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

中文翻译：

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使用遗传算法、随机森林和回归模型对建筑物进行气候化和亮度优化

随着人工智能的普及，加上对环境的日益关注，智能能源管理系统的使用激增。此外，随着越来越多的建筑物过渡到智能电网，因此收集了更多的能源和环境数据，建筑物管理系统的数据驱动方法的数量显着增加。本文提出了一种方法，旨在使用遗传算法、随机森林和两个多项式模型来优化建筑物内部的气候和光度。所提出的方法能够在考虑到用户需求和偏好的情况下对建筑物进行实时管理。空调机组和照明系统经过优化，以最大限度地降低能源成本，同时也改善空气质量并考虑用户的温度和亮度偏好。本文展示了在办公楼案例研究中所提出的解决方案所取得的结果。有希望的结果证明了最大限度地减少能源成本同时最大限度地提高用户舒适度的可能性。