Artificial neural network applied to estimate the power output of bipv systems

This paper presents an artificial neural network (ANN) model to estimate the power generated by integrated photovoltaic systems in buildings - BIPVS. The model has as primordial variables, the solar radiation and the ambient temperature of the site of installation of the photovoltaic generator and integrates secondary variables such as the zenith solar angle and the azimuth solar angle. The artificial neural network consists of three layers of operation that allows to adapt to the behavior of the environmental and electrical variables of the photovoltaic generator to create output variables of electrical power through daily profiles. The neural network was implemented in the software Matlbab™ and it was validated using the actual data of monitoring of a 6 kW BIPV system installed at Universidad de Bogotá Jorge Tadeo Lozano, in Bogotá, Colombia. The results indicate a correlation coefficient of 98% on the output power of the BIPV system between the artificial neural network and the performance data of the solar photovoltaic plant. These results show the reliability of the model for PV systems operating in different climatic conditions and different generation capacities.

• Fecha de publicación: 2017
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/9281
• Acceso en línea: https://issuu.com/iosrjce/docs/j1901017378; http://hdl.handle.net/20.500.12010/9281
• Palabra clave: Photovoltaic systems; Artificial neural network; Power output; BIPVS modelling; Computadores neuronales -- Investigaciones; Generadores de energía fotovoltaica
• License: Abierto (Texto Completo)