Estimating the Performance Loss Rate of Photovoltaic Systems Using Time Series Change Point Analysis

Andreas Livera (), Georgios Tziolis, Marios Theristis, Joshua S. Stein and George E. Georghiou
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Andreas Livera: PV Technology Laboratory, FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus
Georgios Tziolis: PV Technology Laboratory, FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus
Marios Theristis: Sandia National Laboratories, Albuquerque, NM 87185, USA
Joshua S. Stein: Sandia National Laboratories, Albuquerque, NM 87185, USA
George E. Georghiou: PV Technology Laboratory, FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus

Energies, 2023, vol. 16, issue 9, 1-18

Abstract: The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations.

Keywords: change point techniques; modeling; nonlinear degradation; performance loss rate; photovoltaics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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