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- W4384789431 abstract "Reducing stress on energy demand is crucial for promoting controlled environment agriculture, the most energy-intensive sector. Refining the modeling approach and control strategies can help reduce energy demand from crop cultivation facilities. This study presents a novel physics-informed neural network that considers both greenhouse energy demand and crop models to minimize weather forecast errors in controlled environment facilities. The adaptive linearization feedback scheme is used to handle nonlinear relationships between environment, crops, and control elements such as heating, ventilation, CO2 enrichment, and more. The proposed control inputs are determined by the data-driven robust model predictive control, which uses machine learning to address weather forecast uncertainties. The proposed control framework has been demonstrated to effectively reduce energy consumption while maintaining suitable growing conditions for crops, even in challenging conditions." @default.
- W4384789431 created "2023-07-20" @default.
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- W4384789431 date "2023-01-01" @default.
- W4384789431 modified "2023-10-16" @default.
- W4384789431 title "Energy management for controlled environment agriculture based on physics informed neural networks and adaptive linearization based data-driven robust model predictive control with AI" @default.
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- W4384789431 doi "https://doi.org/10.1016/b978-0-443-15274-0.50351-6" @default.
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