  Currently the UK Electric market is guided by load (demand) forecasts
published every thirty minutes by the regulator. A key factor in predicting
demand is weather conditions, with forecasts published every hour. We present
HYENA: a hybrid predictive model that combines feature engineering (selection
of the candidate predictor features), mobile-window predictors and finally LSTM
encoder-decoders to achieve higher accuracy with respect to mainstream models
from the literature. HYENA decreased MAPE loss by 16\% and RMSE loss by 10\%
over the best available benchmark model, thus establishing a new state of the
art for the UK electric load (and price) forecasting.
