Hu, Guoqing (Cornell University), You, Fengqi (Cornell University)

Assessment of AI-Based Robust Model Predictive Control Application in Large-Scale Photovoltaic-Based Controlled Environment Agriculture for Urban Agriculture

Scheduled for presentation during the Regular Session "Nonlinear and robust control of power systems" (ThuS2T3), Thursday, July 11, 2024, 12:50−13:10, Session room 3

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on March 6, 2025

Abstract

This research conducts an in-depth evaluation of integrating photovoltaic-based controlled environment agriculture (PV-based CEA) within urban centers, focusing on the advantages of implementing AI-based robust model predictive control (AI-based RMPC) as opposed to traditional control strategies in such settings. The study explores the impact of these advanced control techniques on energy usage, resource conservation, economic viability, and environmental effects in urban PV-based CEA. A large-scale assessment through simulations conducted year-round in 10 significant U.S. cities will test the effectiveness and feasibility of this approach. Results from these comprehensive simulations suggest considerable improvements, with energy and resource efficiencies showing a 15.4% and 12.3% enhancement, respectively. Furthermore, economic gains are projected at 7.5%, alongside a notable reduction in environmental pollutants, with water pollution dropping by 8.7% and light pollution by 3.6%.