methodology_Report_Final_Version_250128.pdf

work page 2024

[11] [11]

ENTSO-E.European Resource Adequacy Assessment 2025(ENTSO-E, Brussels, 2025).https://eepublicdownloads.blob.core.windows.net/public-cdn-container/ clean-documents/sdc-documents/ERAA/ERAA_2025_ExecutiveReport_ENTSOEProposal_ Dec2025.pdf

work page 2025

[12] [12]

Suarez-Gutierrez, L., Li, C., Müller, W. A. & Marotzke, J. Internal variability in Euro- pean summer temperatures at 1.5°C and 2°C of global warming.Environmen- tal Research Letters13,064026. doi:10.1088/1748-9326/aaba58. (2018)

work page doi:10.1088/1748-9326/aaba58 2018

[13] [13]

Van Der Wiel, K., Wanders, N., Selten, F. M. & Bierkens, M. F. P. Added Value of Large Ensemble Simulations for Assessing Extreme River Discharge in a 2°C Warmer World.Geophysical Research Letters46,2093–2102. doi:10 . 1029 / 2019GL081967. (2019)

work page 2093

[14] [14]

& Selten, F

Van Der Wiel, K., Stoop, L., Van Zuijlen, B., Blackport, R., Van Den Broek, M. & Selten, F. Meteorological conditions leading to extreme low variable renewable energy production and extreme high energy shortfall.Renewable and Sustain- able Energy Reviews111,261–275. doi:10.1016/j.rser.2019.04.065. (2019)

work page doi:10.1016/j.rser.2019.04.065 2019

[15] [15]

& Zscheis- chler, J

Bevacqua, E., Suarez-Gutierrez, L., Jézéquel, A., Lehner, F., Vrac, M., Yiou, P. & Zscheis- chler, J. Advancing research on compound weather and climate events via large ensemble model simulations.Nature Communications14,2145. doi:10.1038/ s41467-023-37847-5. (2023)

work page 2023

[16] [16]

& Van Der Wiel, K

Muntjewerf, L., Bintanja, R., Reerink, T. & Van Der Wiel, K. The KNMI Large En- semble Time Slice (KNMI–LENTIS).Geoscientific Model Development16,4581–

work page

[17] [17]

doi:10.5194/gmd-16-4581-2023. (2023)

work page doi:10.5194/gmd-16-4581-2023 2023

[18] [18]

C., Brayshaw, D

Bloomfield, H. C., Brayshaw, D. J., Shaffrey, L. C., Coker, P. J. & Thornton, H. E. Quan- tifying the increasing sensitivity of power systems to climate variability.Environ- mental Research Letters11,124025. doi:10 . 1088 / 1748 - 9326 / 11 / 12 / 124025. (2016)

work page 2016

[19] [19]

T., Wohland, J., Stoop, L

Craig, M. T., Wohland, J., Stoop, L. P., Kies, A., Pickering, B., Bloomfield, H. C., Brow- ell, J., De Felice, M., Dent, C. J., Deroubaix, A., Frischmuth, F., Gonzalez, P. L., Gro- chowicz, A., Gruber, K., Härtel, P., Kittel, M., Kotzur, L., Labuhn, I., Lundquist, J. K., Pflugradt, N., Van Der Wiel, K., Zeyringer, M. & Brayshaw, D. J. Overcoming the dis- ...

work page doi:10.1016/j.joule.2022.05.010 2022

[20] [20]

Van Duinen, B., van der Most, L., Baatsen, M. L. J. & van der Wiel, K. Meteorological drivers of co-occurring renewable energy droughts in Europe.Renewable and Sustainable Energy Reviews223,115993. doi:10 . 1016 / j . rser . 2025 . 115993. (2025)

work page 2025

[21] [21]

Nik, V. M. Making energy simulation easier for future climate – Synthesizing typ- ical and extreme weather data sets out of regional climate models (RCMs).Ap- plied Energy177,204–226. doi:10.1016/j.apenergy.2016.05.107. (2016)

work page doi:10.1016/j.apenergy.2016.05.107 2016

[22] [22]

& Maerefat, M

Ebrahimpour, A. & Maerefat, M. A method for generation of typical meteorolog- ical year.Energy Conversion and Management51,410–417. doi:10 . 1016 / j . enconman.2009.10.002. (2010)

work page 2009

[23] [23]

