Version 1
: Received: 19 March 2024 / Approved: 20 March 2024 / Online: 20 March 2024 (11:29:31 CET)
How to cite:
Menéndez Medina, A.; Heredia Álvaro, J.A. Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market. Preprints2024, 2024031197. https://doi.org/10.20944/preprints202403.1197.v1
Menéndez Medina, A.; Heredia Álvaro, J.A. Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market. Preprints 2024, 2024031197. https://doi.org/10.20944/preprints202403.1197.v1
Menéndez Medina, A.; Heredia Álvaro, J.A. Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market. Preprints2024, 2024031197. https://doi.org/10.20944/preprints202403.1197.v1
APA Style
Menéndez Medina, A., & Heredia Álvaro, J.A. (2024). Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market. Preprints. https://doi.org/10.20944/preprints202403.1197.v1
Chicago/Turabian Style
Menéndez Medina, A. and José Antonio Heredia Álvaro. 2024 "Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market" Preprints. https://doi.org/10.20944/preprints202403.1197.v1
Abstract
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. Our findings indicate that integrating GPT insights into electricity price trend forecasting can result in more precise predictions and a deeper understanding of market dynamics. Through our research, we aim to provide insights to understand the capabilities of GPT solutions, and how those can be used to enhance prediction accuracy, ultimately supporting informed decision-making for stakeholders across the Spanish electricity market and companies whose margins heavily depend on electricity costs and price volatility.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
