In our rapidly evolving world, the demand for electricity has surged due to its vital role in driving national development across various sectors. Accurate forecasting of electricity consumption is essential for efficient energy resource planning and management.
The Electricity Consumption Analysis and Prediction System leverages a comprehensive dataset collected from the Grid Office in Baneshwor. Using advanced machine learning algorithms — including K-Nearest Neighbors (KNN), Linear Regression, and XGBoost — the system predicts electricity consumption with high accuracy.
After meticulous data cleaning, feature engineering, and integration, XGBoost emerged as the optimal model, achieving excellent performance metrics:
- Mean Squared Error (MSE): 0.03
- Root Mean Squared Error (RMSE): 0.18
- R² Score: 0.95
The system visualizes both analyzed data and model predictions through intuitive charts and graphs, providing users clear insights into consumption trends.
Predictions are available on monthly, daily, and yearly bases specifically for the Baneshwor area, enabling better planning and decision-making.
- Data cleaning and preprocessing of electricity consumption data
- Feature engineering for improved model performance
- Implementation of KNN, Linear Regression, and XGBoost algorithms
- Visualization of electricity consumption trends and model predictions
- Multi-level forecasting: daily, monthly, and yearly predictions
- User-friendly interface using Streamlit (or specify your frontend tech)
Thank you for exploring the Electricity Consumption Analysis and Prediction system! 📞 Contact For any questions or collaboration opportunities, reach out at: pawesha24@gmail.com
Thank you for exploring the Electricity Consumption Analysis and Prediction system!