Title:	Guidelines for Ensuring Data Quality for Photovoltaic System Performance Assessment and Monitoring
Author(s):	A. Livera, M. Theristis, E. Koumpli, G. Makrides, J.S. Stein, G.E. Georghiou
Keywords:	Reliability, Photovoltaic (PV), System Performance, Outlier, Data Quality
Topic:	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:	5DO.2.4
Pages:	1352 - 1356
ISBN:	3-936338-73-6
Paper DOI:	10.4229/EUPVSEC20202020-5DO.2.4
Price:	0,00 EUR
Document(s):	paper, presentation

Abstract/Summary:

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High-quality datasets are crucial for the performance and reliability analysis of photovoltaic (PV) systems. With respect to data integrity, invalid data are a common problem exhibited in PV monitoring systems. A data pipeline approach was recently introduced aiming to support reproducible results in PV performance. The methodology is expanded in this study by examining further outlier observations in respect to detection techniques, impact and treatment methods. The outlier detection results demonstrated that the standard boxplot rule yielded the highest detection rate of 95.3% (by taking a moving data window) at 40% of outlying data points and the effect of random outlying data points was mitigated by listwise deletion. The comparative analysis of outlying data treatment demonstrated that back-filling with the Sandia Array Performance Model (SAPM) yielded more accurate degradation rate (RD) estimates (absolute percentage error, APE, of up to 0.36% at 40% of outlying data) compared to filtering out the outlying data points (APE of up to 2.53% with listwise deletion).