Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.28570

A Comparative Study of LED Lighting Control Strategies for Energy-Efficient Greenhouse Operations

Sajad Ahmadi·Eric J. Stallknecht·Javad Mohammadpour Velni
View original source ↗Read source document ↗Export metadata ↓

Abstract

Greenhouse supplemental lighting is essential for meeting crop Daily Light Integral (DLI) requirement. However, conventional always-on and rule-based strategies are inherently energy-inefficient since they ignore future sunlight availability. This paper reports the results of deploying various supplemental lighting control strategies, namely, always-on, always-off, heuristic, optimal prediction-based, and two peak-shift variants, simultaneously in a single greenhouse at the Hampton Roads AREC of Virginia Tech, to ensure that every treatment experiences identical conditions. %The shared infrastructure is built on an MQTT-based IoT platform with Photosynthetic Photon Flux Density (PPFD) sensing and PWM-driven LED control; The prediction-based and peak-shift strategies optimize LED schedules using a Markov chain-based solar forecast. Experimental results over two replicates %(20~February--15~April~2026, 72~plants per treatment) show that the prediction-based strategy meets the desired DLI target at 65\% cost reduction relative to the always-on baseline while achieving the highest dry-mass yield among the proposed adaptive strategies. The heuristic strategy incurs only a 6\% cost premium over prediction-based control, offering a competitive forecast-free alternative. The peak-shift variants achieve the lowest active-strategy costs by shifting LED demand into off-peak tariff windows. A yield-per-dollar efficiency metric is finally introduced to jointly capture energy cost and crop output showing that the prediction-based strategy ranks highest at 18.976~g/\$, more than double the always-on value of 9.391~g/\$. These findings can provide greenhouse operators with an evidence-based framework for selecting a lighting control approach that balances energy cost, crop quality, and system complexity.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sajad Ahmadi, Eric J. Stallknecht, Javad Mohammadpour Velni. 2026-10-20. A Comparative Study of LED Lighting Control Strategies for Energy-Efficient Greenhouse Operations. https://arxiv.org/abs/2610.28570

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

General-Sum Markov Games with Borel Spaces: Existence of Approximate Nash Equilibria via Finite Model Approximations under Setwise or Weak Continuity

Establishing the existence of exact or near Markov or stationary perfect Nash equilibria in nonzero-sum Markov games over Borel spaces is a challenging problem with limited positive results. Motivated by problems in multi-agent and Bayesian learning, this paper demonstrates the existence of approximate Markov and stationary Nash equilibria for such games under relatively mild regularity conditions; notably under setwise continuity. Our approach is constructive: For both compact and non-compact state spaces, we approximate the Borel model with finite state-action models and show that their equilibria correspond to \(ε\)-equilibria for the original game. Compared with previous results in the literature, which we comprehensively review, we provide more general conditions, along with explicit approximation models whose equilibria are $ε$-equilibria for the original model. For completeness, we also study the approximation of zero-sum Markov games and Markov teams to highlight the key differences between zero-sum and nonzero-sum settings. In particular, while for zero-sum and team games, joint weak (Feller) continuity of the transition kernel is sufficient (as the value function is continuous), this is not the case for general nonzero-sum games.

eess.SY↗
arXiv2026

Estimating Density Functions for Probabilistic Power Flow Using Invertible Neural Networks

Probabilistic power flow (PPF) is essential for quantifying operational uncertainty in modern power systems with high penetrations of renewable generation and flexible loads. Conventional PPF methods primarily rely on Monte Carlo (MC)based power flow (PF) simulations or simplified approximations of voltage probability density functions. Although MC methods provide high accuracy, they incur substantial computational and data-storage costs, whereas simplified approximations, though lightweight, often sacrifice accuracy due to the reliance of certain assumption. In this paper, we propose a novel PPF density approximation framework that leverages the expressive power of neural networks (NNs) and can, in principle, approximate complex voltage distributions directly without restrictive assumptions. The core idea is to learn an explicit mapping between stochastic power injections and system voltages using invertible neural networks (INNs), and then to apply the change-of-variables theorem to obtain the voltage densities. Extensive numerical studies demonstrate that the proposed framework achieves stateof-the-art performance both as an accurate PF solver and as an efficient PPF density estimator

eess.SY↗
arXiv2026

Implicit Predecessor-Based Region of Attraction Estimation and Robust Invariance Analysis for a Two-Wheeled Inverted Pendulum

Estimating the region of attraction (RoA) of nonlinear systems is fundamental for assessing closed-loop stability and ensuring safe operation. While Lyapunov-based approaches provide certified stability guarantees, they often yield conservative inner approximations of the RoA. This paper combines a certified Lyapunov-based positively invariant set with a predecessor-based implicit representation to compute a significantly less conservative inner approximation of the RoA while preserving formal stability guarantees. In addition, the robust positive invariance of the initial certified Lyapunov-based invariant set is analyzed under bounded additive input disturbances, providing formal robustness guarantees. The proposed methodology is demonstrated on a nonlinear two-wheeled inverted pendulum stabilized by a saturated linear quadratic regulator. The resulting RoA approximation is compared with the initial Lyapunov-certified invariant set and validated through Monte Carlo simulations and hardware experiments, showing a substantially enlarged certified operating region that matches the empirical closed-loop behavior. These results demonstrate the practical applicability of combining certified Lyapunov analysis with predecessor-based set propagation for RoA approximation and robustness assessment of nonlinear systems.

eess.SY↗