This project is an AI-based system that predicts future energy consumption using machine learning techniques. It analyzes time-based patterns such as hour of the day and day of the week to forecast electricity usage.
The goal is to help optimize energy usage in smart cities, buildings, and industries.
Energy demand is often unpredictable, leading to:
- Power wastage
- High electricity costs
- Inefficient resource planning
- Increased carbon emissions
This project solves these issues by forecasting energy usage using AI.
- Predict future energy consumption
- Analyze usage patterns
- Reduce energy wastage
- Support smart energy management systems
- Google Colab
- Python
- Pandas
- Matplotlib
- Scikit-learn
- Joblib
-
Model Used: MLP Regressor (Neural Network)
-
Input Features:
- Hour of the day
- Day of the week
-
Output:
- Predicted energy consumption
AI-Energy-Forecasting/
│
├── energy_forecasting.ipynb
├── requirements.txt
├── README.md
├── outputs/
- MAE: (add your value here)
- Input: Hour = 14, Day = 2
- Output: Predicted Energy Value
- Open the notebook in Google Colab
- Run all cells step-by-step
- Train the model
- View predictions
- Smart Cities
- Energy Management Systems
- Power Grid Optimization
- Industrial Energy Monitoring
- Renewable Energy Planning
- Time-series based forecasting
- Neural network model
- Data visualization
- Model saving using joblib
- Beginner-friendly implementation
- Use real-world datasets
- Implement LSTM (Deep Learning)
- Build a web dashboard
- Add real-time prediction API
- Time-series data analysis
- Feature engineering
- Machine learning model training
- Model evaluation (MAE)
- Project deployment basics
Vaishnava Devi
Special thanks to my mentor for guidance and support.
✅ Completed 🚀 Ready for GitHub & LinkedIn showcase