AI-Enabled Decision Support Framework for Sustainable Energy Management in Smart Cities-A Study

Abstract

The persistent trend of urbanization necessitates the development of smart cities for a sustainable future. Urban sustainability include proactive strategies aimed at maximizing resource use, minimizing ecological effect, and improving overall efficiency. Energy management is a primary consideration in urban, residential, and architectural development. Artificial Intelligence (AI) employs data analytics and machine learning to facilitate business automation and address complex tasks across several sectors. Consequently, artificial intelligence must be included into the strategic framework, particularly in the long-term strategy for smart city development. Decision Support Systems (DSS) are combined with human-machine interface techniques such as the Internet of Things (IoT).         
The expansion in size and complexity of IoT smart devices, industrial equipment, sensors, and mobile apps poses a growing difficulty in fulfilling Service Level Agreements (SLAs) across various cloud data centers and user demands. The difficulty would be exacerbated if the energy consumption of industrial IoT networks significantly escalated. Consequently, Decision Support System (DSS) models are essential for automated decision-making in critical Internet of Things (IoT) environments such as advanced industrial systems and smart cities. This research investigates how As urbanization presents new issues for cities globally, the notion of smart cities emerges as a possible answer, with artificial intelligence (AI) occupying a pivotal position in this endeavor. metamorphosis. This study provides a literature analysis of artificial intelligence solutions used in smart cities, concentrating on six primary domains: smart transportation, smart environment, smart governance, smart living, smart economics, and smart people. The study covers papers from 2021 to 2024 accessible on Scopus. This article analyzes the implementation of AI across many domains, highlighting obstacles, progress, and prospective trajectories.