Delayed Investment Decisions in Renewable Energy under Uncertainty: A Deep Learning–Based Approach

Insaf Agram (), Abdelhak Rais and Nacira Agram
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Insaf Agram: University of Biskra, Department of Economic Sciences
Abdelhak Rais: University of Biskra, Department of Economic Sciences
Nacira Agram: KTH Royal Institute of Technology, Department of Mathematics

A chapter in Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026), 2026, pp 430-433 from Springer

Abstract: Abstract We investigate a stochastic control problem for renewable energy capacity installation under uncertainty and implementation delay. Investment decisions are irreversible and subject to time-to-build constraints such as construction, regulatory approval, and grid integration. Electricity demand uncertainty and renewable intermittency are modeled through jump-driven stochastic dynamics, capturing both continuous fluctuations and rare extreme events. The introduction of delay induces path dependence and leads to a non-Markovian control problem. To address this challenge, we propose a deep learning-based global control framework that directly approximates optimal feedback policies from simulated trajectories. Unlike dynamic programming or BSDE-based methods, the approach avoids value function approximation and remains tractable in high-dimensional and delayed settings. Numerical experiments show that delay significantly alters optimal investment timing and induces smoother, anticipative strategies.

Keywords: Delay system; renewable energy; deep learning (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-711-8_40

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DOI: 10.2991/978-94-6239-711-8_40

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