Freire-Mahía, NoelGarcía-Fischer, AgustínDíaz-Longueira, AntonioJove, EstebanQuintián, Héctor2026-09-162026-09-162025Freire-Mahía, N., García-Fischer, A., Díaz-Longueira, A., Jove, E., & Quintián, H. (2026). Prediction of Photovoltaic Energy Time Series: a Comparative Study between LSTM, Hybrid and XGBoost Models. In Proceedings XoveTIC 2025: Impulsando el talento científico (pp. 231-238). Servizo de Publicacións UDC. https://doi.org/10.17979/spu.23.c40978-84-9749-925-5https://hdl.handle.net/2183/49282Presentado en: VIII Congreso Xove TIC: impulsando el talento científico. Octubre, 2025, A Coruña.[Abstract] In recent years significant progress has been made in the field of renewable energy, with photovoltaics standing out in particular. This is partly because the users usually try to use clean energy to protect the planet, as well as seeking energy independence and saving money. However, one of the main disadvantages of implementing photovoltaic systems is knowing how much energy will be generated and how to manage it. For this reason, multiple models have been created that are capable of making accurate predictions. This article uses historical data from a simulated installation with PVGIS located at the epef, based on which some of the most prominent methods for making these predictions are presented and analysed, specifically LSTM, XGBoost and hybrid models. These predict the power generated at two stages with acceptable accuracy, enabling the user to improve energy planning. Finally, a comparison will be made, concluding with a brief recommendation on which one to use depending on the context.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Photovoltaic energy forecastingLong short-term memory (LSTM)XGBoostHybrid prediction modelsRenewable energy managementconference outputopen access10.17979/spu.23.c40