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arXiv · 2205.10449

A Hybrid Model for Forecasting Short-Term Electricity Demand

Maria Eleni Athanasopoulou·Justina Deveikyte·Alan Mosca·Ilaria Peri·Alessandro Provetti
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Abstract

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.

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Maria Eleni Athanasopoulou, Justina Deveikyte, Alan Mosca, Ilaria Peri, Alessandro Provetti. 2022-05-20. A Hybrid Model for Forecasting Short-Term Electricity Demand. https://doi.org/10.1145/3490354.3494371

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