Advanced lighting simulation tools as well as computationally intelligent systems present the possibility of using a model based on computation as a means of controlling lighting on the visual task. Lighting control has now become an essential element of good design and an integral part of energy management programmes. This paper presents a novel computational model suitable for the adaptive predictive control of artificial light in accordance with the variation of daylight. Simulated data and an adaptive neuro-fuzzy inference system are incorporated into the model. The software package Radiance is used to carry out the simulation. In this process, the role of a simulator is considered as the source of the system knowledge by which a supervised learner, implemented in adaptive neuro-fuzzy inference system is trained for faster predictions. The goal of this paper is to make use of the benefits of the hybridization between simulation and machine learning for the purpose of light control.
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