Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/129644
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Type: Journal article
Title: An active learning approach for identifying the smallest subset of informative scenarios for robust planning under deep uncertainty
Author: Giudici, F.
Castelletti, A.
Giuliani, M.
Maier, H.R.
Citation: Environmental Modelling and Software, 2020; 127:1-13
Publisher: Elsevier
Issue Date: 2020
ISSN: 1364-8152
1873-6726
Statement of
Responsibility: 
Federico Giudici, Andrea Castelletti, Matteo Giuliani, Holger R.Maier
Abstract: Deep uncertainty in future climate, socio-economic and technological conditions poses a great challenge to medium-long term decision making. Recently, several approaches have been proposed to identify solutions that are robust with respect to a large ensemble of deeply uncertain future scenarios. In this paper, we introduce ROSS (Robust Optimal Scenario Selection), a novel algorithm that uses an active learning approach for adaptively selecting the smallest scenario subset to be included into a robust optimization process. ROSS contributes a twofold novelty in the field of robust optimization under deep uncertainty. First, it allows the computational requirements for the generation of robust solutions to be considerably reduced with respect to traditional optimization methods. Second, it allows the identification of the most informative regions of the scenario set containing the scenarios to be included in the optimization process for generating a robust solution. We test ROSS on the real case study of robust planning of an off-grid hybrid energy system, combining diesel generation with renewable energy sources and storage technologies. Results show that ROSS enables computational requirements to be reduced between 23% to 84% compared with traditional robust optimization methods, depending on the complexity of the robustness metrics considered. It is also able to identify very small regions of the scenario set containing the most informative scenarios for generating a robust solution.
Keywords: Robust optimization; deep uncertainty; active learning; robust planning; hybrid energy systems
Rights: © 2020 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.envsoft.2020.104681
Published version: http://dx.doi.org/10.1016/j.envsoft.2020.104681
Appears in Collections:Aurora harvest 4
Civil and Environmental Engineering publications

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