Dense Skip Attention Based Deep Learning for Day-Ahead Electricity Price Forecasting

Abstract

The forecasting of the day-ahead electricity price (DAEP) has become more of interest to decision makers in the liberalized market, as it can help optimize bidding strategies and maximize profits with the gradual market expansion. Deep learning (DL) is a promising method for its strong nonlinear approximation capabilities. However, it is challenging for traditional DL models to obtain a high forecasting precision for the DAEP, due to its internal temporal and feature-wise variabilities. To address the issue, this paper proposes a dense skip attention based DL model. In this model, to tackle the feature-wise variability, a mechanism of dense skip attention is proposed to efficiently assign learnable weights on the features for training. In terms of the temporal variability, a drop-connected structure based on the advanced residual unshared convolutional neural network (ARUCNN) and gate recurrent units (GRUs) is further proposed. In this structure, the ARUCNN is developed by embedding advanced activations to deal with the short-term dependencies and degradation while GRUs addressing the long-term ones, and they are integrated via a drop connection to reduce the overfitting. Through validating on real DAEP data in day-ahead markets of Sweden, Denmark, Norway and Finland, the results verify our proposed approach outperforms the existing methods in the deterministic and interval forecasting of DAEP.