VPD-Centric Cascading Control with Neural Network Optimization for Energy-Efficient Climate Management in Controlled Environment Agriculture

TL;DR

This paper introduces a VPD-centric cascading control system with neural network optimization for energy-efficient climate management in controlled environment agriculture, achieving significant energy savings and stability improvements.

Contribution

It presents a novel cascading control architecture with neural network optimization that outperforms traditional PID control in energy efficiency and stability.

Findings

30-38% HVAC energy reduction across facilities

68-73% improvement in VPD stability

60-67% faster disturbance recovery

Abstract

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.