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Description
This paper introduces a novel agrivoltaic system designed to maximize crop yield. The proposed solution integrates two main components: a predictive model and an adaptive structure. The predictive model, built on algorithms, can forecast solar power generation and crop yield based on historical and real-time data inputs such as solar radiation, temperature, and crop-specific parameters. This enables a data-driven approach to designing efficient agrivoltaic systems, thereby harmonizing the coexistence of solar energy production and agriculture.
The adaptive structure is an innovative design feature allowing solar panels' angles to be adjusted according to changing environmental conditions. This adaptive feature ensures optimal sunlight distribution to crops underneath while maximizing solar energy capture. Our proposed system dynamically balances the competing needs of energy production and agricultural output in real-time.
Lastly, the paper discusses an applied case study demonstrating the implementation and benefits of such an agrivoltaic project. This work serves as a blueprint for future sustainable projects, offering insights into the transformative potential of intelligently designed agrivoltaic systems that prioritize agricultural yield. The findings offer promising implications for the future of sustainable agriculture and renewable energy sectors.
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| Keywords | Agrivoltaic System, Predictive Model, Adaptive Solar Panel Angle, Energy-Agriculture Balance |
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