Comparison of SOM and conventional neural network data division for PV reliability power prediction

Abstract

This paper studies the influence of neural network clustering in power prediction of soiled PV panels using artificial neural networks. Self-organizing maps were used to cluster and preprocess the data before training the neural network. 70% of data from each cluster is used for training and 15% each for testing and validation. The accuracy of prediction from the developed model was compared with a neural network model which uses random data division without data preprocessing. It was observed that preprocessing the data through clustering would enhance the accuracy of prediction as compared to model developed without data preprocessing. At lower irradiance levels (200-400W/m 2 ) the percentage error in prediction was 8% and at higher irradiance levels (800-1200W/m 2 ) the error decreased to 2%.