⚡ AI-Powered Hybrid Energy Source Predictor

An AI system that predicts and optimizes hybrid energy generation using solar and wind data. The system integrates machine learning models, a FastAPI backend, and a Streamlit dashboard to provide energy predictions, optimization, and explanations.

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🚀 Project Overview

Renewable energy systems often rely on multiple sources like solar and wind. This project builds an AI-powered hybrid energy prediction platform that: • Predicts solar power generation • Predicts wind power generation • Performs hybrid energy optimization • Recommends the best energy source • Provides AI explanation for decisions • Displays results through an interactive dashboard

🧠 System Architecture User Interface (Streamlit) ↓ FastAPI Backend API ↓ Machine Learning Models ↓ Hybrid Optimization Engine ↓ AI Advisor Explanation

This architecture follows real production ML system design where frontend and ML inference are separated.

🛠 Tech Stack

Programming • Python

Machine Learning • Scikit-learn • XGBoost • TensorFlow / LSTM

Backend • FastAPI • Uvicorn

Frontend • Streamlit • Plotly

Utilities • NumPy • Pandas • Joblib

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📂 Project Structure

AI-Powered-Hybrid-Energy-Source-Predictor │ ├── api │ ├── main.py │ └── routes.py │ ├── dashboard │ └── app.py │ ├── src │ ├── data_pipeline │ ├── models │ ├── optimization │ ├── llm_agent │ ├── rag │ └── utils │ ├── configs │ ├── data_config.yaml │ └── model_config.yaml │ ├── artifacts │ ├── solar_model.pkl │ └── wind_model.pkl │ ├── data │ ├── requirements.txt └── README.md

⚙️ Installation

Clone the repository pip install -r requirements.txt

▶️ Running the Project

1️⃣ Start Backend API uvicorn api.main:app --reload

API docs will be available at: http://127.0.0.1:8000/docs

2️⃣ Start Dashboard streamlit run dashboard/app.py

Dashboard will open at: http://localhost:8501

📊 Dashboard Features • Solar energy prediction • Wind energy prediction • Hybrid energy optimization • Recommended energy source • Energy comparison graph • AI explanation for energy selection

📈 Example Output Solar Power: 1255 kW Wind Power: 651 kW Total Energy: 1906 kW Recommended Source: Solar

🔮 Future Improvements

Planned upgrades for the system: • Real-time weather API integration • 24-hour energy forecasting • Battery storage optimization • AI chatbot energy advisor • Cloud deployment (AWS / Render)

🎯 Use Cases • Renewable energy management • Smart grid systems • Energy planning & optimization • AI-assisted renewable energy decisions

👨‍💻 Author

Priyanshu Kumar

Computer Science & AI/ML Enthusiast Focused on building real-world AI systems and energy optimization solutions.

⭐ If you like this project

Give it a star on GitHub ⭐