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A Hybrid Model for Forecasting Short-Term Electricity Demand

2022-05-20 22:13:25
Maria Eleni Athanasopoulou, Justina Deveikyte, Alan Mosca, Ilaria Peri, Alessandro Provetti
arXiv_AI
   
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.

Abstract (translated)

URL

https://arxiv.org/abs/2205.10449

PDF

https://arxiv.org/pdf/2205.10449.pdf

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