Spatial estimation of solar radiation using geostatistics and machine learning techniques

Abstract

In large solar fields, where the control system is distributed, it is important to know the values of solar radiation in the complete area. Local solar radiation can be obtained by means of static sensors, using e.g. a wireless sensor network or movable sensors with drones for the general obtainment of variables. In this paper, solar radiation estimation is accomplished using Ordinary Kriging and distance weighting, and an alternative method is presented, which is based on a non-supervised competitive artificial neural network called Self-Organizing Map. This neural network generates a map with the most representative nodes and their weights, which are used to obtain the spatial variability of solar radiation in the area.