Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices

Sinan Deng, John Inekwe, Vladimir Smirnov, Andrew Wait and Chao Wang

No 2023-03, Working Papers from University of Sydney, School of Economics

Abstract: In this paper, we propose a seasonal attention mechanism, the effectiveness of which is evaluated via the Bidirectional Long Short-Term Memory (BiLSTM) model. We compare its performance with alternative deep learning and machine learning models in forecasting the balancing settlement prices in the electricity market of Great Britain. Critically, the Seasonal Attention-Based BiLSTM framework provides a superior forecast of extreme prices with an out-of-sample gain in the predictability of 25-37% compared with models in the literature. Our forecasting techniques could aid both market participants, to better manage their risk and assign their assets, and policy makers, to operate the system at lower cost.

Keywords: forecasting; electricity; balance settlement prices; Long Short-Term Memory; machine learning. (search for similar items in EconPapers)
Date: 2023-06
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-ene and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:syd:wpaper:2023-03

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