Statistical Processing of Data Coming from a Photovoltaic Plant for Accurate Energy Planning

This paper presents a statistical approach to manage sampled data coming from a photovoltaic installation. The proposed statistical methods are the k-means clustering and the normal density probability distribution. The use of the proposed methods allows to simplify the problem of the PV plant energy assessment respect to the option of obtaining the desired information by managing a large amount of experimental observations. The proposed methods represent useful tools for an appropriate energy planning in distributed generation systems.