Athanasopoulou, Maria Eleni and Deveikyte, Justina and Mosca, Alan and Peri, Ilaria and Provetti, Alessandro (2021) A hybrid model for forecasting short-term electricity demand. In: 2nd ACM International Conference on AI in Finance, 03-05 Nov 2021, London, UK. (In Press)
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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.
Metadata
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keyword(s) / Subject(s): | Hybrid models, Neural Networks, Regression, Feature Engineering |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
| Depositing User: | Alessandro Provetti |
| Date Deposited: | 25 Jan 2022 18:16 |
| Last Modified: | 09 Aug 2023 12:52 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/47349 |
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