Machine learning-based real-time daylight analysis in buildings

Abstract

Daylight analysis is essential in building design to ensure indoor environment quality, including health and thermal comfort vis-à-vis energy. It is a repeating and time-consuming process of design options. Several studies conducted machine learning models to accurately predict daylight performance in particular design situations. Therefore, developing an AI-based real-time daylight analysis platform becomes more promising. However, buildings can be designed with arbitrary shapes, creating a real challenge for the AI to recognize any building layout. From that perspective, the idea of finding the design variables that characterize all the building layouts becomes the key solution. To unlock this challenge, we promote a novel method of creating design variables and building a machine learning model that can efficiently forecast daylight performance with different building layouts. The daylight metric was Useful Daylight Illuminance with four ranges, and the case studies were assumed medium-sized buildings located in Ho Chi Minh City, Vietnam. All the data for training and predicting were created by the simulation DIVA tool. Obtained results showed the excellent performance of the proposed approach, which brings more promising in developing a data-driven machine learning platform for real-time daylight validation. Moreover, the present framework can adapt to any specific machine learning model or daylight simulation tool and daylight metrics.