An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment

Abstract

Relieving the stress from energy demand is critical for encouraging the application of built environment in agriculture, which is the most energy-intensive food-production sector. In this study, to enhance energy efficiency and crop growth optimization for the built environment, we present a novel artificial intelligence (AI)-based control framework which combines a physics-informed neural network and data-driven robust model predictive control. The physics-informed neural network is constructed for accurately predicting the built environment’s indoor climate and crop states based on control inputs, including controls of temperature, humidity, CO2, irrigation, and fertilization. Data-driven robust model predictive control uses machine learning techniques to account for unpredictable changes in weather conditions to make the best control decisions for actuators. Two examples of the implementation of the proposed AI-based control framework in tomato cultivation environments in Ithaca, New York and Tucson, Arizona are shown to showcase its effectiveness in varying climates. The results show our proposed AI-based control framework can maintain the ideal cultivation environment and, on average, lower cost by 46.4% compared to the conventional control approaches.