AI-Powered Electricity Consumption Forecasting ⚡

Goal: Forecast electricity usage to support smart cities and climate tech.

🚀 How it Works

1. Data Loading: Real-world smart grid logs are loaded from energy.txt using Python.
2. Feature Engineering: Extracts hourly and daily patterns to understand human behavior.
3. AI Training: Uses a Multi-Layer Perceptron (MLP) Neural Network to learn usage trends.
4. Prediction Engine: A Flask API serves real-time energy forecasts.

🛠️ Tech Stack

• Languages: Python
• Libraries: Pandas, Scikit-learn, Flask, Joblib
• Dataset: energy.txt (Time-series energy data)

📂 Project Structure

• energy_forecasting.ipynb: The training process.
• app.py: The live prediction server.
• energy_forecast_model.pkl: The saved model ("Frozen Brain").
• energy.txt: Historical usage logs.