D., Psiloglou, B

Kambezidis, H. D., Psiloglou, B. E., Kaskaoutis, D. G., Karagiannis, D., Petrinoli, K., Gavriil, A. & Kavadias, K. Generation of typical meteorological years for 33 loca- tions in Greece: adaptation to the needs of various applications.Theoretical and Applied Climatology141,1313–1330. doi:10.1007/s00704-020-03264-7. (2020)

work page doi:10.1007/s00704-020-03264-7 2020

[24] [24]

P., Brayshaw, D

Hilbers, A. P., Brayshaw, D. J. & Gandy, A. Importance subsampling: improving power system planning under climate-based uncertainty.Applied Energy251, 113114. doi:10.1016/j.apenergy.2019.04.110. (2019)

work page doi:10.1016/j.apenergy.2019.04.110 2019

[25] [25]

& Knopf, B

Nahmmacher, P., Schmid, E., Hirth, L. & Knopf, B. Carpe diem: A novel approach to select representative days for long-term power system modeling.Energy112, 430–442. doi:10.1016/j.energy.2016.06.081. (2016)

work page doi:10.1016/j.energy.2016.06.081 2016

[26] [26]

& Brandt, A

Teichgraeber, H. & Brandt, A. R. Clustering methods to find representative peri- ods for the optimization of energy systems: An initial framework and compar- ison.Applied Energy239,1283–1293. doi:10 . 1016 / j . apenergy . 2019 . 02 . 012. (2019)

work page 2019

[27] [27]

& Robinius, M

Hoffmann, M., Kotzur, L., Stolten, D. & Robinius, M. A Review on Time Series Ag- gregation Methods for Energy System Models.Energies13,641. doi:10.3390/ en13030641. (2020)

work page 2020

[28] [28]

Hoffman, K. L. & Padberg, M. inEncyclopedia of Operations Research and Man- agement Science(eds Gass, S. I. & Harris, C. M.) 849–853 (Springer US, New York, NY, 2001). doi:10.1007/1-4020-0611-X_1068

work page doi:10.1007/1-4020-0611-x_1068 2001

[29] [29]

7th International Conference Energy & Meteorology (ICEM) (2023)

Thorey, J.Sampling representative years for a TSO in a climate simulation of 200 yearsin. 7th International Conference Energy & Meteorology (ICEM) (2023)

work page 2023

[30] [30]

Van Laarhoven, P. J. M. & Aarts, E. H. L.Simulated Annealing: Theory and Ap- plicationsdoi:10 . 1007 / 978 - 94 - 015 - 7744 - 1. (Springer Netherlands, Dordrecht, 1987)

work page 1987

[31] [31]

F., Overbeek, B., Reerink, T., Selten, F., Severijns, C., Siegmund, P., Sterl, A., de Valk, C., van Velthoven, P., de Vries, H., van Weele, M

Van Dorland, R., Beersma, J., Bessembinder, J., Bloemendaal, N., Drijfhout, S., Groenland, R., Haarsma, R., Homan, C., Keizer, I., Krikken, F., van Meijgaard, E., Meirink, J. F., Overbeek, B., Reerink, T., Selten, F., Severijns, C., Siegmund, P., Sterl, A., de Valk, C., van Velthoven, P., de Vries, H., van Weele, M. & Schreur, B. W.KNMI National Climate S...

work page 2023

[32] [32]

The Wasserstein distances, pages 93--111

Villani, C. inOptimal Transport: Old and New(ed Villani, C.) 93–111 (Springer, Berlin, Heidelberg, 2009). doi:10.1007/978-3-540-71050-9_6

work page doi:10.1007/978-3-540-71050-9_6 2009

[33] [33]

& Poggio, T

Frogner, C., Zhang, C., Mobahi, H., Araya, M. & Poggio, T. A.Learning with a Wasser- stein LossinAdvances in Neural Information Processing Systems28(Curran Associates, Inc., 2015).https : / / proceedings . neurips . cc / paper _ files / paper / 2015/hash/a9eb812238f753132652ae09963a05e9-Abstract.html. REFERENCES 20

work page 2015

[34] [34]

Kolouri, S., Nadjahi, K., Simsekli, U., Badeau, R. & Rohde, G.Generalized Sliced Wasserstein DistancesinAdvances in Neural Information Processing Systems 32(Curran Associates, Inc., 2019).https://proceedings.neurips.cc/paper_files/ paper/2019/hash/f0935e4cd5920aa6c7c996a5ee53a70f-Abstract.html

work page 2019

[35] [35]

