VPD-Centric Cascading Control with Neural Network Optimization for Energy-Efficient Climate Management in Controlled Environment Agriculture
This work provides a practical, validated solution for energy-efficient climate control in commercial controlled environment agriculture, addressing a known bottleneck in HVAC energy waste.
Conventional PID-based climate control in controlled environment agriculture wastes 20-40% HVAC energy due to cross-coupling conflicts. The proposed VPD-centric cascading control with neural network optimization achieves 30-38% HVAC energy reduction, 68-73% better VPD stability, and 60-67% faster disturbance recovery across 30+ facilities in 8 U.S. climate zones.
Conventional climate control in Controlled Environment Agriculture (CEA) uses independent PID loops for temperature and humidity, creating cross-coupling conflicts that waste 20-40% of HVAC energy. We propose a cascading architecture that elevates Vapor Pressure Deficit (VPD) from a monitored metric to the primary outer-loop control variable. A 7-3-3 neural network optimizer selects energy-minimal temperature-humidity setpoints along the VPD constraint surface, feeding inner PID loops that drive HVAC actuators. Lyapunov stability analysis guarantees bounded PID gains. Deployment across 30+ commercial facilities in 8 U.S. climate zones over 7+ years demonstrates 30-38% HVAC energy reduction, 68-73% improvement in VPD stability, and 60-67% faster disturbance recovery compared to independent PID baselines.