In the rapidly evolving field of renewable energy, agrivoltaics—the simultaneous use of land for both agriculture and solar photovoltaic power generation—has emerged as a promising approach to optimize land use and enhance sustainability. However, one of the significant technical challenges that has hindered the widespread implementation of agrivoltaic systems is accurately and efficiently calculating ground irradiance. Ground irradiance denotes the amount of solar energy reaching the crops beneath the solar panels, and precise estimation is crucial for predicting crop yields and optimizing panel placement. In an exciting breakthrough, a team of researchers led by Kurumundayil and colleagues has developed a fast and accurate framework for ground irradiance computations using advanced physics-informed deep learning models, setting a new standard for agrivoltaic system analysis.
Agrivoltaic systems consist of dual-function land areas where photovoltaic panels are installed at certain elevations above crop fields. While these panels generate electricity, they also shade the crops beneath, altering the light distribution and thus potentially affecting plant growth. Capturing the complex interplay of solar irradiance—both direct and diffuse—and shading is essential for balancing power generation with agricultural productivity. Traditional models for simulating ground irradiance often rely on complex ray-tracing algorithms or numerical solutions to radiation transfer equations, which while accurate, are computationally expensive and impractical for large-scale or dynamic system design.
The research team’s innovation lies in harnessing the power of physics-informed deep learning to quickly predict ground irradiance while maintaining high fidelity to physical principles. Physics-informed neural networks (PINNs) integrate physical laws directly into the architecture of deep learning models, enabling them to learn from both data and governing equations simultaneously. This contrasts with purely data-driven approaches that may lack generalizability or physical interpretability. By embedding the known physics of radiative transfer and shading within the network, the model ensures physically consistent outputs across diverse agrivoltaic configurations.
One of the standout features of this approach is the remarkable computational speed it achieves. While classical models might require hours of processing time for detailed simulations of irradiance distribution on a single day with specific weather and system setups, the PINN-based model produces results in seconds. This speed unlocks the potential for real-time optimization and adaptive control of agrivoltaic systems, a game-changer in operational deployment. Rapid predictions across various panel angles, heights, and spacings enable stakeholders to fine-tune installations for maximal energy yield without compromising crops.
Moreover, the physics-informed model is trained using a hybrid strategy that combines synthetic data generated from rigorous simulations with a curated set of empirical measurements from field experiments. This hybrid training compensates for the limited availability of ground-truth irradiance data typically encountered in agrivoltaic contexts. Consequently, the model demonstrates impressive robustness and generalizability across different climatic conditions, vegetation types, and system geometries, exhibiting reliable performance even under novel scenarios unseen during training.
Delving deeper into the technical workings, the PINN architecture incorporates governing equations describing solar irradiance as a function of panel geometry, solar position, atmospheric conditions, and bidirectional reflectance distribution functions (BRDF) of the ground surface. By constraining the neural network outputs to satisfy these equations, the model inherently respects conservation of energy and radiative transfer laws. This embedding effectively reduces the solution search space during training, improving convergence and eliminating physically impossible predictions—an issue common in purely empirical models.
Field validation experiments play a crucial role in substantiating the model’s efficacy. Kurumundayil et al. report comprehensive comparisons between model-predicted ground irradiance maps and sensor readings from agrivoltaic installations in diverse environments. These validations underscore the model’s accuracy across diurnal and seasonal cycles, capturing subtle variations induced by panel shading and diffuse skylight. The model’s adaptability extends to dynamic weather changes, which affect irradiance distribution and are notoriously difficult to capture with static or deterministic models.
Another important advancement facilitated by this work is the ability to handle complex system geometries beyond simple arrays. Many existing irradiance models falter when confronted with irregular or optimized panel arrangements designed to maximize both power and crop viability. The PINN framework’s flexibility allows it to incorporate arbitrary panel shapes, alignments, and non-uniform spacing, revealing nuanced shading patterns and optimizing trade-offs. This paves the way for highly customized agrivoltaic systems tailored to specific crop requirements and land constraints.
The implications of this breakthrough are vast. Agrivoltaics often suffers from a technological bottleneck due to the difficulty in predicting and managing light availability for crops under ever-changing environmental and structural settings. By providing a fast, reliable tool for irradiance simulation, the research team offers farmers, engineers, and policymakers an unprecedented capacity to design systems that boost both food and energy production sustainably. This could accelerate the adoption of agrivoltaics worldwide, especially in regions where land competition and climate stress pose serious challenges.
Furthermore, the approach exemplifies a broader paradigm shift in environmental modeling, where physics-informed deep learning bridges the gap between first-principles understanding and data-driven analytics. Such hybrid models can transcend the limitations of traditional methods that are either computationally prohibitive or overly reliant on sparse data. The success of this framework in agrivoltaics suggests potential applicability across other domains where complex light interactions impact ecosystem services, such as forestry, urban planning, and climate modeling.
The study’s release comes at a time of heightened urgency for integrated solutions to climate change, food security, and renewable energy. As global populations expand and arable land becomes scarcer, maximizing productivity per unit area gains paramount importance. Agrivoltaics offers a compelling synergy, but only if underpinning technologies for system design and management mature. The fast irradiance computation method developed by Kurumundayil and colleagues thus addresses a critical knowledge gap, enabling scalable and practical agrivoltaic deployment.
Looking ahead, the research team acknowledges future directions aimed at incorporating multiphysics phenomena such as microclimatic changes, evapotranspiration, and soil moisture dynamics within their model framework. Integrating these additional environmental factors could further enhance predictive capabilities, enabling holistic system optimization that accounts for the complex feedback loops between plants, solar panels, and the surrounding atmosphere. Such integrative models hold promise for designing next-generation agrivoltaic systems that are not only energy efficient but also climate resilient and agroecologically sound.
In conclusion, the fusion of physics-informed deep learning with agrivoltaic irradiance modeling represents a milestone in the quest for sustainable land use and renewable energy innovation. This breakthrough offers an elegant solution to the longstanding challenge of balancing solar energy harvesting with agricultural productivity. With its blend of computational efficiency, physical consistency, and robust performance, the new approach equips stakeholders with a powerful toolset to accelerate the agrivoltaic revolution. As renewable energy integrates ever more closely with agricultural landscapes, advances like this illuminate the path to a greener, more resilient future.
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Kurumundayil, L., Burkhardt, D., Gfüllner, L. et al. Fast ground irradiance computations for agrivoltaics via physics-informed deep learning models. Commun Eng 4, 173 (2025). https://doi.org/10.1038/s44172-025-00523-1
Tags: advanced computational models in agrivoltaicsagricultural productivity and solar poweragrivoltaics and solar energydual-use land systemsefficient irradiance estimation techniquesground irradiance calculationsinnovative agricultural technologieslight distribution in agrivoltaicsphysics-informed deep learningrenewable energy optimizationsolar panel crop interactionsustainable agriculture practices
