Performance enhancement of neural network training using hybrid data division technique for photovoltaic power prediction

Abstract

The data available for training, testing and validation of a neural network defines the efficiency or performance of the network. This research work compares the data division techniques like random division, Self-Organizing Maps, fuzzy c means and K-means to predict power output of a solar panel under loss conditions. The data used is obtained from a series of experiments on a soiled panel. Finally, a new data division technique for designing neural networks in PV module output prediction is proposed and its efficiency is compared with other discussed data division techniques. The proposed data division technique helps in building a better neural network model with comparatively less data available.