Double deep Q-network for intelligent control and energy efficiency optimization of zonal ventilation in laying-hen houses

Precise environmental control in laying hen houses is essential for animal welfare and production efficiency. Traditional ventilation strategies based on fixed temperature thresholds cause significant environmental fluctuations and high energy consumption due to frequent fan cycling. To address this, we propose a ventilation control strategy utilizing a Double Deep Q-Network (Double DQN) reinforcement learning algorithm. The system partitions the hen house into four equal-volume zones, each equipped with a positive-pressure fan unit. These units cooperate with a central negative-pressure fan set for precise temperature and humidity regulation. The strategy employs a composite state space integrating real-time environmental parameters (temperature, humidity) and fan operation status. A multi-dimensional action space defines the on/off combinations for the 16 commands governing the four positive-pressure fan units. A dual-objective reward function incorporates both environmental parameter deviation from setpoints and penalties for fan switching. Experimental results demonstrate that the Double DQN strategy significantly reduces the standard deviation of temperature and humidity across all zones compared to traditional threshold control, achieving closer proximity to the target setpoint (26°C, 70%). Furthermore, it reduces the daily energy consumption of the positive-pressure fan units by 10.35% (103.63 kWh total). This strategy markedly enhances environmental control precision and stability while conserving energy, offering a novel intelligent solution for sustainable facility livestock environmental management.