Deep reinforcement learning approach for hybrid renewable energy systems optimization
Authors:
- Inoussa LegreneILInoussa Legrene
- Tony WongTWTony Wong
- Louis-A. DessaintLDLouis-A. Dessaint
Open Access
- 16 July 2025
- journal article
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 159
Abstract
No abstract availableThis publication has 44 references indexed in Scilit:
- Socio‐techno‐economic‐environmental sizing of hybrid renewable energy system using metaheuristic optimization approachesEnvironmental Progress & Sustainable Energy, 2024
- Operative generative design using non-dominated sorting genetic algorithm II (NSGA-II)Automation in Construction, 2023
- Optimal sizing of hybrid renewable energy systems for reliability enhancement and cost minimization using multiobjective technique in microgridsEnergy Storage, 2022
- Multi-objective particle swarm optimization with guided exploration for multimodal problemsApplied Soft Computing, 2022
- A multi-objective particle swarm optimization algorithm based on two-archive mechanismApplied Soft Computing, 2022
- Deep reinforcement learning in computer vision: a comprehensive surveyArtificial Intelligence Review, 2021
- A multi-objective particle swarm for constraint and unconstrained problemsNeural Computing and Applications, 2021
- A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applicationsArtificial Intelligence Review, 2020
- Optimal Sizing in Hybrid Renewable Energy System with the Aid of Opposition Based Social Spider OptimizationJournal of Electrical Engineering & Technology, 2019
- Multi-objective optimization of the environmental-economic dispatch with reinforcement learning based on non-dominated sorting genetic algorithmApplied Thermal Engineering, 2019