US-20260126768-A1 - AI-Driven Solar Panel System with Dynamic Multi-Factor Adjustment and Automated Maintenance for Maximized Energy Output

Abstract

The present invention relates to an AI-driven system for optimizing the energy capture of renewable energy farms, including solar farms, wind farms, and ocean current farms. The system utilizes artificial intelligence (AI) to monitor real-time environmental data, such as solar irradiance, wind speed, ocean current speed, temperature, and humidity. The AI dynamically adjusts the operational parameters of energy capture devices—solar panels, wind turbines, and ocean current turbines—to optimize energy output in varying conditions. The system integrates predictive environmental data, such as weather forecasts and tidal patterns, to proactively adjust energy capture settings in advance of changes in environmental conditions.

Inventors

• Alexander Davis

Assignees

• Alexander Davis

Dates

Publication Date

20260507

Application Date

20241103

Claims (10)

1. 1 : An AI-driven renewable energy system comprising: a. A plurality of energy capture devices selected from the group consisting of solar panels, wind turbines, and ocean current turbines; b. An artificial intelligence (AI) system comprising an application-specific integrated circuit (ASIC) for an artificial neural network connected to the computer memory device, the ASIC comprising: a plurality of neurons organized in an array, wherein each neuron comprises a register, a processing element and at least one input, and a plurality of synaptic circuits, each synaptic circuit including a memory for storing a synaptic weight, wherein each neuron is connected to at least one other neuron via one of the plurality of synaptic circuits configured to monitor environmental data in real-time, including but not limited to solar irradiance, wind speed, ocean current speed, temperature, humidity, and salinity; c. A mechanical adjustment subsystem operatively connected to the AI system, wherein the AI system dynamically adjusts the operational parameters of the energy capture devices, including solar panel tilt, wind turbine yaw and blade pitch, and ocean turbine angle and blade pitch, to optimize energy capture under varying environmental conditions.
2. 2 : The system of claim 1 , further comprising a predictive maintenance system that continuously monitors the operational status of the energy capture devices by analyzing sensor data related to vibration, temperature, wear, and output efficiency, and wherein the AI system predicts maintenance needs based on patterns of operational data.
3. 3 : The system of claim 1 , wherein the AI system is configured to integrate predictive environmental data such as weather forecasts, tidal movements, and seasonal patterns, and dynamically adjusts the energy capture devices in anticipation of changes in sunlight, wind speed, and ocean current strength to maintain optimal energy production.
4. 4 : The system of claim 1 , further comprising an energy storage subsystem operatively connected to the AI system, wherein the AI system manages the storage of excess energy produced during periods of peak generation, and the discharge of stored energy during periods of low environmental energy availability or high energy demand.
5. 5 : The system of claim 1 , wherein the AI system includes a feedback loop with human experts such as electrical engineers or system designers, allowing for continual refinement and improvement of the energy optimization algorithms based on expert input and operational feedback.
6. 6 : The system of claim 1 , wherein the energy capture devices further include dual-sided solar panels with integrated mirrors, and wherein the AI system dynamically adjusts both the positioning of the solar panels and the angles of the mirrors to maximize light reflection and energy absorption.
7. 7 : The system of claim 1 , wherein the AI system is configured to detect obstructions or performance-degrading conditions such as shading, debris, or biofouling, and automatically triggers maintenance actions, including but not limited to cleaning mechanisms or adjustments to avoid the obstruction and restore optimal energy capture.
8. 8 : The system of claim 1 , wherein the AI system manages the integration of the renewable energy farm with an external energy grid, dynamically balancing the supply of generated energy to the grid and the use of energy storage systems to maintain grid stability and minimize fluctuations in energy supply.
9. 9 : The system of claim 1 , wherein the AI system adjusts operational parameters of energy capture devices based on location-specific environmental factors, such as geographic positioning, altitude, proximity to bodies of water, and local climate patterns, to maximize energy yield across different regions and weather conditions.
10. 10 : The system of claim 1 , wherein the AI system is configured to provide energy yield predictions by analyzing historical environmental data and performance metrics, thereby enabling future energy production planning and optimization of storage and distribution strategies.

