🌞 Dual Axis Solar Tracker Power Prediction

Tech Stack

• Python
• Arduino
• ESP32
• Random Forest
• Machine Learning
• IoT

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📖 Description

This project implements a dual-axis solar tracker system with integrated power prediction capabilities. It uses advanced IoT hardware, machine learning models, and data analysis tools to optimize solar energy harvesting.

Key Components:

1. IoT and Sensors:

   • DHT11: Measures temperature and humidity.
   • BH1750: Measures light intensity.
   • MPU6050: Monitors orientation and movement.
   • ACS712: Tracks current for power monitoring.

2. Embedded Systems:

   • Arduino and ESP32: Used for hardware control and data collection.

3. Machine Learning Models:

   • Linear Regression
   • Random Forest Regressor
   • Decision Tree Regressor
     These models predict solar power generation based on historical and real-time data.

4. Weather Integration:

   • Weather data from OpenWeatherMap API enhances prediction accuracy.
   • Historical weather data from Antwerp used for power prediction experiments.

5. Data Processing and Analysis:

   • Files converter.py and final.py:
     • converter.py: Handles dataset processing, Random Forest model training, and feature extraction for IoT integration.
     • final.py: Merges weather and power data, builds regression models, and provides detailed data analysis and visualization.

6. Visualization:

   • Data preprocessing, visualization, and analysis using:
     • Pandas
     • Matplotlib
     • Seaborn

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🚀 Features

• Real-time sensor data acquisition.
• Dual-axis solar tracker for optimized sunlight absorption.
• Machine learning-based solar power prediction.
• Weather data integration for enhanced prediction.
• Data visualization for daily solar power trends.

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📂 File Descriptions

• File converter.ipynb:

  • Data processing and Random Forest model training.
  • Generates feature importance and exports model headers for embedded systems.

• File converter.ipynb:

  • Merges historical weather and power datasets.
  • Builds regression models and evaluates performance.
  • Visualizes weather conditions and solar power trends.

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🛠️ Installation and Usage

1. Clone the repository:

   git clone https://github.com/kavindu26589/Dual-Axis-Solar-Tracker-Project.git