& Siddiqui, A

Condeixa, L., Oliveira, F. & Siddiqui, A. S.Wasserstein-Distance-Based Temporal Clustering for Capacity-Expansion Planning in Power Systemsin2020 Inter- national Conference on Smart Energy Systems and Technologies (SEST)(IEEE, Istanbul, Turkey, 2020), 1–6. doi:10.1109/SEST48500.2020.9203449

work page doi:10.1109/sest48500.2020.9203449 2020

[36] [36]

M., Guibas, L

Solomon, J., Rustamov, R. M., Guibas, L. & Butscher, A.Wasserstein Propagation for Semi-Supervised LearninginProceedings of the 31st International Confer- ence on Machine Learning32(PMLR, 2014), 306–314.https://proceedings. mlr.press/v32/solomon14.html

work page 2014

[37] [37]

& Karagöz, Y

Pusat, S. & Karagöz, Y. A new reference wind year approach to estimate long term wind characteristics.Advances in Mechanical Engineering13,16878140211021268. doi:10.1177/16878140211021268. (2021)

work page doi:10.1177/16878140211021268 2021

[38] [38]

& Bosshard, T

Pechlivanidis, I., Gupta, H. & Bosshard, T. An Information Theory Approach to Identifying a Representative Subset of Hydro-Climatic Simulations for Impact Modeling Studies.Water Resources Research54,5422–5435. doi:10 . 1029 / 2017WR022035. (2018)

work page 2018

[39] [39]

Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2024

Copernicus Climate Change Service.Climate and energy related variables from the Pan-European Climate Database derived from reanalysis and climate pro- jectionsversion 4.2. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2024. doi:10.24381/cds.f323c5ec

work page doi:10.24381/cds.f323c5ec 2024

[40] [40]

J., Huertas-Hernando, D., Radu, D., Sharp, J., Zappa, W

Dubus, L., Brayshaw, D. J., Huertas-Hernando, D., Radu, D., Sharp, J., Zappa, W. & Stoop, L. P. Towards a future-proof climate database for European energy sys- tem studies.Environmental Research Letters17,121001. doi:10 . 1088 / 1748 - 9326/aca1d3. (2022)

work page 2022

[41] [41]

T., Mur- cia, J

Koivisto, M., Kanellas, P., Troccoli, A., Aldrigo, G., Amaro e Silva, R., Olsen, B. T., Mur- cia, J. P., Angeloni, D., Borga, M., Campostrini, S., Cordeddu, S., Lusito, L., Saint- Drenan, Y. .-., Restivo, E. & Zaramella, M.Developing support service to ENTSO-E: including the impacts of climate change in the Pan-European Climate Database (PECD)in22nd Wind ...

work page arXiv 2023

[42] [42]

P., Olsen, B

Nayak, S., Simutis, E., Murcia Leon, J. P., Olsen, B. T. & Koivisto, M. J. Validation of European wind generation time series simulation: Importance of wakes, micro- scale adjustments and stochastic simulations.Applied Energy402,126882. doi:10.1016/j.apenergy.2025.126882. (2025). 42.Regions in the European Union Nomenclature of territorial units for stati...

work page doi:10.1016/j.apenergy.2025.126882 2025

[43] [43]

Sohoni, V., Gupta, S. C. & Nema, R. K. A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Sys- tems.Journal of Energy2016,8519785. doi:10.1155/2016/8519785. (2016)

work page doi:10.1155/2016/8519785 2016

[44] [44]

& Fouquau, J

Bessec, M. & Fouquau, J. The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach.Energy Economics30, 2705–2721. doi:10.1016/j.eneco.2008.02.003. (2008). REFERENCES 21

work page doi:10.1016/j.eneco.2008.02.003 2008

[45] [45]

& Bintanja, R

Van Der Most, L., Van Der Wiel, K., Benders, R., Gerbens-Leenes, P., Kerkmans, P. & Bintanja, R. Extreme events in the European renewable power system: Vali- dation of a modeling framework to estimate renewable electricity production and demand from meteorological data.Renewable and Sustainable Energy Reviews170,112987. doi:10.1016/j.rser.2022.112987. (2022)

work page doi:10.1016/j.rser.2022.112987 2022

[46] [46]

