Model 1.4

Model 1.4

Gradient Boosting Trees using GBtree (Python xgboost library)

Monthly (kWh) consumption forecasting:

1. Twelve regression models (one per month).

2. Regression using (daily kWh) consumption extracted features.

From weekend/weekday and full series:

- Statistical: median, variance, quantiles, ...

- Time-series: autocorrelations, trends, seasonalities, ..

Regression model training: Nested cross-validation.

- Outer k-fold CV loop splits held-out test data.

- Inner k-fold CV loop splits train-validation data.

Hyperparameter optimization on the outer loop using: Dual annealing (global + local search).