Predicting Wind Pressure on Solar Arrays with AI!

 

The growing interest in “PV+” development—where photovoltaic (PV) systems integrate with various infrastructures—demands innovative structural solutions. Among these, the long-span flexible PV structure stands out for its adaptability and efficient land use. However, due to its lightweight, low stiffness, extended span, and elevated installation, it faces intensified wind-induced vibrations. This paper explores the challenges posed by wind loads and presents a data-driven AI approach to overcome the limitations of conventional wind pressure measurement methods, especially in the context of wind tunnel testing.

Challenges in Wind Load Estimation for Flexible PV Structures

Wind pressure plays a crucial role in the structural integrity of large-scale, lightweight PV systems. The conventional methods, such as wind tunnel testing, suffer from spatial measurement limitations, particularly when capturing complex, fluctuating pressure distributions. These limitations significantly hinder the accuracy of load prediction models for flexible PV structures, especially in real-world environments. This study addresses the need for more accurate, high-resolution wind pressure estimation to ensure structural safety and optimal energy performance.

Fully Convolutional Networks (FCNs) for Spatial Pressure Prediction

To overcome the constraints of traditional measurement techniques, this research introduces a fully convolutional network (FCN) architecture tailored for predicting wind pressure on PV arrays. By leveraging the full spatial resolution of the input data and utilizing multi-scale features with skip connections, the FCN enhances the learning of localized and global wind pressure patterns. This approach enables the prediction of the complete wind pressure field using limited input from the first row of array data.

Model Performance and Validation Metrics

The predictive power of the FCN model is demonstrated through a rigorous evaluation of accuracy metrics. The model achieves a relative error of approximately 9%, while the correlation coefficient between predicted and measured values exceeds 0.95. Such results highlight the model’s effectiveness in generalizing wind pressure behavior across a large flexible PV surface, confirming its reliability for structural analysis and design applications.

Engineering Implications and Real-World Applications

The application of AI, particularly FCNs, in predicting wind pressure for PV structures has vast engineering implications. This approach can significantly reduce the reliance on extensive physical testing, lower costs, and shorten design cycles. Moreover, it opens the door for adaptive, real-time wind load monitoring and automated structural health assessment, ensuring the long-term safety and efficiency of solar energy infrastructures in diverse environments.

Future Research Directions in AI-Powered Structural Prediction

This study lays the groundwork for future exploration in AI-enhanced modeling of environmental loads on renewable energy structures. Further research could involve integrating temporal wind variations, extending the model to account for thermal effects, and adapting it for other types of lightweight renewable infrastructure. Moreover, hybrid AI models combining convolutional and recurrent networks could provide even more nuanced predictions in dynamic environments.

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