Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84192
Title: Polyhedral predictive regions for power system applications
Authors: Golestaneh, Faranak
Pinson, Pierre
Gooi, Hoay Beng
Keywords: Box Uncertainty Sets
Engineering::Electrical and electronic engineering
Probabilistic Forecasting
Issue Date: 2018
Source: Golestaneh, F., Pinson, P., & Gooi, H. B. (2019). Polyhedral predictive regions for power system applications. IEEE Transactions on Power Systems, 34(1), 693-704. doi:10.1109/TPWRS.2018.2861705
Series/Report no.: IEEE Transactions on Power Systems
Abstract: Despite substantial improvement in the development of forecasting approaches, conditional and dynamic uncertainty estimates ought to be accommodated in decision-making in power system operation and market, in order to yield either cost-optimal decisions in expectation, or decision with probabilistic guarantees. The representation of uncertainty serves as an interface between forecasting and decision-making problems, with different approaches handling various objects and their parameterization as input. Following substantial developments based on scenario-based stochastic methods, robust and chance-constrained optimization approaches have gained increasing attention. These often rely on polyhedra as a representation of the convex envelope of uncertainty. In this paper, we aim to bridge the gap between the probabilistic forecasting literature and such optimization approaches by generating forecasts in the form of polyhedra with probabilistic guarantees. For that, we see polyhedra as parameterized objects under alternative definitions (under L1 and L∞ norms), the parameters of which may be modeled and predicted. We additionally discuss assessing the predictive skill of such multivariate probabilistic forecasts. An application and related empirical investigation results allow us to verify probabilistic calibration and predictive skills of our polyhedra.
URI: https://hdl.handle.net/10356/84192
http://hdl.handle.net/10220/50181
ISSN: 0885-8950
DOI: http://dx.doi.org/10.1109/TPWRS.2018.2861705
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TPWRS.2018.2861705.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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