Polyhedral predictive regions for power system applications

• Main Authors: Golestaneh, Faranak, Pinson, Pierre, Gooi, Hoay Beng
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Box Uncertainty Sets; Engineering::Electrical and electronic engineering; Probabilistic Forecasting
• Online Access: https://hdl.handle.net/10356/84192; http://hdl.handle.net/10220/50181
• Institution: Nanyang Technological University

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.