Energy Demand Prediction

01 — The problem

Grid operators need demand forecasts accurate enough to plan capacity without over-provisioning. Under-forecast and you risk outages; over-forecast and you waste money.

02 — The approach

1. 01

   Engineered features from historical load, seasonality, and weather signals.

2. 02

   Benchmarked XGBoost against Random Forest and tuned for forecast accuracy on held-out periods.

3. 03

   Framed outputs around planning decisions, not just error metrics.

03 — Stack

04 — Outcome