This project demonstrates predictive modeling and feature engineering by for forecasting and resource optimization system for residential energy usage. The goal was to predict residential energy demand based on complex environmental and structural inputs. The methodology involved:
- Feature Engineering: Identifying and prioritizing non-obvious features crucial for accurate prediction, including HVAC system type, fixtures, type of cooling/heating, building characteristics (e.g., number of stories, square footage), and granular weather correlations (dry-bulb temperature, relative humidity). This mirrors the complexity of non-location-based valuation.
- Modeling: Developing a high-accuracy, time-series-based XGBoost model to forecast consumption patterns.
- Actionable Insights: Converting model predictions into quantifiable business recommendations (load-shifting strategies) to reduce peak demand and costs, demonstrating risk and cost management.
| Skill Area | Technology Used | Achievement / Output |
|---|---|---|
| Prediction Model | XGBoost (Time-Series) | Achieved 92% prediction accuracy in forecasting residential energy demand. |
| Feature Interpretation | SHAP Values | Used SHAP to create explainable, actionable insights (identifying the influence of specific weather/structural factors). |
| Business Impact | Forecasting, Optimization | Enabled a 28% reduction in peak load (translating directly to cost/risk reduction) via data-driven strategies. |
R / XGBoost / SHAP / Time-Series Modeling / SQL / Shiny