Work

Integrating meteorological data for next-day photovoltaic energy prediction using XGBoost

Year: 2025

Type: article

Abstract: This study focuses on the methodology for developing a model to forecast electricity production from photovoltaic (PV) panels through the analysis and processing of meteorological and historical data.An important aspect is the application of the XGBoost algorithm, with a detailed discussion of its selection and the process of hyperparameter tuning.The study also presents an approach to integrating various data sources, including retrieving meteorological data from the Open-Meteo API and combining it with data on PV energy production.The resulting model demonstrates high predictive performance, confirming the effectiveness of XGBoost in capturing complex, non-linear relationships even with limited or noisy training data.

Source: Advances in Science and Technology – Research Journal

DOI: 10.12913/22998624/209204

Authors: Piotr Kraska, Krzysztof Hanzel

Institution: Silesian University of Technology

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $330