Eee projects on solar based greenhouse climate control — Simulation & Power Modeling

Abstract — Eee projects on solar based greenhouse climate control

Eee Projects On Solar Based Greenhouse Climate Control: Power Electronics, MATLAB/Simulink & Circuit Modeling Integrated Analysis: MATLAB/Simulink · MOSFET/IGBT Gate Drivers · SPWM/SVPWM · THD Spectrum · ~13502 Words Chapter 1: Power Electronics Overview, System Specifications & Topology Modeling The integration of photovoltaic (PV) systems into greenhouses not only optimizes land use but also enhances sustainable agricultural practices by enabling dual benefits of food production and renewable energy generation. However, accurate prediction of internal environmental conditions is crucial to ensure optimal crop growth while maximizing energy production.

Analytical & State-Space Control Methodology

This study introduces a novel application of Spatio-Temporal Graph Neural Networks (STGNNs) to greenhouse microclimate modeling, comparing their performance with traditional Recurrent Neural Networks (RNNs). While RNNs excel at temporal pattern recognition, they cannot explicitly model the directional relationships between environmental variables. Our STGNN approach addresses this limitation by representing these relationships as directed graphs, enabling the model to capture both environmental dependencies and their directionality. Using high-frequency data collected at 15-minute intervals from a greenhouse in Volos, Greece, we demonstrate that RNNs achieve exceptional accuracy in winter conditions ( R 2 = 0.985 R^{2}=0.985 ) but show limitations during summer cooling system operation. Though STGNNs currently show lower performance (winter R 2 = 0.947 R^{2}=0.947 ), their architecture offers greater potential for integrating additional variables such as PV generation and crop growth indicators.

Simulink Waveforms & Power Quality Findings

Modern agriculture faces increasing pressure to optimize land use while reducing its environmental impact. The integration of photovoltaic (PV) systems into agricultural greenhouses has emerged as a promising solution to these challenges, offering dual benefits of food production and renewable energy generation. However, this integration introduces new complexities in greenhouse management, as PV panels directly influence light distribution, temperature patterns, and overall microclimate dynamics. These Agri-PV systems optimize land use by combining solar energy harvesting with protected crop cultivation, offering potential benefits for both energy and food security. Yet the successful implementation of PV-integrated greenhouses requires accurate prediction and control of the internal microclimate to ensure optimal growing conditions while maximizing energy production. While sophisticated physical models incorporating greenhouse thermodynamics, crop physiology, and PV system performance exist, their complexity often restricts real-time applications and rapid design optimization. This study, conducted within the framework of the European REGACE project, represents an initial step toward developing a comprehensive digital twin for PV-integrated greenhouses. Rather than relying on complex physical models, we explore empirical approaches that employ advanced machine learning to predict greenhouse microclimates. The focus on neural network-based models aims to lay the foundation for a more complete digital twin that will ultimately integrate crop growth dynamics and PV energy production. By comparing traditional Recurrent Neural Networks (RNNs) with directed Spatio-Temporal Graph Neural Networks (STGNNs), we investigate whether the explicit modeling of environmental relationships and directional influences can improve prediction accuracy while maintaining computational efficiency. Effective control and optimization of PV-integrated greenhouse environments present a fundamental challenge. Accurate climate control software integrating real-time data from environmental control computers (ECCs) can significantly enhance greenhouse management ( Aaslyng et al., 2005 ) . Nonetheless, internal parameters such as temperature and humidity are influenced by various external conditions, including ambient temperature, solar radiation, and wind speed. Conventional data are collected at high frequency ( Aaslyng et al., 2003 ) . Researchers have therefore pursued increasingly sophisticated methods to balance these complexities, from physical simulations to data-driven models. Greenhouses represent intricate thermodynamic systems where external conditions and the internal microclimate interplay to influence crop growth and energy use. The introduction of PV panels adds a layer of complexity, prompting further advancements in modeling techniques ( Ouazzani Chahidi et al., 2021 ; Baglivo et al., 2020 ; Brækken et al., 2023 ) . These approaches range from detailed physics-based studies focusing on ventilation and evapotranspiration ( Stanciu et al., 2016 ; Abdel-Ghany & Kozai, 2006 ; Mobtaker et al., 2019 ; Singh et al., 2018 ; Fitz-Rodríguez et al., 2010 ) to data-driven or hybrid solutions capable of simulating both microclimate and crop growth processes ( Massa et al., 2011 ) . While such models provide essential insights into greenhouse behavior, they can be computationally intensive, and their parameterization often demands multidisciplinary expertise. To address these constraints, researchers have increasingly explored empirical, data-driven approaches that learn greenhouse behavior directly from observations. Early neural network applications demonstrated promise ( Seginer et al., 1994 ; Bussab et al., 2007 ) , evolving into more sophisticated architectures such as Radial Basis Function Neural Networks ( Ferreira et al., 2002 ) and optimized Multi-layer Perceptrons ( D’Emilio et al., 2012 ) . A major leap forward came with Recurrent Neural Networks (RNNs), notably Long Short-Term Memory (LSTM) variants, which excel at modeling time-dependent relationships in greenhouse environments ( Fourati & Chtourou, 2007 ; Gharghory, 2020 ; Hongkang et al., 2018 ) . In parallel, Nonlinear AutoRegressive models with eXogenous inputs (NARX) have shown effectiveness in capturing complex greenhouse system dynamics ( Manonmani et al., 2018 ; Gao et al., 2023 ) . Despite these advances, conventional neural networks predominantly treat environmental variables independently, limiting their ability to depict spatial or directional interconnections. This gap becomes especially relevant in PV-integrated greenhouses, where factors such as solar radiation, humidity, and temperature form intricate environmental interactions that purely temporal models may overlook.

Scope & Practical Applications

• High-efficiency DC-DC converters and isolated resonant topologies (LLC / DAB)
• Sinusoidal and Space Vector PWM inverters for grid integration & industrial motor drives
• MOSFET / IGBT gate driver optimization with active Miller-clamp shoot-through protection
• Power quality conditioning with Total Harmonic Distortion (THD) strictly compliant with IEEE 519
• Complete Simscape .SLX model files, parameter calculation scripts, and IEEE dissertation reports