Sustainable cooling and heating in smart cities using solar energy system planning

In order to reduce waste and boost efficiency, smart cities must first and foremost eliminate trash. Effectiveness can be demonstrated in virtually any field of endeavour by saving energy, reducing energy intensity, sustaining economic growth, increasing productivity, protecting the environment, and collaborating in the battle against climate change. Because of a lack of money and technological capabilities, smart and sustainable city goals are difficult for impoverished towns to achieve. The study aims at finding the renewable energy solutions with energy efficiency and that should be technically feasible and practicable in poor communities in order to improve living conditions and overall life quality in these communities. In this paper, we use Naïve Bayes classifier to classify the input data to find the situation in which the climate can be maintained in such a way that it reduces the burden of environment in smart cities. The results of simulation shows that the Naïve Bayes classifier is effective enough in building sustainable environment with various tests.