Radiant Future AI 🌞

Radiant Future AI is an innovative, AI-powered solution designed to deliver personalized recommendations for solar panel installations. At its core lies a state-of-the-art machine learning model that predicts solar panel requirements with exceptional accuracy. This feature ensures precise, reliable, and actionable insights, making solar energy adoption easier and more effective.

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🌟 Core Features

1. Solar Panel Requirements Prediction

• Advanced Machine Learning Model:
  Utilizes Random Forest Regression (RFR), chosen for its superior performance after rigorous comparisons with models like Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). The RFR model ensures high accuracy and robustness in predictions.
• Dataset:
  Trained on a curated dataset featuring historical solar energy production, geographical information, and weather patterns. Data preprocessing included normalization, handling missing values, and feature selection to enhance model performance.
• Model Tuning:
  Hyperparameter tuning via grid search optimized parameters like the number of trees and depth to boost efficiency.
• Evaluation Metrics:
  The model was evaluated using metrics such as R² score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) to meet and exceed industry benchmarks.

2. Energy Consumption Prediction

A system that forecasts energy consumption based on historical data and user inputs, enabling efficient energy planning.

3. Weather-Driven Insights

Real-time weather data integration provides actionable insights into solar energy generation, helping users optimize energy usage and panel positioning.

4. ROI Analysis

A comprehensive tool offering visualized insights, such as daily energy generation, monthly energy consumption, and installation costs, showcasing the financial benefits of investing in solar energy.

5. Installation Pricing

An intelligent pricing calculator that provides accurate cost estimates for solar panel installations based on multiple parameters.

6. Carbon Footprint Reduction Estimation

Estimates the reduction in carbon footprint achieved through solar energy adoption, promoting sustainable energy practices.

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💻 Technologies Used

• Machine Learning Models:

  • Random Forest Regression (RFR)
  • Stochastic Gradient Descent (SGD)
  • Multi-Layer Perceptron (MLP)

• Evaluation Metrics:

  • R² score
  • Mean Absolute Error (MAE)
  • Root Mean Squared Error (RMSE)

• Frontend: HTML, CSS, JavaScript

• Backend: Node.js, Firebase Authentication, MongoDB

• APIs Used:

  • Open Weather API: For real-time weather data
  • Geocode API: For geographical data
  • Peak Sunny Hours by NASA API: To predict solar energy potential

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🏗️ Project Architecture

Frontend

Developed with HTML, CSS, and JavaScript, ensuring a responsive and interactive user experience.

Backend

Built with Node.js, the backend handles secure and efficient user data interactions. Firebase Authentication manages user access, while MongoDB serves as the database for storing user data and project details.

APIs

Real-time data fetching enhances prediction accuracy and user experience by integrating weather, location, and solar energy data.

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🚀 How to Run

1. Clone the repository:
   Open your terminal and run:

   git clone https://github.com/soumya-1712/radiant-future-ai.git
   

2. Navigate to the project directory:

   cd radiant-future-ai
   

3. Install dependencies:

   npm install
   

4. Run the application:

   npm start
   

5. Run the application:

   http://localhost:3000
   

🤝 Contributors

• Aman Raj
• Soumya Dhakad
• Hardik Kanzariya
• Priyanshu Pandey