Quantile Fourier regressions for decision making under uncertainty
Optimization and Control
2024-09-17 v1
Abstract
Weconsider Markov decision processes arising from a Markov model of an underlying natural phenomenon. Such phenomena are usually periodic (e.g. annual) in time, and so the Markov processes modelling them must be time-inhomogeneous, with cyclostationary rather than stationary behaviour. We describe a technique for constructing such processes that allows for periodic variations both in the values taken by the process and in the serial dependence structure. We include two illustrative numerical examples: a hydropower scheduling problem and a model of offshore wind power integration.
Keywords
Cite
@article{arxiv.2409.10455,
title = {Quantile Fourier regressions for decision making under uncertainty},
author = {Arash Khojaste and Geoffrey Pritchard and Golbon Zakeri},
journal= {arXiv preprint arXiv:2409.10455},
year = {2024}
}