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Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market
dc.contributor.author | Menéndez Medina, Alberto | |
dc.contributor.author | Heredia Álvaro, José Antonio | |
dc.date.accessioned | 2024-05-23T07:17:44Z | |
dc.date.available | 2024-05-23T07:17:44Z | |
dc.date.issued | 2024-05-13 | |
dc.identifier.citation | Menéndez Medina, A.; Heredia Álvaro, J.A. Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market. Energies 2024, 17, 2338. https://doi.org/10.3390/en17102338 | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/207489 | |
dc.description.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. | ca_CA |
dc.format.extent | 15 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | MDPI | ca_CA |
dc.rights | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | electricity market price | ca_CA |
dc.subject | Spain | ca_CA |
dc.subject | GPT | ca_CA |
dc.subject | Generative AI | ca_CA |
dc.subject | sentiment analysis | ca_CA |
dc.title | Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.3390/en17102338 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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Excepto si se señala otra cosa, la licencia del ítem se describe como: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).