Climatization and luminosity optimization of buildings using genetic algorithm, random forest, and regression models

Abstract With the rise in popularity of artificial intelligence, coupled with growing concern over environment, there has been a surge use intelligent energy management systems. Additionally, as more buildings transition into smart grid and, consequently, and environmental data is gathered, significant increase number data-driven approaches for building This paper proposes methodology that aims to optimize climatization luminosity inside building, using genetic algorithm, random forest, two polynomial models. The proposed enables real-time taking account user needs preferences. Air conditioner units light systems are optimized minimize costs, while also improving air quality considering users’ temperature shows results achieved, by solution, an office case study. promising demonstrate possibility minimizing costs maximizing comfort.