Optimizing Renewable Energy in Smart Cities by Integrating Big Data and Decision Support Systems

The rapid development of the smart city concept encourages the need for energy management that is more efficient, sustainable and adaptive to the needs of modern urban communities. In this context, renewable energy is the main solution to reduce dependence on fossil energy sources that are limited and pollute the environment. This research aims to optimize the utilization of renewable energy in smart cities by integrating Big Data technology and Decision Support Systems (DSS). The approach used in this research is a case study and system modeling method, which involves collecting energy data from various sources such as IoT sensors, weather stations, and energy distribution systems in real-time. The data is then analyzed using Big Data Analytics techniques to identify energy consumption patterns, potential renewable energy production, and peak load predictions. Furthermore, a decision support system was designed to assist policy makers and city managers in determining optimal energy distribution and usage strategies based on the available data and simulations. The results show that the integration of Big Data and DSS is able to increase the efficiency of renewable energy utilization up to 25% compared to conventional systems. In addition, the system is also able to dynamically respond to changing conditions and provide more accurate and adaptive decision recommendations. These findings indicate that the synergy between data technology and decision support systems plays a strategic role in creating sustainable and environmentally sound smart cities.