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Optimizing Reservoir Operations with Reinforcement Learning: A Data-Driven Framework | ||
| Civil Engineering and Applied Solutions | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 29 مرداد 1404 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22080/ceas.2025.29738.1032 | ||
| نویسندگان | ||
| Fariborz Masoumi1؛ Mehdi Jorabloo* 2؛ Gholamreza Shobeyri3 | ||
| 1Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 2Department Water Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran. | ||
| 3Faculty of Civil, Water & Environmental Engineering, Shahid Beheshti University, Tehran, Iran | ||
| تاریخ دریافت: 06 مرداد 1404، تاریخ بازنگری: 28 مرداد 1404، تاریخ پذیرش: 29 مرداد 1404 | ||
| چکیده | ||
| Effective reservoir management demands adaptive, data-driven strategies to optimize storage and release decisions while balancing multiple, often competing, operational objectives. This study investigates the application of Q-Learning, a model-free reinforcement learning (RL) algorithm, for optimizing reservoir releases under dynamic and uncertain hydrological conditions. Unlike conventional rule-based or offline optimization methods, the proposed RL approach continuously refines its release policy by learning from environmental feedback and observed states, enabling real-time adaptation without the need for a predefined system model. The framework is tested on the Dez Reservoir in Iran, a real-world case study characterized by significant inflow variability and seasonal water demand. Simulation results demonstrate that Q-Learning effectively manages operational complexity, maintaining storage within prescribed bounds and delivering release patterns closely aligned with demand. To benchmark performance, a simplified Ant Colony Optimization (ACO) model is implemented for comparison. While ACO shows moderate capability in deficit reduction, Q-Learning outperforms it in terms of constraint satisfaction and long-term feasibility. Findings highlight the strong potential of reinforcement learning to support intelligent, scalable, and robust decision-making in modern reservoir operation systems under uncertainty | ||
| کلیدواژه‌ها | ||
| Reservoir Operation؛ Reinforcement Learning؛ Dez Dam؛ Machine Learning | ||
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آمار تعداد مشاهده مقاله: 2 |
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