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Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market

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  • Alberto Menéndez Medina

    (Cátedra Industria 4.0, Universitat Jaume I, 12071 Castellón, Spain)

  • José Antonio Heredia Álvaro

    (Cátedra Industria 4.0, Universitat Jaume I, 12071 Castellón, Spain)

Abstract

The electricity market in Spain holds significant importance in the nation’s economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country’s ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI’s ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins.

Suggested Citation

  • Alberto Menéndez Medina & José Antonio Heredia Álvaro, 2024. "Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market," Energies, MDPI, vol. 17(10), pages 1-15, May.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2338-:d:1393354
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    1. Kok, M. & Lootsma, F. A., 1985. "Pairwise-comparison methods in multiple objective programming, with applications in a long-term energy-planning model," European Journal of Operational Research, Elsevier, vol. 22(1), pages 44-55, October.
    2. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    3. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    4. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    5. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
    6. Qianqian Xie & Weiguang Han & Yanzhao Lai & Min Peng & Jimin Huang, 2023. "The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges," Papers 2304.05351, arXiv.org, revised Apr 2023.
    7. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
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    Cited by:

    1. Cristian Valeriu Stanciu & Narcis Eduard Mitu, 2025. "Price Behavior and Market Integration in European Union Electricity Markets: A VECM Analysis," Energies, MDPI, vol. 18(4), pages 1-25, February.

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