JCSIS
JCSIS

Optimization of Smart Cities CO₂ Reduction Through Machine Learning-Based Renewable Energy Algorithms

Nader BehdadLima HongouSofia Arkhstan
Volume 4

Abstract

Reducing CO₂ emissions in urban environments is essential for combating climate change and promoting sustainability. This study proposes an optimization approach for smart cities by integrating machine learning algorithms with renewable energy systems to reduce CO₂ emissions. The system utilizes real-time data from IoT sensors to monitor energy consumption and CO₂ levels across various urban sectors. Machine learning models, such as regression analysis and deep learning algorithms, are applied to optimize the deployment of renewable energy sources, including solar, wind, and biomass, to minimize emissions. The optimization process involves smart grid management and demand-response strategies to balance energy supply and demand efficiently. Results demonstrate that the proposed approach significantly reduces CO₂ emissions while enhancing the overall energy efficiency of smart cities, offering a scalable solution for sustainable urban development.


Keywords

Smart cities, CO₂ reduction, machine learning, renewable energy, optimization, sustainability

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