Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)

International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)

📍Biskra, Algeria🗓️ 13-14 April 2026
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Delayed Investment Decisions in Renewable Energy under Uncertainty: A Deep Learning–Based Approach

Authors
Insaf Agram1, *, Abdelhak Rais1, Nacira Agram2
1Department of Economic Sciences, University of Biskra, Biskra, Algeria
2Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
*Corresponding author. Email: insaf.agram@univ-biskra.dz
Corresponding Author
Insaf Agram
Available Online 24 June 2026.
DOI
10.2991/978-94-6239-711-8_40How to use a DOI?
Keywords
Delay system; renewable energy; deep learning
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.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
Series
Advances in Economics, Business and Management Research
Publication Date
24 June 2026
ISBN
978-94-6239-711-8
ISSN
2352-5428
DOI
10.2991/978-94-6239-711-8_40How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Insaf Agram
AU  - Abdelhak Rais
AU  - Nacira Agram
PY  - 2026
DA  - 2026/06/24
TI  - Delayed Investment Decisions in Renewable Energy under Uncertainty: A Deep Learning–Based Approach
BT  - Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
PB  - Atlantis Press
SP  - 430
EP  - 433
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-711-8_40
DO  - 10.2991/978-94-6239-711-8_40
ID  - Agram2026
ER  -