A Preliminary Study on Low-Carbon City Planning Methods Supported by the Cluster Optimization Algorithm

With the acceleration of global city, low-carbon urban planning has become increasingly important. Traditional urban planning methods have encountered certain problems in facing this challenge. Therefore, from the perspective of clustering optimization algorithms, this article proposes a low-carbon urban planning method based on clustering optimization algorithms. Plan and adjust clustering optimization algorithms to comprehensively identify low-carbon city data and shorten the time of clustering optimization algorithms. Structuring urban areas using clustering algorithms to reduce carbon emissions and energy consumption. In the experiment, this method was applied to low-carbon planning in a certain city and achieved good results. The results show that the low-carbon urban planning method using clustering optimization algorithm can reduce carbon emissions, save energy, improve regional quality, and provide new solutions for urban planning.