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JCSIS

Renewable Energy System Optimization Through IoT-Based Deep Learning and Metaheuristic Algorithms

Khadija ShazlyLima HongouHakan Khan
Volume 4

Abstract

As global energy demands rise and environmental concerns intensify, the optimization of renewable energy systems has become essential for achieving sustainable development goals. This paper presents a comprehensive approach to enhancing the efficiency and reliability of renewable energy systems by integrating Internet of Things (IoT) technologies with deep learning and metaheuristic algorithms. IoT devices are used to collect real-time data on energy production, environmental conditions, and consumption patterns. Deep learning models are applied for accurate forecasting of energy demand and production, enabling dynamic system responses. Metaheuristic algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—are employed to optimize energy flow, resource allocation, and storage management. The synergy of these technologies contributes to reducing operational costs, minimizing energy losses, and improving the overall sustainability of energy networks in smart city infrastructures.

Keywords

Renewable energy optimization, IoT, deep learning, metaheuristic algorithms, smart grids, energy forecasting

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