Title: Sun Tracking System Using Raspberry Pi or ESP32 Camera Module and Machine Learning for Solar Panels
Abstract:
This hypothetical research paper presents a novel approach to design and implement a sun tracking system for solar panels using a Raspberry Pi or ESP32 camera module and machine learning techniques. The proposed system aims to optimize the energy output of solar panels by automatically adjusting their orientation to follow the movement of the sun throughout the day. The system utilizes computer vision algorithms based on a pre-trained machine learning model to detect the sun’s position in captured images and subsequently control a servo motor to align the solar panel optimally.
1. Introduction:
The growing demand for renewable energy sources has intensified the need for efficient solar energy harvesting. Sun tracking systems enhance the overall efficiency of solar panels by dynamically adjusting their orientation to face the sun directly. This paper presents a cost-effective and scalable approach using Raspberry Pi camera and machine learning to autonomously track the sun.
2. Related Work:
Review existing literature on sun tracking systems, computer vision techniques, and machine learning applications in solar energy optimization. Identify gaps in the research and highlight the uniqueness of the proposed approach.
3. System Architecture:
Illustrate the high-level architecture of the sun tracking system, including components such as Raspberry Pi, camera module or ESP32 Camera, servo motor, and machine learning model. Describe how these components interact to achieve sun tracking.
4. Image Capture and Processing:
Detail the image capture process performed by the Raspberry Pi camera module at regular intervals (e.g., every 10 minutes). Explain the preprocessing steps to enhance image quality and isolate the sun from the background.
5. Machine Learning Model:
Describe the pre-trained machine learning model used for sun detection in the captured images. Clarify the choice of the model, its training process, and its effectiveness in accurately identifying the sun’s position.
You can find out an example Jupyter Notebook here https://github.com/dhirajpatra/jupyter_notebooks/blob/main/DataScienceProjects/image_processing/find_the_bright_spot.ipynb
6. Sun Position Calculation:
I have used mere CV2 and Gaussian method for the above example which trac the bright spot in an image. For our case it is sun ofcourse.
It also calculate the how many degree upward movement required. We can take the easy pattern by dividing the imaeg/photo equal four parts and to check how many degree the sun is in the image upward [x].
7. Servo Motor Control:
Here are the steps on how to move a servo motor fitted with a camera based on the microcontroller telling the servo which degree it needs to move to upward:
Attach the servo motor to the microcontroller.
Initialize the servo motor.
Create a variable to store the desired angle.
Read the desired angle from the microcontroller.
Move the servo motor to the desired angle.
Here is an example code that you can use:
import serv
def main():
# Attach the servo motor to the microcontroller.
servo_motor = servo.Servo(12)
# Initialize the servo motor.
servo_motor.initialize()
# Create a variable to store the desired angle.
desired_angle = 90
# Read the desired angle from the microcontroller.
desired_angle = input("Enter the desired angle: ")
# Move the servo motor to the desired angle.
servo_motor.write(desired_angle)
if __name__ == "__main__":
main()This code will first attach the servo motor to the microcontroller and initialize it. Then, it will create a variable to store the desired angle. The desired angle will be read from the microcontroller. Finally, the servo motor will be moved to the desired angle.
8. Experimental Setup:
Outline the hardware components used in the experimental setup, including the Raspberry Pi, camera ESP32 camera module, servo motor, and solar panel. Describe the environment and testing conditions.
We need to direct the tracker to move the row of panels.
By combining Raspberry Pi camera module and machine learning, this research paper presents a cost-effective and scalable solution for solar panel sun tracking. The proposed system’s potential benefits include increased solar energy harvesting efficiency, reduced energy costs, and a more sustainable approach to renewable energy utilization. With further research and development, this sun tracking system has the potential to contribute significantly to the transition towards greener energy sources.
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