At the core of a smart greenhouse lies a network of sensors and actuators responsible for monitoring parameters such as temperature, humidity, light intensity, and air quality. These data are transmitted in real time to a central hub, where they undergo initial preprocessing before being passed to the Energy Management System (EMS). The EMS analyzes environmental conditions alongside the growth requirements of plants and issues control commands to cooling, heating, lighting, and energy storage systems. This ensures optimal consumption and minimizes unwanted energy peaks. A distributed and modular architecture enables easy expansion if the greenhouse size increases or new sensors are added—without requiring a full system redesign.

Reliable and low-power communication layers such as LoRaWAN or NB‑IoT make it possible to scale the greenhouse system by connecting dozens or even hundreds of sensors with minimal energy consumption. Secure protocols like MQTT with TLS encryption protect against cyber threats and safeguard sensitive data. In cloud control centers, historical data are archived and made available for machine learning algorithms, enabling long-term trend analysis and continuous optimization of control strategies.

– Algorithms and Machine Learning for Consumption Optimization

Modern energy management systems go far beyond basic on/off rules—they leverage advanced algorithms based on prediction and multi-objective decision-making. For instance, a study published in MDPI presented a method that minimizes grid consumption while maximizing battery charge, optimizing several parameters simultaneously to achieve energy savings of 25–30%. This approach balances artificial lighting intensity, air temperature, and battery charge status to control grid usage and ensure stable plant growth conditions.

Reinforcement learning algorithms have recently gained popularity. These methods allow a control agent to learn optimal strategies through trial and error in a simulated environment. Field results show that reinforcement learning can reduce temperature and humidity fluctuations by up to 20% while improving energy efficiency by 10–15% compared to traditional methods. These systems also adapt to changing weather conditions and maintain high performance without the need for manual intervention.

– Emiliano Seri, Researcher at Sapienza University of Rome: “Integrating photovoltaic systems in greenhouses not only optimizes land use but also enables precise control and prediction of internal environmental conditions using graph neural networks.”

The integration of the Internet of Things (IoT) and cloud-based systems enables remote access and deeper analytics. Historical data stored in the cloud feed machine learning algorithms, which identify consumption patterns and predict future conditions. This process allows for the creation of detailed dashboards and automatic alerts, giving operators the ability to respond quickly to anomalies. Mobile apps also let users monitor greenhouse status at any time, adjust parameters, and receive both numerical and graphical reports.

– Kangqing Wang, Researcher at the University of Science and Technology of China: “The integration of IoT and machine learning in greenhouses enables precise environmental control, improving productivity and reducing resource waste.”

– Control Loop Optimization with Predictive Modeling

Model Predictive Control (MPC) is considered one of the most powerful methods for greenhouse management. It simulates near-future greenhouse behavior and solves a multivariable optimization problem to determine control commands. Research has shown that MPC can reduce energy consumption by 20–25% compared to traditional methods and respond more effectively to sudden changes in temperature or sunlight. MPC is especially effective during unstable seasons with fluctuating weather, helping maintain product quality.

In a practical implementation within a commercial greenhouse, the MPC algorithm was based on simplified dynamic models and 15-minute sensor data. It maintained ideal conditions—temperatures between 18–25°C and humidity levels of 60–70%—while reducing gas and electricity usage by 18% and 22%, respectively.

The use of lithium-ion or flow batteries alongside solar panels allows surplus energy to be stored and used during peak demand hours. A study in *Nature Scientific Reports* found that adding an energy storage system increased self-sufficiency from 43.43% to 69.45% in summer, and from 24.17% to 81.36% in winter. This significantly reduces strain on the power grid and the cost of emergency energy supply.

Smart demand scheduling can also shift non-essential loads to off-peak hours or draw from storage, depending on electricity pricing. This strategy not only lowers costs but also supports grid stability and enables participation in energy flexibility markets.

A Canadian study showed that greenhouses implementing smart energy management systems reduced their annual energy costs by up to 30%, achieving return on investment in under three years. This success is a driving force behind the rapid expansion of smart greenhouses across the Americas and highlights the efficiency of combining modern technologies for sustainable productivity.

Field trials in European greenhouses confirm that a well-balanced combination of sensors, predictive algorithms, and energy storage can raise the self-sufficiency rate to over 75%, virtually eliminating the risk of power outages. This breakthrough opens up new horizons for expanding smart agriculture and lowering operational costs in remote areas.