Energy efficiency security in urban areas: Challenges and implementation

Abstract

In this paper, we explore the transformative potential of data-driven methodologies in the intricate landscape of urban energy transactions, with a keen focus on enhancing decision-making intelligence, amplifying the integration of renewable energy sources, and fostering active community engagement. A pivotal contribution to this research lies in the introduction of an innovative Intrusion Detection System (IDS) fortified by the robust capabilities of deep evolving convolutional Generative Adversarial Networks (GANs). This novel security measure is designed to fortify urban energy transactions against potential cyber threats, ensuring the integrity and reliability of the energy infrastructure. Furthermore, our study introduces a groundbreaking hybridization of the Bat Algorithm (BA) and Teaching-Learning-Based Optimization (TLBO) for the optimization of GAN model training. This fusion of optimization techniques enhances the efficiency and accuracy of the training process, elevating the reliability of the generated models within the context of urban energy management. To validate and demonstrate the practical applicability of these innovations, we deploy them on the real-time dataset of a smart city's digital twin. This implementation serves as a tangible proof of concept, offering valuable insights into the performance and adaptability of the proposed approaches within real-world urban energy scenarios. As the findings unfold, it becomes evident that our research significantly contributes to the evolution of urban energy management, providing a robust foundation for the development of secure, resilient, and community-centric smart city energy systems. Through these advancements, we pave the way for a sustainable urban future where energy transactions are not only efficient but also secure and inclusive, aligning with the broader goals of smart city development.