Solar Radiation Forecasting: A Study Case in the Colombian Caribbean Region

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Carrascal, G (Author) Other; Barrios, J (Author) Other; Ferreira, F (Author) FEP; Plaza, J (Author) Other

Conference proceedings International

Title: Springer Proceedings in Mathematics and Statistics

Pages: 199-210

26th Congress of the Portuguese Statistical Society, SPE 2023

Evora, 13 October 2021 through 16 October 2021

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-017-VFC

DOI: 10.1007/978-3-031-68949-9_15

Abstract (EN): This paper presents the forecast of monthly and daily solar radiation in the Colombian Caribbean region using time series analysis. Three models are implemented, the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) for the monthly forecast and two machine learning algorithms, support vector machine (SVM) the regression tree (RT) for the daily forecast. Performance in forecasting solar radiation is compared, with and without climatic variables. Data, including historical solar radiation and climatic variables, were collected by the Institute of Hydrology, Meteorology, and Environmental Studies in Colombia (IDEAM). Results indicate that while the SARIMA model provides acceptable forecasting, and machine learning models demonstrate better performance, which can improve enhancing decision-making in local energy planning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11