AI-Based Solar Tracking Systems for Enhanced Energy Capture

An intelligent solar racking system for optimizing power generation in modular solar systems. The system uses sensors throughout the racking frame to monitor parameters like voltage, temperature, irradiation, etc. for each module and the frame. A computing device analyzes the sensor data to determine module and system operations. It can detect module issues, optimize electrical configuration, and provide feedback to systems. The sensor-based monitoring allows proactive maintenance, isolation of faulty modules, voltage adjustments, and big data analysis.

Source

A machine-learning-based photovoltaic power generation control system that collects real-time voltage and power data from photovoltaic modules, learns the data through a machine learning platform, and controls the modules based on extracted control information to optimize power generation. The system analyzes connected devices in real-time and performs modeling for various service functions, enabling real-time control of the photovoltaic power generation system.

Source

Solar energy tracking method that improves accuracy through deep learning. The method employs machine learning algorithms to enhance the traditional solar tracking system's ability to maintain optimal orientation and position. The system uses data acquisition equipment to continuously monitor the solar panel's position and orientation, while the control device processes this data to automatically adjust the tracking parameters. This approach enables more precise control of the solar panels, particularly under challenging environmental conditions like weather variations and shading.

Source

Predictive tracking system for photovoltaic power systems that optimizes panel alignment based on real-time weather conditions. The system employs a neural network to analyze sky images and solar position data to predict the optimal tracking angle for maximum energy production. The system continuously monitors cloud cover and adjusts the tracking position to avoid excessive movement during periods of high cloud cover, while maintaining optimal performance during periods of clear sky. This approach enables maximum energy production through predictive tracking, rather than relying solely on traditional solar position calculations.

Source

A solar tracking system that optimizes energy capture through a machine learning-based performance model that dynamically adjusts panel orientation in response to changing weather conditions. The system incorporates a global performance model that incorporates weather forecasts, topography data, and historical performance metrics, and continuously updates its parameters through machine learning algorithms. The model optimizes panel orientation for both optimal tracking performance and reduced shading, with the ability to switch between tracking and backtracking modes. This approach enables the system to adapt to varying solar radiation patterns and topographical conditions, maximizing energy output while minimizing energy losses.

Source

Solar power generation system that optimizes solar tracking using artificial neural networks. The system comprises a plurality of photovoltaic panels arranged in a plurality of columns and rows; Group tracking control units that are physically connected to the solar panels to provide power for the rotational motion of each of the solar panels; and a central control server for controlling the rotational movement by transmitting a control command to each of the group tracking controllers.

Source

A solar tracking system that optimizes energy output by independently adjusting each row of solar panels based on a performance model that accounts for local weather conditions, topography, and photovoltaic technology spectral response. The system generates performance models for each row using weather data and spectral response characteristics, and updates these models through machine learning algorithms to continuously improve tracking efficiency.

Source

A solar power generation system that optimizes panel positioning through machine learning-based tracking. The system employs AI algorithms to predict the sun's trajectory and position, and integrates with a microcontroller to control the solar panels' orientation. The system achieves accurate alignment through real-time sun position prediction, leveraging GPS data and sensor measurements. This enables continuous optimization of panel positioning despite the dynamic sun movement, resulting in improved energy output.

Source

A solar tracking system that optimizes energy generation by dynamically adjusting solar panel orientation based on environmental conditions. The system includes sensors to monitor irradiance and electrical current, and a controller that analyzes data to detect persistent cloudy conditions and shade patterns. When conditions are favorable, the system tracks the sun's position to maximize energy generation. When conditions are unfavorable, the system adjusts panel orientation to optimize energy capture from diffuse light. The system also learns and adapts to local conditions through machine learning algorithms, enabling continuous optimization of energy generation.

Source

A method for optimizing solar energy production in non-ideal weather conditions using machine learning algorithms to analyze sky image data and predict diffuse irradiance levels. The method involves dividing the sky into sectors, determining diffuse irradiance levels for each sector using machine learning regression models, and adjusting solar photovoltaic modules based on the predicted diffuse irradiance levels. The method can be implemented using a network of sky cameras and pyranometers to capture image data and measure irradiance levels.

Source

A self-powered solar tracking system that optimizes energy production by dynamically adjusting the angle of solar modules based on real-time current monitoring and machine learning predictions of shading patterns. The system uses a tracker controller that receives current data from multiple PV strings, measures the tilt angle, and applies machine learning algorithms to determine shading patterns and optimize module positioning. The controller can also prioritize maximum power output over shading avoidance.

