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

Procedure for the practical and economic integration of solar PV energy in the city of Bogotá

Photovoltaic (PV) solar technology is becoming progressively widespread at both urban and rural levels, thanks to the worldwide costs falling of the solar panels. This work presents a methodology for integrating photovoltaic solar systems at the residential level in Bogotá, Colombia, and the analysis of Law 1715–2014 and its implications in the renewable energy incentives. The technical and economic aspects to supply the energy demand of residential consumers of strata 2 and 3 of the Colombian capital were analyzed. The study defines two financing scenarios: one including the economic incentives of Law 1715 of 2014 and the other without considering them. These scenarios encompassed four sub-scenarios. Two sub-scenarios were designed to cover the energy consumption of 1 consumer of category (stratum) two and one consumer of category 3, and the other two sub-scenarios represented the coverage of the demand of all subscribers in each category. The main results indicate that covering from 10% to 100% of the power requirement of a single consumer in category two allows annual savings ranging from USD 29.72 and USD 293.27, respectively; while covering from 10% to 100% of the requirement of a single consumer of category 3 allows annual savings ranging from USD 29.62 and USD 296.18 respectively. The environmental analysis determined that the CO2 emissions avoided in sub-scenarios 1 and 2 in the next 25 years were 20.41 and 20.39 tons of CO2 equivalent