Four breakthrough fronts driving next-generation solar-assisted greenhouses

Energy management systems (EMS) enhanced with AI are emerging as the backbone of solar-assisted greenhouse operations. These systems coordinate energy inputs from PV/T modules, batteries, PCMs and grid electricity to ensure stable operation. EMS platforms allow greenhouses to shift from simple energy consumers to dynamic energy prosumers capable of balancing energy generation and demand.

Researchers warn that conventional greenhouse technology is no longer sufficient to deliver resilient, low-carbon agriculture amidst intensifying climate pressures. The latest evidence points to AI-driven solar solutions as a rapidly expanding field capable of transforming energy use, crop environments and long-term agricultural sustainability.

The study, titled Artificial Intelligence in Solar-Assisted Greenhouse Systems: A Technical, Systematic and Bibliometric Review of Energy Integration and Efficiency Advances and published in Technologies, examines 115 indexed documents, of which 79 met full inclusion criteria through a PRISMA-based screening. Their findings reveal fast-growing academic and industrial interest, technological breakthroughs, and persistent structural gaps that must be addressed before solar-AI greenhouses achieve commercial, global-scale deployment.

Global momentum builds behind AI-optimized solar agriculture

The research shows a steep climb in publications since 2019, with Engineering, Energy and Computer Science dominating contributions. China, India, Saudi Arabia and the United Kingdom emerge as the most active regions, signaling accelerating innovation across diverse climates and agricultural economies. The authors argue that interest has grown as solar-assisted greenhouses move from experimental prototypes into promising solutions for regions facing heat stress, water shortages and rising energy costs.

The review identifies four central research fronts driving the current wave of development. The first centers on thermal modeling and the optimization of energy flows between solar collectors, greenhouse structures and plant environments. Researchers are increasingly deploying supervised machine learning and deep learning systems to model temperature, humidity and solar radiation patterns with greater accuracy. These systems help predict microclimate behavior and reduce energy expenditure while improving internal environmental stability.

The second research front focuses on predictive control and microclimate automation. Model predictive control, deep reinforcement learning and intelligent rule-based systems are emerging as strong candidates for regulating ventilation, shading, irrigation and heating in real time. AI-driven controllers can respond to external climate fluctuations far faster than traditional static controls, allowing crops to benefit from more precise environmental management. Studies show that these advanced controllers reduce thermal swings, optimize ventilation and improve energy efficiency across multiple scenarios.

The third front involves integrating photovoltaic-thermal (PV/T) technology and phase change materials (PCMs). These hybrid systems capture heat and electricity simultaneously while storing excess thermal energy for later use. The review reports strong performance benefits when PV/T arrays are paired with PCMs, including improved heat recovery, longer energy retention periods and overall better thermal buffering. Such systems are beginning to push greenhouse design toward net-zero or near-zero-energy operation, particularly in sun-rich regions.

The fourth front highlights sustainability and agrivoltaics, an emerging field where crop cultivation and solar electricity generation operate side by side. Evidence cited by the study shows that agrivoltaic systems can reduce water consumption by up to 40 percent, stabilize crop temperatures under extreme heat and increase climate resilience for sensitive crops. The shading created by solar panels can also reduce evapotranspiration, contributing to water conservation in arid climates.

Together, these four lines of research demonstrate a coordinated global effort to make controlled-environment agriculture both highly productive and low in environmental impact.

AI models reshape forecasting, energy optimization and microclimate control

AI, especially machine learning and deep learning, is improving solar forecasting, energy optimization and predictive environmental control. Solar-driven systems depend heavily on accurate forecasting because irradiance varies by season, geography and daily weather conditions. The authors note that neural networks, long short-term memory models and hybrid regression techniques are delivering stronger forecasting accuracy for both short-term and long-term solar availability. Improved forecasting allows better energy planning, reduces waste and supports the integration of storage components such as PCMs.

Predictive control systems are also advancing rapidly. Instead of pre-programmed schedules, AI-driven controllers learn from historical datasets and current conditions to anticipate system behavior. Deep reinforcement learning, an emerging technique in greenhouse automation, enables controllers to learn optimal strategies for regulating ventilation, heating and shading while minimizing energy consumption. The review shows that these methods significantly outperform traditional PID controllers in both stability and energy savings.

Energy management systems (EMS) enhanced with AI are emerging as the backbone of solar-assisted greenhouse operations. These systems coordinate energy inputs from PV/T modules, batteries, PCMs and grid electricity to ensure stable operation. EMS platforms allow greenhouses to shift from simple energy consumers to dynamic energy prosumers capable of balancing energy generation and demand.

The review also demonstrates that AI-augmented sensing systems are becoming more sophisticated. IoT networks embedded with temperature, humidity, CO₂ and soil moisture sensors feed real-time data into AI models, creating highly responsive greenhouse ecosystems. As sensors become cheaper and more accurate, large-scale data acquisition supports increasingly precise AI-based climate control.

Growing interest in net-zero-energy greenhouses is driving deeper integration between AI algorithms, renewable energy systems, and emerging materials such as PCMs. Several studies cited in the review show that solar-assisted greenhouses using AI-driven controllers achieve more stable microclimates, faster recovery from adverse conditions and reduced dependency on fossil-based heating.

Despite these advances, the researchers warn that most AI-solar systems remain limited to small-scale prototypes or controlled experimental conditions. Commercial-scale deployments are rare, and many AI systems struggle when exposed to the variability of real-world agricultural environments.

Major technical gaps persist as solar-AI solutions move toward commercialization

The authors also bring to light several structural gaps that must be addressed before solar-assisted AI greenhouses are fully viable at commercial scale. One major challenge lies in the absence of standardized performance metrics. Different studies use varied indicators for energy efficiency, water savings, climate stability and crop productivity, making cross-comparison difficult. Without standardized baselines, policymakers and investors lack the tools needed to evaluate competing designs or measure long-term returns.

Another gap involves limited integration between AI models and physics-based tools such as computational fluid dynamics (CFD), thermodynamic simulations and digital twins. While AI excels at pattern recognition and prediction, physics-based models provide critical insight into heat transfer, airflow and material behavior. The authors argue that hybrid AI-physics models could deliver more robust forecasting and control strategies, especially under extreme weather conditions or system failures. Current research rarely bridges these domains.

The review also notes a shortage of techno-economic, environmental and life-cycle assessments. Although AI-solar systems often show strong performance benefits, there is insufficient evidence on long-term economic feasibility, maintenance requirements, carbon payback periods and return-on-investment profiles for commercial farms. Without these assessments, adoption remains slow.

Data quality and sensor reliability remain major barriers as well. Greenhouse environments often involve high humidity, dust, pests and mechanical vibration, all of which can degrade sensor performance. AI systems dependent on continuous sensing may experience rapid accuracy loss if sensor networks falter.

The authors caution that socioeconomic and policy dimensions are significantly underexplored. Most existing research focuses on technical optimization, even though farmers’ adoption decisions depend heavily on cost, training, labor requirements and local energy regulations. The lack of policy-oriented studies limits the ability of governments to support widespread deployment.

To advance the field, the authors recommend several key actions:

• conducting full-scale field trials across diverse climates
• creating standardized performance measurement frameworks
• integrating AI with digital twins and CFD models
• expanding research into policy, economic feasibility and user adoption
• increasing cross-disciplinary collaboration between engineers, data scientists and agronomists