Photovoltaic power pattern clustering is fundamental in providing enhanced knowledge on the impacts of integrating photovoltaic systems into the electrical grid without extensive analysis and simulations. This paper investigates a set of clustering algorithms and validity indices to find the most efficient ones in grouping photovoltaic power patterns data. Furthermore, the introduction of the recently-developed bio-inspired optimization method, Bat, with various objective functions in clustering photovoltaic power patterns is presented. In order to evaluate the clustering results in a comprehensive manner, six internal validity indices are employed and a method to determine the optimum number of clusters is introduced. The clustering results on two datasets show that bio-inspired clustering algorithm Bat based on within cluster-sum-of-squares to between-cluster variation (Bat WCBCR) as an objective function produces significantly high separated and well compact clusters. (C) 2017 Elsevier Ltd. All rights reserved.