Global Horizontal Irradiance in Brazil: A Comparative Study of Reanalysis Datasets with Ground-Based Data

• Published in: Energies (Basel) Vol. 17; no. 20; p. 5063
• Main Authors: Araujo, Margarete Afonso de Sousa Guilhon, Aguilar, Soraida, Souza, Reinaldo Castro, Cyrino Oliveira, Fernando Luiz
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: Alternative energy sources; Climate change; Comparative analysis; data evaluation; Datasets; Electric power production; Electricity; Emission standards; Emissions; Energy industry; Energy resources; Energy transition; global horizontal irradiance; Greenhouse gases; ground-based data; Industrial plant emissions; Measurement; Precipitation; Radiation; reanalysis; renewable energy sources; Renewable resources; Solar energy; Solar energy industry; solar irradiance; Time series; Variables; Brazil
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17205063

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets.