A review on machine learning algorithms to predict daylighting inside buildings

•A brief introduction on white-box and black-box approaches is provided.•Prior reviews on predicting building performance by machine learning are explored.•Studies on using machine learning to estimate internal daylighting are analyzed.•The review analysis considered the scope, algorithms, data, and evaluation metrics.•Future trends are revealed for the use machine learning in Architectural practice. Steep increases in air temperatures and CO2 emissions have been associated with the global demand for energy. This is coupled with population growth and improved living standards that encourages the reliance on mechanical acclimatization. Lighting energy alone is responsible for a large portion of total energy consumption in office buildings; and the demand for artificial light is expected to grow in the next years. One of sustainable approaches to enhance energy-efficiency is to incorporate daylighting strategies, which entail the controlled use of daylight inside buildings. Daylight simulation is an active area of research that offers accurate estimations, yet requires a complex set of inputs. Even with today’s computers, simulations are computationally expensive and time-consuming, hindering to acquire accelerated preliminary approximations in acceptable timeframes, especially for the iterative design alternatives. Alternatively, predictive models that build on machine learning algorithms have granted much interest from the building design community due to their ability to handle such complex non-linear problems, acting as proxies to heavy simulations. This research presents a review on the growing directions that exploit machine learning to rapidly predict daylighting performance inside buildings, putting a particular focus on scopes of prediction, used algorithms, data sources and sizes, besides evaluation metrics. This work should improve architects’ decision-making and increase the applicability to predict daylighting. Another implication is to point towards knowledge gaps and missing opportunities in the related research domain, revealing future trends that allow for such innovative approaches to be exploited more commonly in Architectural practice.