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Scenario generation by selection from historical data

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  • Michal Kaut

    (SINTEF)

Abstract

In this paper, we present and compare several methods for generating scenarios for stochastic-programming models by direct selection from historical data. The methods range from standard sampling and k-means, through iterative sampling-based selection methods, to a new moment-based optimization approach. We compare the models on a simple portfolio-optimization model and show how to use them in a situation when we are selecting whole sequences from the data, instead of single data points.

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  • Michal Kaut, 2021. "Scenario generation by selection from historical data," Computational Management Science, Springer, vol. 18(3), pages 411-429, July.
    • Handle: RePEc:spr:comgts:v:18:y:2021:i:3:d:10.1007_s10287-021-00399-4
    DOI: 10.1007/s10287-021-00399-4
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    References listed on IDEAS

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    1. Georg Ch. Pflug & Alois Pichler, 2011. "Approximations for Probability Distributions and Stochastic Optimization Problems," International Series in Operations Research & Management Science, in: Marida Bertocchi & Giorgio Consigli & Michael A. H. Dempster (ed.), Stochastic Optimization Methods in Finance and Energy, edition 1, chapter 0, pages 343-387, Springer.
    2. Francisco Munoz & Jean-Paul Watson, 2015. "A scalable solution framework for stochastic transmission and generation planning problems," Computational Management Science, Springer, vol. 12(4), pages 491-518, October.
    3. Kjetil Høyland & Stein W. Wallace, 2001. "Generating Scenario Trees for Multistage Decision Problems," Management Science, INFORMS, vol. 47(2), pages 295-307, February.
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    Cited by:

    1. Firehiwot Girma Dires & Mikael Amelin & Getachew Bekele, 2023. "Long-Term Hydropower Planning for Ethiopia: A Rolling Horizon Stochastic Programming Approach with Uncertain Inflow," Energies, MDPI, vol. 16(21), pages 1-15, November.
    2. Tiong, Achara & Vergara, Hector A., 2023. "Evaluation of network expansion decisions for resilient interdependent critical infrastructures with different topologies," International Journal of Critical Infrastructure Protection, Elsevier, vol. 42(C).
    3. Tristan Rigaut & Pierre Carpentier & Jean-Philippe Chancelier & Michel Lara, 2024. "Decomposition methods for monotone two-time-scale stochastic optimization problems," Computational Management Science, Springer, vol. 21(1), pages 1-37, June.
    4. Daniel Fernández Valderrama & Juan Ignacio Guerrero Alonso & Carlos León de Mora & Michela Robba, 2024. "Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems," Energies, MDPI, vol. 17(21), pages 1-14, October.
    5. Michal Kaut, 2024. "Handling of long-term storage in multi-horizon stochastic programs," Computational Management Science, Springer, vol. 21(1), pages 1-26, June.
    6. Olkkonen, Ville & Lind, Arne & Rosenberg, Eva & Kvalbein, Lisa, 2023. "Electrification of the agricultural sector in Norway in an effort to phase out fossil fuel consumption," Energy, Elsevier, vol. 276(C).

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