MPC for the Indoor Climate Control and Energy Optimization of a Building-Integrated Rooftop Greenhouse Systems

In this work, we design a nonlinear model predictive control (NMPC) strategy to optimize energy management in integrated rooftop greenhouses and buildings, aimed at minimizing operational expenses and reducing climate irregularities. This integrated system is tailored to maintain ideal conditions for both plant growth and building occupancy, utilizing the building's excess heat and air to lower energy consumption and CO2 emissions. The NMPC framework employs dynamic nonlinear models for temperature, humidity, and CO2, enhanced with real-time weather data, to improve control accuracy beyond traditional MPC methods. It manages various systems including fans, pad cooling, shading devices, heating, ventilation, air conditioning, CO2 injection, and lighting to precisely control environmental conditions. A case study from a rooftop greenhouse in Brooklyn, New York, demonstrates the NMPC's efficacy, showing an average energy savings of 15.2%. These results highlight the significant potential of NMPC to advance urban agricultural systems and building management practices. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)