Decision making with dynamic probabilistic forecasts

Peter Tankov and Laura Tinsi

Papers from arXiv.org

Abstract: We consider a sequential decision making process, such as renewable energy trading or electrical production scheduling, whose outcome depends on the future realization of a random factor, such as a meteorological variable. We assume that the decision maker disposes of a dynamically updated probabilistic forecast (predictive distribution) of the random factor. We propose several stochastic models for the evolution of the probabilistic forecast, and show how these models may be calibrated from ensemble forecasts, commonly provided by weather centers. We then show how these stochastic models can be used to determine optimal decision making strategies depending on the forecast updates. Applications to wind energy trading are given.

Date: 2021-06
New Economics Papers: this item is included in nep-cwa, nep-ene, nep-env, nep-for and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://arxiv.org/pdf/2106.16047 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2106.16047

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().