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dc.contributor.authorNASRI, Sonia-
dc.date.accessioned2025-11-19T14:13:48Z-
dc.date.available2025-11-19T14:13:48Z-
dc.date.issued2025-05-
dc.identifier.urihttp://dspace.univ-tiaret.dz:80/handle/123456789/16858-
dc.description.abstractThe increasing complexity of urban environments and the growing demand for sustainable development have accelerated the adoption of smart city paradigms. Smart cities integrate advanced technologies to enable real-time monitoring and intelligent management of critical resources such as electricity gas, and water. These systems aim to enhance operational efficiency, reduce environmental impact, and support long-term sustainability. However, the implementation of such systems is not without challenges. Issues related to interoperability, scalability, data overload, and the energy requirements of IoT infrastructure could hinder the effectiveness and reliability of smart city applications. In particular, energy management within smart buildings remains a critical area requiring robust predictive models capable of handling uncertainty and complex consumption patterns.en_US
dc.language.isoenen_US
dc.publisherUniversity of Ibn Khaldoun Tiareten_US
dc.subjectSmart citiesen_US
dc.subjectenergy managementen_US
dc.subjectdeep learningen_US
dc.subjectFuzzificationen_US
dc.titleProposed Approach for an Intelligent Energy Management System in Smart Citiesen_US
dc.typeThesisen_US
Collection(s) :Master

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