Abstract
Data mining techniques have been used on data collected from a photovoltaic system to predict its generation and performance. Nevertheless, up to date, this computing approach has needed the simultaneous measurement of environmental parameters that are collected by an array of sensors. This chapter presents the application of several computing learning techniques to electrical data in order to detect and classify the occurrence of failures (i.e. shadows, bad weather conditions, etc.) without using environmental data. The results of a 222kWp (CdTe) case study show how the application of computing learning algorithms can be used to improve the management and performance of photovoltaic generators without relying on environmental parameters.
Lucía Serrano-Luján
Associate Professor
Lucía Serrano-Luján is an Associate Professor in the Department of Computer Science. Her research field is multidisciplinary. She developed a Life Cycle Assessment methodology to assess renewable energies and applied AI to their data. Her main goal is to impact energy-related materials production and find a more sustainable way to develop them. She has applied LCA to reduce graphene oxide and perovskites solar cells, build integrated photovoltaics, etc.