Ai-driven biomimetic clustered photovoltaic system for enhanced solar energy generation and optimization

Abstract

There is disclosed an AI-driven biomimetic clustered photovoltaic (PV) system designed to maximize solar energy efficiency. Inspired by sunflowers, the system features autonomous intelligent PV clusters with radial and triangular panels that dynamically optimize sunlight absorption through biomimetic opening and closing mechanisms. Triaxial tracking and 360-degree rotation ensure precise sun tracking, while smart microcontrollers enable real-time weather monitoring and panel adjustment. The system integrates a self-sufficient cleaning and cooling system using rainwater and ambient moisture. Electrical performance sensors and AI-driven 360-dome cameras detect faults and enhance security. Clusters collaborate via extendable wings and are segmented for independent movement and optimal sun exposure. Scalable and adaptable, the system supports hybrid solar-wind energy harvesting and employs genetic algorithms for continuous learning and self-optimization across diverse environmental conditions. This approach enhances maintenance efficiency, adapts to environmental changes, and optimizes panel configurations for maximum energy production, surpassing traditional methods in complex environmental contexts.