Automated day-lighting systems can modulate window blinds and electrical lights for maintaining the proper illumination levels and save significant electrical energy in buildings. This paper presents an initial work toward developing an automated installation and maintenance of a generic daylighting system which is able to self-calibrate and adapt to the building needs with minimal human intervention. The system operates based on information provided by a wireless sensor network, and processed through learning algorithms and feedback control principle. This paper focuses on a preliminary simulation study to establish a control baseline and identifies the required elements. It demonstrates the concept, using daylighting simulation software in the context of a test cell which represents a virtual office space. A startup baseline for the optimal blind slat angle settings for the windows is developed with the objective of maintaining uniform lighting levels on a horizontal surface inside the test cell. The lighting baseline simulations are limited to specific times and days of a year to reduce and optimize the simulation process and are applied to predict the optimal blind slat angles for other days of the year. This paper presents and discusses the results of such an analysis including an extrapolation to all year round.