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Comparison of Different Data Sources for Machine Learning Algorithms in Photovoltaic Output Power Estimation
P. Graniero, A. Louwen, R. Schlatmann, C. Ulbrich
Modelling / Modeling, Model/s, Machine Learning, Power Production
PV Systems and Storage – Modelling, Design, Operation and Performance
Subtopic: Operation, Performance and Maintenance of PV Systems
Event: 37th European Photovoltaic Solar Energy Conference and Exhibition
Session: 5CV.4.18
1636 - 1639
ISBN: 3-936338-73-6
Paper DOI: 10.4229/EUPVSEC20202020-5CV.4.18
0,00 EUR
Document(s): paper, poster


Machine learning algorithms offer the promising opportunity to monitor a photovoltaic system’s performance, particularly its power output, using alternative data sources. In this work we consider performance data collected from nearby sites, rather than environmental data acquired with costly sensors or purchased from external sources. This study compares five different machine learning algorithms and their respective prediction accuracy of the output power using as input either power data from nearby systems or environmental data collected from an onsite weather station or one nearby. We show that power data from nearby sites can be used for predictions that are as precise as those based on onsite measured environmental data.