Abstract
This paper demonstrates a mechanism of modeling the performance of inverters using performance data along with climatological parameters. The proposed methodology is to integrate PV generation data at inverter level from different generation sources in a single platform. A robust network architecture along with data communication devices is used for fetching the inverter level data. This data is appended with real time climatological parameters. A model is then developed for futuristic prediction of PV installation performance data with respect to climatological parameters. Artificial Neural Network (ANN) architecture is used in the process for correlating the climatological parameters with respect to each technology of solar panel for predicting DC current output of inverter. An accuracy of 93.9% is achieved through this model for predicting the DC output of a PV system.