  We describe our experience in developing a predictive model that placed high
position in the BigDeal Challenge 2022, an energy competition of load and peak
forecasting. We present a novel procedure for feature engineering and feature
selection, based on cluster permutation of temperatures and calendar variables.
We adopted gradient boosting of trees and we enhance its capabilities with
trend modelling and distributional forecasts. We also include an approach to
forecast combination known as temporal hierarchies, which further improves the
accuracy.
