This paper presents a greenhouse indoor environment controller based on model predictive control (MPC), which can be integrated into existing greenhouse regulatory systems to optimally maintain critical climatic variables, including artificial lighting levels, CO2 rate, indoor temperature and humidity level within acceptable limits. The MPC based optimization problem aims to maximize the rate of crop photosynthesis while optimizing the use of the available water and energy sources, taking into account the unpredictability and intermittent nature of renewable energies and external atmospheric conditions. This would facilitate the management of greenhouses by anticipating control actions for a better quality of crops production. For that, the mathematical formulation of the optimal control problem is presented, and the numerical results related to the application of the MPC to case studies are analyzed integrating the effects of greenhouse structural considerations and the influence of climate data on its operation.

The global agricultural sector faces increasing challenges in adopting sustainable practices and reducing its environmental footprint. Smart greenhouse agriculture has emerged as a key solution, enabling efficient year-round crop production while minimizing dependence on traditional field farming. However, achieving near-zero energy consumption in greenhouses remains a major challenge due to the high operational energy demands. This review examines the current state of energy consumption in greenhouses, critically analyzes existing technological solutions, and identifies key challenges, such as high energy consumption for heating, cooling, and lighting. The study highlights opportunities for integrating renewable energy sources, optimizing energy-saving systems, and using advanced control technologies such as artificial intelligence (AI) and the Internet of Things (IoT) to monitor microclimatic conditions. Results show that integrating these solutions can significantly reduce energy consumption while maintaining optimal growing environments. The main findings include prioritizing the adoption of hybrid renewable energy systems, improving greenhouse design and material selection, and enhancing real-time monitoring systems with smart technologies. Future research should focus on cost-effective innovations, interdisciplinary approaches, and the scalability of energy-efficient designs. This review provides actionable information for researchers, policymakers, and practitioners to advance the transition to sustainable, near-zero energy greenhouse systems.

This paper presents an effective solution to manage the power flows exchanges in a campus integrated microgrid for peak reduction/shaving purposes. The campus integrated microgrid is composed of photovoltaic parking shades, an energy storage system, electric vehicles and bikes, loads, an advanced metering infrastructure, and a smart control unit. The latter is based on Model Predictive Control (MPC) whose objective is to reduce/shave the peak load of the campus while satisfying the Energy Storage System ESS, electrical Vehicles (EVs) and Electrical Bikes (EBs) state of charge. The proposed strategy aims to take the advantage of combining storage and photovoltaic (PV) systems to Vehicle to Campus (V2C) and Bike to Campus (B2C) concepts to support the microgrid to pay the minimum billing power while ensuring a good service quality to the EVs and EBs users. For that, the integration of the renewable energy sources and the different storage systems into the microgrid is modeled, and the MPC-based optimization framework is formulated. Besides, the results related to the application of the MPC to real case studies are presented, integrating the effects of static and dynamic weighting factors on the microgrid operation.

In this paper, a comprehensive decision support tool based advanced monitoring system is developed to support transition to smart greenhouses for sustainable and clean food production. The decision framework aims to optimally control and manage the microclimate environments of smart connected greenhouses, where each greenhouse is defined as a self-water producing through an enhanced water desalination process. The main advantage of the current approach lies in the ability of the greenhouses to produce their water loads locally. This paper aims to develop an efficient decision tool able of performing specific monitoring and control functionalities to optimize the operation of the greenhouses where the aim is the energy and water savings. A decision model is implemented for the precise regulation and control of the indoor microclimate defining the optimal growth conditions for the crops. Furthermore, a predictive algorithm is developed to simulate in real time the operation of the greenhouses under various conditions, to assess the response of the system to storage dynamics and renewable sources, as well to control the complex indoor microclimate, energy and water flows, as well to optimize the crops growth. The developed tool is tested through a case study where the influences of climate data on the operation of the whole network are analyzed via numerical results.

The authors frame greenhouse operation as a Controlled Environment Agriculture (CEA) challenge involving multiple interdependent targets: air temperature and humidity, CO₂ enrichment, photoperiod-constrained lighting, and irrigation under dynamic and limited energy availability. We propose a knowledge-driven, multi-objective Model Predictive Controller whose cost function integrates expert priorities elicited via an online Analytic Hierarchy Process (AHP) survey; these AHP-derived weights parameterize the controller’s objectives and are solved over two 72 h seasonal episodes, so the MPC can anticipate renewable availability and coordinate HVAC, (de)humidification, CO₂ dosing, LED lighting, and irrigation alongside dispatch from photovoltaic and wind sources, battery storage, and the grid. By embedding the physical interdependence of climate variables directly into the decision layer, the controller schedules energy-intensive actions around renewable peaks and avoids counterproductive actuator conflicts. Seasonal case studies (summer/high solar and winter/low solar) demonstrate robust performance: temperature tracking errors of SMAPE 2.25%/3.05% and CO₂ SMAPE 3.72–3.92%; humidity control with SMAPE 7.04–8.56%; lighting and irrigation following setpoints with low NRMSE (0.08–0.14). Summer energy was 59% renewable; winter was only 13%, increasing grid reliance to 77.5% (peaks: 4.57 kW/6.92 kW for 197.7/181.5 kWh). Under water or energy scarcity, the controller degrades gracefully, protecting high-priority agronomic variables while allowing bounded relaxation on lower-priority targets. This expert-informed, predictive, and resource-aware orchestration offers a scalable route to precision greenhouse control within the food–water–energy nexus.