Artificial intelligence (AI) technology has rapidly advanced and transformed the nature of scientific inquiry. The recent release of the large language model Chat Generative Pre-Trained Transformer (ChatGPT) has attracted significant attention from the public and various industries. This study applied ChatGPT to autonomous building system operations, specifically coupling it with an EnergyPlus reference office building simulation model. The operational objective was to minimize the energy use of the building systems, including four air-handling units, two chillers, a cooling tower, and two pumps, while ensuring that indoor CO₂ concentrations remain below 1000 ppm. The performance of ChatGPT in an autonomous operation was compared with control results based on a deep Q-network (DQN), which is a reinforcement learning method. The ChatGPT and DQN lowered the total energy use by 16.8% and 24.1%, respectively, compared with the baseline operation, while maintaining an indoor CO₂ concentration below 1000 ppm. Notably, compared with the DQN, ChatGPT-based control does not require a learning process to develop intelligence for building control. In real-world applications, the high generalization capabilities of the ChatGPT-based control, resulting from its extensive training on vast and diverse data, could potentially make it more effective.

Blind systems have been introduced to provide visual and thermal comfort, as well as to reduce energy use in buildings. A wide variety of such systems exist in terms of thermal and optical properties, location and physical configuration. The current problem with blinds is that their operation is not based on the dynamics of the room but on the static or manual control operated by occupants, although many studies have recognized that dynamic control can far outperform static control. One reason for the lack of dynamic control is that it is not easy to combine the room dynamics with any possible optimization algorithm. Hence, in this study, a whole building simulation tool, EnergyPlus, was integrated with the MATLAB optimization toolbox to solve for optimal control of blind systems. This paper addresses the difference between static versus optimal control of interior and exterior blind systems in office buildings.