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.

A Hybrid Model for Forecasting Short-Term Electricity Demand / M. Eleni Athanasopoulou, J. Deveikyte, A. Mosca, I. Peri, A. Provetti (ACM INTERNATIONAL CONFERENCE PROCEEDINGS SERIES). - In: 2nd ACM International Conference on AI in Finance / [a cura di] A. Calinescu, L. Szpruch, E. Kurshan. - Prima edizione. - [s.l] : ACM, 2021. - ISBN 978-1-4503-9148-1. - pp. 1-6 (( Intervento presentato al 2. convegno ACM International Conference on AI in Finance tenutosi a London nel 2021 [10.1145/3490354.3494371].

A Hybrid Model for Forecasting Short-Term Electricity Demand

A. Provetti
Ultimo
2021

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.
Hybrid models; Neural Networks; Regression; Feature Engineering
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/890314
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