# A Hybrid Model for Forecasting Short-Term Electricity Demand

arXiv: https://arxiv.org/abs/2205.10449 · HTML: https://mlsanity.com/paper/2205.10449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 24 references that are in the map.
- First posted on arXiv 20 May 2022.

## Abstract (arXiv)

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.