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Outdoor PV System Monitoring—Input Data Quality, Data Imputation and Filtering Approaches

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  • Sascha Lindig

    (Institute for Renewable Energy, EURAC Research, Viale Druso 1, 39100 Bolzano, Italy
    Faculty of Engineering, University of Ljubljana, Trzaska Cesta 25, 1000 Ljubljana, Slovenia)

  • Atse Louwen

    (Institute for Renewable Energy, EURAC Research, Viale Druso 1, 39100 Bolzano, Italy)

  • David Moser

    (Institute for Renewable Energy, EURAC Research, Viale Druso 1, 39100 Bolzano, Italy)

  • Marko Topic

    (Faculty of Engineering, University of Ljubljana, Trzaska Cesta 25, 1000 Ljubljana, Slovenia)

Abstract

Photovoltaic monitoring data are the primary source for studying photovoltaic plant behavior. In particular, performance loss and remaining-useful-lifetime calculations rely on trustful input data. Furthermore, a regular stream of high quality is the basis for pro-active operation and management activities which ensure a smooth operation of PV plants. The raw data under investigation are electrical measurements and usually meteorological data such as in-plane irradiance and temperature. Usually, performance analyses follow a strict pattern of checking input data quality followed by the application of appropriate filter, choosing a key performance indicator and the application of certain methodologies to receive a final result. In this context, this paper focuses on four main objectives. We present common photovoltaics monitoring data quality issues, provide visual guidelines on how to detect and evaluate these, provide new data imputation approaches, and discuss common filtering approaches. Data imputation techniques for module temperature and irradiance data are discussed and compared to classical approaches. This work is intended to be a soft introduction into PV monitoring data analysis discussing best practices and issues an analyst might face. It was seen that if a sufficient amount of training data is available, multivariate adaptive regression splines yields good results for module temperature imputation while histogram-based gradient boosting regression outperforms classical approaches for in-plane irradiance transposition. Based on tested filtering procedures, it is believed that standards should be developed including relatively low irradiance thresholds together with strict power-irradiance pair filters.

Suggested Citation

  • Sascha Lindig & Atse Louwen & David Moser & Marko Topic, 2020. "Outdoor PV System Monitoring—Input Data Quality, Data Imputation and Filtering Approaches," Energies, MDPI, vol. 13(19), pages 1-18, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5099-:d:422169
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    Cited by:

    1. Juan Ignacio Herraiz & Rita Hogan Almeida & Manuel Castillo-Cagigal & Luis Narvarte, 2023. "Experimental Performance Evaluation of a PV-Powered Center-Pivot Irrigation System for a Three-Year Operation Period," Energies, MDPI, vol. 16(9), pages 1-19, April.
    2. Gabriel Nicolae Popa & Angela Iagăr & Corina Maria Diniș, 2020. "Considerations on Current and Voltage Unbalance of Nonlinear Loads in Residential and Educational Sectors," Energies, MDPI, vol. 14(1), pages 1-29, December.
    3. Fangfang Li & Hui Sun & Yu Gu & Ge Yu, 2022. "A Noise-Aware Multiple Imputation Algorithm for Missing Data," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
    4. Fuster-Palop, Enrique & Vargas-Salgado, Carlos & Ferri-Revert, Juan Carlos & Payá, Jorge, 2022. "Performance analysis and modelling of a 50 MW grid-connected photovoltaic plant in Spain after 12 years of operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    5. Karttunen, Lauri & Jouttijärvi, Sami & Poskela, Aapo & Palonen, Heikki & Huerta, Hugo & Todorović, Milica & Ranta, Samuli & Miettunen, Kati, 2023. "Comparing methods for the long-term performance assessment of bifacial photovoltaic modules in Nordic conditions," Renewable Energy, Elsevier, vol. 219(P1).
    6. Romero-Fiances, Irene & Livera, Andreas & Theristis, Marios & Makrides, George & Stein, Joshua S. & Nofuentes, Gustavo & de la Casa, Juan & Georghiou, George E., 2022. "Impact of duration and missing data on the long-term photovoltaic degradation rate estimation," Renewable Energy, Elsevier, vol. 181(C), pages 738-748.
    7. Gianfranco Di Lorenzo & Erika Stracqualursi & Leonardo Micheli & Salvatore Celozzi & Rodolfo Araneo, 2022. "Prognostic Methods for Photovoltaic Systems’ Underperformance and Degradation: Status, Perspectives, and Challenges," Energies, MDPI, vol. 15(17), pages 1-6, September.

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