Source

Intelligent sun tracking for precise solar monitoring. The method utilizes a combination of advanced sensors and machine learning algorithms to achieve accurate solar position determination. The system integrates multiple sensors, including photodiodes, GPS, and magnetometers, to provide comprehensive data. The sensors continuously monitor the sun's position and orientation, while the machine learning algorithm analyzes this data to calculate the solar's precise location. This enables highly accurate solar tracking, particularly in applications where precise positioning is critical, such as solar energy systems.

Source

Smart solar tracking system for photovoltaic power generation that enables rapid and precise alignment of solar cells. The system employs a novel tracking mechanism that uses a specially designed solar tracking device with a unique optical configuration. When sunlight hits the device, it focuses onto a precisely positioned photodiode along its edge, allowing for instantaneous detection of solar cell alignment. The system achieves this through a patented quadrangular pyramid design with four sensing panels that converge at the focal point, enabling precise tracking of solar cell alignment. The device automatically adjusts its position based on the detected alignment, eliminating the need for manual tracking. This approach enables rapid response to changing solar conditions, improving overall system efficiency and reliability.

Source

A solar tracking system that enables efficient energy generation by dynamically adjusting the orientation of solar panels to track the sun's movement throughout the day. The system employs an asymmetrical mounting design that allows the panels to be positioned at different angles relative to the vertical axis, eliminating the need for traditional fixed mounting structures. This enables the panels to capture more solar radiation by tracking the sun's position, resulting in higher energy production compared to traditional fixed mounting systems. The system can be configured for either photovoltaic (PV) panels or heat collection applications, and its adjustable mounting design enables precise control over the solar panel's orientation.

Source

A smart solar system that enables efficient electricity generation by tracking three-dimensional solar panels through its mobile mechanism. The system features a modular design with 8-12 blade configurations, a 2-part vertical swing mechanism, and a polyamide castermid for blade movement. The mechanism enables continuous blade opening and closing while maintaining optimal light transmission and panel efficiency. The system includes automated blade cleaning and protection features, as well as remote monitoring capabilities through Ethernet/Internet connectivity.

Source

Solar tracking system for photovoltaic panels that maintains optimal energy collection angles without external power sources. The system employs a novel tracking mechanism that periodically rotates solar panels to maintain their normal orientation relative to the sun's rays. The rotation is achieved through a mass-based positioning system that precisely controls the tilt angle between the tracking arm and the solar panel's normal plane. This approach ensures that the solar panels capture 99% of available solar energy regardless of the sun's position, even in locations with irregularly varying daylight patterns. The system is designed to operate independently of the internet and is particularly suitable for critical infrastructure and renewable energy applications.

Source

A solar tracking system that achieves maximum energy production through intelligent sun tracking without traditional radiation sensors. The system employs a fuzzy logic control method that calculates the optimal solar path using latitude, longitude, and time parameters, while simultaneously determining the optimal tilt angle. This approach enables precise tracking of the sun's position and orientation, allowing the system to maintain optimal alignment regardless of weather conditions.

Source

A solar tracking and monitoring system that maximizes energy collection while minimizing system costs. The system employs a novel tracking configuration that enables optimal alignment of solar panels with the sun's movement, thereby increasing energy capture. The system's monitoring capabilities provide real-time data on panel performance, enabling proactive maintenance and optimization. The configuration achieves higher energy collection efficiency through advanced tracking algorithms and optimized panel positioning, while eliminating unnecessary tracking components that were previously required to achieve similar performance.

Source

A smart solar tracking system that optimizes solar panel performance through AI-driven automation. The system employs a dual-axis tracking mechanism that continuously adjusts the solar panel's position to maximize energy production. The system's AI algorithm continuously monitors solar radiation levels and adjusts the panel's orientation to optimize energy generation. This intelligent approach enables the system to adapt to changing solar conditions, ensuring maximum energy output even during periods of low sunlight.

Source

Estimating solar radiation for photovoltaic energy production using a wide-angle camera and a convolutional neural network (CNN) that processes images to predict energy output. The method involves capturing images with a fisheye camera, segmenting the sky into cloud types, and training a CNN to estimate solar radiation parameters. The CNN can be used to predict energy output at the time of image capture or forecast future energy production based on a sequence of images.

Source