Dynamic sensing and control system using artificial intelligent techniques for non-uniform indoor environment

Traditional ventilation systems have difficulty in effectively capture the dynamic and non-uniform distribution of the indoor environment, making it difficult to meet the dynamic demand of the indoor environment in real time for regulation and control. This results in a large amount of energy being wasted, and the comfort of indoor personnel is not guaranteed. Microsensors are used to monitor the air quality of the indoor environment for responding to the indoor environmental conditions and providing real-time regulation of the indoor ventilation system. However, monitoring by a single microsensor alone does not reflect the global indoor environment and can easily lead to non-uniform distribution of indoor environment and thermal discomfort for personnel. Excessive sensors can lead to wasted resources and inconvenient space in the room. Therefore, this research developed dynamic ventilation control system based on “limited monitoring - fast prediction - real-time control” method. The system realized online transmission and reception of indoor environment monitoring data through wireless communication technology. Combined with fast prediction model to obtain the optimal air changes per hour (ACH) value of indoor environment, it can help realize the real-time regulation and intelligent control of indoor ventilation system, further promoting a balance between the energy consumption and indoor environmental quality (up to 60% ventilation energy savings). This work can provide important strategies and technologies for the construction and implementation for intelligent ventilation control systems. • Coupling dynamic control method and ventilation system design aiming for indoor air pollution prediction and online control. • A new intelligent dynamic ventilation system based on an embedded system and software design. • LLVM-based ANN applied for real time indoor environment prediction, with the maximal error of 12%. • A significant guidance provided for the development and application of intelligent ventilation system.