J., Robin, Y

François, B., Vrac, M., Cannon, A. J., Robin, Y. & Allard, D. Multivariate bias cor- rections of climate simulations: which benefits for which losses?Earth System Dynamics11,537–562. doi:10.5194/esd-11-537-2020. (2020)

work page doi:10.5194/esd-11-537-2020 2020

[47] [47]

& Ghil, M

Vissio, G., Lembo, V., Lucarini, V. & Ghil, M. Evaluating the Performance of Cli- mate Models Based on Wasserstein Distance.Geophysical Research Letters47, e2020GL089385. doi:10.1029/2020GL089385. (2020)

work page doi:10.1029/2020gl089385 2020

[48] [48]

N., Sile, T., Witha, B., Davis, N

Hahmann, A. N., Sile, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García- Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T. & Söderberg, S. The making of the New European Wind Atlas – Part 1: Model sensitivity.Geoscientific Model Development13,5053–5078. doi:10.5194/gmd-13-5053-2020. (2020)

work page doi:10.5194/gmd-13-5053-2020 2020

[49] [49]

& Pfister, H

Bonneel, N., Rabin, J., Peyré, G. & Pfister, H. Sliced and Radon Wasserstein Barycen- ters of Measures.Journal of Mathematical Imaging and Vision51,22–45. doi:10. 1007/s10851-014-0506-3. (2015)

work page 2015

[50] [50]

& Sriver, R.Quantifying uncertainty in climate projections with confor- mal ensemblesdoi:10.48550/arXiv.2408.06642(2024)

Harris, T. & Sriver, R.Quantifying uncertainty in climate projections with confor- mal ensemblesdoi:10.48550/arXiv.2408.06642(2024)

work page doi:10.48550/arxiv.2408.06642(2024 2024

[51] [51]

& Fernandes Montesuma, E.POT python optimal transport (version 0.9.5)2024

Flamary, R., Vincent-Cuaz, C., Courty, N., Gramfort, A., Kachaiev, O., Quang Tran, H., David, L., Bonet, C., Cassereau, N., Gnassounou, T., Tanguy, E., Delon, J., Collas, A., Mazelet, S., Chapel, L., Kerdoncuff, T., Yu, X., Feickert, M., Krzakala, P., Liu, T. & Fernandes Montesuma, E.POT python optimal transport (version 0.9.5)2024. https://github.com/Pyt...

work page 2024

[52] [52]

Székely, G. J. & Rizzo, M. L. Energy statistics: A class of statistics based on dis- tances.Journal of Statistical Planning and Inference143,1249–1272. doi:10. 1016/j.jspi.2013.03.018. (2013)

work page 2013

[53] [53]

Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Optimization by Simulated Annealing. Science220,671–680. doi:10.1126/science.220.4598.671. (1983)

work page doi:10.1126/science.220.4598.671 1983

[54] [54]

& Doorman, G

Wolfgang, O., Haugstad, A., Mo, B., Gjelsvik, A., Wangensteen, I. & Doorman, G. Hydro reservoir handling in Norway before and after deregulation.Energy. 11th Conference on Process Integration, Modelling and Optimisation for Energy Sav- ing and Pollution Reduction34,1642–1651. doi:10.1016/j.energy.2009.07.025. (2009)

work page doi:10.1016/j.energy.2009.07.025 2009

[55] [55]

M., Bintanja, R., Blackport, R

Van Der Wiel, K., Selten, F. M., Bintanja, R., Blackport, R. & Screen, J. A. Ensem- ble climate-impact modelling: extreme impacts from moderate meteorologi- cal conditions.Environmental Research Letters15,034050. doi:10.1088/1748- 9326/ab7668(2020)

work page doi:10.1088/1748- 2020

[56] [56]

& Schlachtberger, D

Brown, T., Hörsch, J. & Schlachtberger, D. PyPSA: Python for Power System Anal- ysis.Journal of Open Research Software6.doi:10.5334/jors.188. (2018)

work page doi:10.5334/jors.188 2018

[57] [57]

Ruane, A. C. & McDermid, S. P. Selection of a representative subset of global climate models that captures the profile of regional changes for integrated cli- mate impacts assessment.Earth Perspectives4,1. doi:10 . 1186 / s40322 - 017 - 0036-4. (2017). A ILLUSTRATION OF (SEASONAL) SLICED-WASSERSTEIN DISTANCE METRIC22 SUPPLEMENTARY INFORMATION A ILLUSTRATI...

work page arXiv 2017