Adaptive blind control strategy based on iterative optimization to minimize cooling and lighting demands in office space

The emergence of adaptive facades offers a new approach for buildings to enhance their resilience against external weather conditions while responding to occupants’ demands, thereby improving both indoor environmental quality and energy performance. Appropriate control methods are crucial to achieving these purposes. However, most existing studies for automatic control of blinds have focused on visual comfort, leaving potential for further energy savings by reducing cooling and artificial lighting demands. Additionally, current optimization methods for slat angles are mostly simplified as a discrete process, neglecting the impact of thermal mass in building envelopes. Therefore, this paper aims to explore the energy reduction potential of window blinds by developing an iterative optimization method for devising hourly adaptive control strategies. To this end, a co-simulation platform between EnergyPlus and Python was established for the optimization and a case study in a subtropical city was conducted. The proposed strategies effectively balanced lighting and cooling demands to achieve an overall energy reduction of 7.3%–12.5% compared to reference cases while also ensuring visual comfort by mitigating glare risk and excessive daylight. These advantages were also compared with several simpler control scenarios, with analyses tailored to various glazing types and orientations. Furthermore, the optimal window configurations with blind control strategies for different orientations were determined. The findings also indicated that glass properties markedly impact the performance of control strategies, underscoring the necessity of holistically considering shading components and glazing types in the optimization to achieve optimal performance.