Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks

This paper presents a prediction model of solar radiation for dimensioning photovoltaic generation systems in the Atlantic Coast of Colombia, using artificial neural networks. As a case of study is presented the municipality "El Carmen de Bolivar" located in this region. To obtain the model, the average data of daily temperature, relative humidity and solar radiation from the last ten years, reported by weather stations in this city were used. Six neural networks were designed with six variants of input variables (temperature, humidity and month) and the output variable (solar radiation). The best result was obtained using all input variables. In the training process, the correlation index (R) between solar radiation estimated by the model and the recorded data was 0.8. In validating the correlation index was 0.77.

• Autores: Noriega Angarita, Eliana Maria; Sousa Santos, Vladimir; Quintero Duran, Michell Josep; Gil Arrieta, Cesar Javier
• Tipo de recurso: Article of journal
• Fecha de publicación: 2016
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/817
• Acceso en línea: http://hdl.handle.net/11323/817; https://repositorio.cuc.edu.co/
• Palabra clave: Artificial neural networks; Modelling; Photovoltaic generation systems; Prediction; Solar radiation
• Rights: openAccess
• License: Atribución – No comercial – Compartir igual