Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms

Abstract

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field.

Combinatorial Component Day-Ahead Load Forecasting through Unanchored Time Series Chain Evaluation

“…Lastly, neural network models such as the Multi-Layer Perceptron (MLP) and the Long Short-Term Memory network (LSTM) are black-box approaches that enable adaptive and accurate approximation of complex non-linear functions and exhibit improved resilience towards missing values and outliers [23][24][25][26][27][28]. It is worth noting that advances in neural network research led to the integration of attention mechanisms such as self-attention and multi-head attention [29]. These attention mechanisms enhanced the interpretability of complex neural network architectures and contributed towards the development of models that focus on the most important parts of the data in order to express non-linear relationships.…”