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A deep learning approach to renewable capacity installation under jump uncertainty

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  • Agram, Nacira
  • Benth, Fred Espen
  • Pucci, Giulia
  • Rems, Jan

Abstract

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.

Suggested Citation

  • Agram, Nacira & Benth, Fred Espen & Pucci, Giulia & Rems, Jan, 2026. "A deep learning approach to renewable capacity installation under jump uncertainty," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 250(C), pages 152-177.
    • Handle: RePEc:eee:matcom:v:250:y:2026:i:c:p:152-177
    DOI: 10.1016/j.matcom.2026.06.024
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