Paper ID: 2205.10449

A Hybrid Model for Forecasting Short-Term Electricity Demand

Maria Eleni Athanasopoulou, Justina Deveikyte, Alan Mosca, Ilaria Peri, Alessandro Provetti

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.

Submitted: May 20, 2022