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

Sergio Cantillo-Luna, Ricardo Moreno-Chuquen (), Jesus Lopez-Sotelo and David Celeita
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Sergio Cantillo-Luna: Faculty of Engineering, Universidad Autónoma de Occidente, Cali 760030, Colombia
Ricardo Moreno-Chuquen: Faculty of Engineering and Design, Universidad Icesi, Cali 760031, Colombia
Jesus Lopez-Sotelo: Faculty of Engineering, Universidad Autónoma de Occidente, Cali 760030, Colombia
David Celeita: School of Engineering, Science and Technology, Universidad del Rosario, Bogotá 111221, Colombia

Energies, 2023, vol. 16, issue 19, 1-24

Abstract: 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.

Keywords: decision making; deep learning; electricity price forecasting (EPF); probabilistic forecasting; time series forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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