Seasonality in deep learning forecasts of electricity imbalance prices
提出一种季节性注意力机制，结合双向长短期记忆模型预测英国电力市场平衡结算价格，在极端价格预测上比现有模型提升11%-15%，有助于市场参与者和政策制定者。
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 11%–15% 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.