Jan Rems: A Deep Learning Approach to Renewable Capacity Installation under Jump Uncertainty

V torek, 12. 5. 2026, ob 16:30 bo v predavalnici 3.06 v okviru seminarja VeSFiM potekalo predavanje Jana Remsa z naslovom A Deep Learning Approach to Renewable Capacity Installation under Jump Uncertainty.

Povzetek: We study a stochastic model for the installation of renewable energy capacity under demand uncertainty and jump driven dynamics. The system is governed by a multidimensional Ornstein-Uhlenbeck (OU) process driven by a subordinator, capturing abrupt variations in renewable generation and electricity load. Installation decisions are modeled through control actions that increase capacity in response to environmental and economic conditions. We consider two distinct solution approaches. First, we implement a threshold based control rule, that leads to a nonlinear partial integro-differential equation (PIDE), which we solve by reformulating it as a backward stochastic differential equation with jumps. Second, to benchmark the performance of the first approach, we use a deep control algorithm that directly learns a state and time dependent feedback threshold policy by minimizing the expected cost functional using neural networks.

Predavanje bo potekalo v živo.

Vljudno vabljeni!