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- W2887087637 abstract "The increasing penetration of renewables in a liberalised market environment makes more and more crucial the need to evaluate the dynamic behaviour of the grid and to cope with forecast uncertainties in operational planning and quasi-online environment. The EU FP7 project iTesla addressed these needs and achieved several major objectives, including the definition of a platform architecture, a dynamic data structure, and dynamic model validation. Specifically, one of the modules of the platform runs a model to compute the multivariate distributions of renewable and load forecast errors, based on historical data series and conditioned to the specific forecast state of the grid, with the aim to sample the “uncertainty region” of states around the forecast state. However, the model performance assessment needed to be completed. Therefore, the paper focuses on the verification process of the model performances and on the potential upgrades to provide a satisfactory trade-off between accuracy and predictive capability." @default.
- W2887087637 created "2018-08-22" @default.
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- W2887087637 date "2018-06-01" @default.
- W2887087637 modified "2023-09-26" @default.
- W2887087637 title "Verification and upgrades of an advanced technique to model forecast uncertainties in large power systems" @default.
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- W2887087637 doi "https://doi.org/10.1109/pmaps.2018.8440482" @default.
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