Description

BACKGROUND OF THE INVENTION Field of Invention The present invention relates to the field of solar energy systems and, more specifically, to an artificial intelligence (AI)-driven solar panel system for residential and commercial solar farms. The system is designed to dynamically adjust the orientation, positioning, and operational parameters of solar panels based on real-time environmental data and predictive analytics. Additionally, the invention incorporates a feedback loop with human engineers or designers to refine system performance and integrates autonomous maintenance features to optimize long-term efficiency and reliability. The invention addresses the challenges of maximizing solar energy output under varying conditions such as weather changes, shading, and seasonal shifts. BRIEF SUMMARY OF THE INVENTION The present invention discloses an advanced artificial intelligence (AI)-driven solar panel system designed to maximize the efficiency of solar energy capture in residential and commercial solar installations. The system utilizes AI algorithms that analyze real-time environmental data, such as solar irradiance, weather conditions, and panel performance, to dynamically adjust the orientation and configuration of solar panels. This real-time optimization ensures that the panels are always positioned to capture the maximum amount of solar energy throughout the day and across different seasons. The system incorporates a feedback loop between electrical engineers or system designers and the AI, allowing for continual refinement and improvement based on human expertise. The invention also features autonomous maintenance capabilities, such as self-cleaning functions and predictive maintenance, which proactively address potential inefficiencies and ensure long-term reliability of the solar panels. By combining AI-driven optimization with autonomous maintenance and human feedback, the invention significantly improves energy yield, reduces operational costs, and increases the lifespan of solar energy systems in various environmental conditions. BRIEF DESCRIPTION OF THE FIGURES FIG. 1: step by step of the invention's process for an AI-driven system for optimizing the energy capture of renewable energy farms. DETAILED DESCRIPTION The present invention is an advanced AI-driven solar system design and optimization platform, engineered to create comprehensive, site-specific solar energy solutions tailored to the unique characteristics of each installation site. The invention is applicable to both residential and commercial solar energy setups, facilitating the design of custom solar energy systems by considering numerous factors, including site orientation, geographical location, energy requirements, and environmental conditions. Comprehensive AI-Based Design System At the core of this invention is an AI-driven design system that automates the initial planning and configuration of a solar installation. When provided with inputs such as images of the property, site orientation (e.g., north, south), and location data, the AI system analyzes the specific characteristics of the site and determines the optimal solar solution. Based on factors such as sunlight exposure, shading, climate, and the energy needs of the site, the AI generates a tailored solar design, including recommendations for the number, type, and positioning of solar panels, as well as associated components like inverters and battery systems. Customized Panel Selection and Orientation The AI system selects panel types based on the characteristics and intended function of the installation. For example, it may suggest dual-sided panels with mirror integration for locations with ample reflected sunlight, or stationary, fixed-angle panels for locations where panel movement is unnecessary. In cases where dynamic orientation is beneficial, the AI may recommend panels with tracking capabilities to follow the sun's path, optimizing energy capture throughout the day. The orientation recommendations are only one component in the broader system design, balancing other factors like energy storage and future scalability. Optimized Layout and Positioning Once the AI determines the appropriate types of solar panels, it then calculates the optimal positioning for maximum efficiency. Taking into account shading from nearby structures, terrain irregularities, and seasonal sunlight variations, the AI configures the layout of the installation to maximize sunlight capture for each panel. This layout adapts dynamically based on the AI's real-time data analysis, ensuring that energy capture remains efficient across different times of the day and year. Integration of System Components Beyond panel selection and layout, the AI system incorporates additional energy management components. It designs the system to integrate inverters, energy storage solutions, and grid management capabilities based on the anticipated energy demands and storage requirements of the site. The AI ma