Electricity Price Forecasting One Day Ahead by Employing Hybrid Deep Learning Model

Abdellatif, Abdallah; Mubarak, Hamza; Ahmad, Shameem; Mekhilef, Saad; Abdellatef, Hamdan; Mokhlis, Hazlie; Kanesan, Jeevan

doi:10.1109/globconht56829.2023.10087634

Cited by 4 publications

(1 citation statement)

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“…Therefore, the combination of intelligence optimization theories and advancements in computer technology has led to a growing research interest in predictive models in this area, particularly using deep learning (DL) architectures due to their remarkable performance and broad application scope. There have been developments in electricity price time series prediction models with different architectures, some of which are: convolutional network (CNN) [51][52][53], recurrent neural network (RNN)-based models [54][55][56][57][58], generative models [59,60], Bayesian networks (BNs) [61,62], and hybrid models (ensembles, signal preprocessing steps, among others) [63][64][65][66].…”

Section: Literature Reviewmentioning

confidence: 99%

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Cantillo-Luna,

Moreno-Chuquen,

Lopez-Sotelo

et al. 2023

Energies

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This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.

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