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arXiv · 2604.13461

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

Andrii Vakhnovskyi
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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.

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Andrii Vakhnovskyi. 2026-04-15. VPD-Centric Cascading Control with Neural Network Optimization for Energy-Efficient Climate Management in Controlled Environment Agriculture. https://arxiv.org/abs/2604.13461

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