PreprintarXiv licencearXiv
Data-Driven Stochastic Control of Delayed Air-Conditioning Systems
Abstract
We consider a stochastic optimal control problem arising in the operation of industrial air-conditioning systems with delayed activation effects. The delayed transition between inactive and operational regimes induces a non-Markovian structure in the controlled dynamics. We derive a regression-based discrete dynamic programming algorithm on nested state-control grids. We give a probabilistic proof of convergence of the idealized time-discrete dynamic programming problem to the continuous-time value function. Numerical experiments based on calibrated multidimensional forecast-tracking Ornstein--Uhlenbeck models illustrate the effectiveness of the proposed methodology.
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BibTeX
@misc{bayer2026datadriven,
title = {{Data-Driven Stochastic Control of Delayed Air-Conditioning Systems}},
author = {Christian Bayer and Raul Tempone and Leon Wilkosz},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35811},
eprint = {2610.35811},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35811},
}
RIS
TY - UNPB
TI - Data-Driven Stochastic Control of Delayed Air-Conditioning Systems
AU - Christian Bayer
AU - Raul Tempone
AU - Leon Wilkosz
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.35811
UR - https://arxiv.org/abs/2610.35811
AB - We consider a stochastic optimal control problem arising in the operation of industrial air-conditioning systems with delayed activation effects. The delayed transition between inactive and operational regimes induces a non-Markovian structure in the controlled dynamics. We derive a regression-based discrete dynamic programming algorithm on nested state-control grids. We give a probabilistic proof of convergence of the idealized time-discrete dynamic programming problem to the continuous-time value function. Numerical experiments based on calibrated multidimensional forecast-tracking Ornstein--Uhlenbeck models illustrate the effectiveness of the proposed methodology.
ER -