Forecasting energy demands accurately is essential for optimizing power distribution grids, reducing waste, and managing household utility costs efficiently.
This repository implements a Predictive Regression Model designed to analyze and forecast Household Electricity Consumption based on historical load data and associated environmental or temporal features. The project focuses on data preprocessing, feature correlation analysis, regression model training, and performance evaluation using standard statistical metrics.
- Data Preprocessing & Cleaning: Handling missing values, scaling features, and parsing temporal structures from historical energy data.
- Feature Engineering: Selection and transformation of key predictors influencing power consumption patterns.
- Regression Modeling: Implementation of regression baselines (e.g., Linear Regression, Ridge/Lasso, or Polynomial expansions) tailored for continuous target forecasting.
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Evaluation Metrics: Detailed analysis using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and
$R^2$ Score to validate predictive performance.
Ensure you have Python installed along with standard scientific computing and machine learning libraries:
pip install numpy pandas matplotlib seaborn scikit-learn