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JCSIS

Renewable Energy Efficiency Optimization Through Neural Networks and Quantum Algorithms in Smart Cities

Narcisa ZlatanHakan KhanNajaad OubeBlika
Volume 4

Abstract

The increasing demand for sustainable and efficient energy systems in smart cities has necessitated the integration of advanced computational techniques for renewable energy optimization. This research explores the combined application of neural networks and quantum algorithms to enhance the efficiency and stability of renewable energy sources, such as solar and wind, within urban infrastructures. Neural networks are employed for dynamic prediction and adaptive control of energy consumption and generation patterns, while quantum algorithms are leveraged to solve complex optimization problems related to grid stability, load forecasting, and resource allocation. The hybrid model aims to improve decision-making speed and accuracy in real-time energy management systems. Experimental evaluations demonstrate superior performance over conventional models in terms of accuracy, scalability, and computational cost, indicating a significant step toward intelligent, sustainable urban energy ecosystems.

Keywords

renewable energy, neural networks, quantum algorithms, smart cities, energy optimization, sustainable systems

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