IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v77y2015icp51-63.html
   My bibliography  Save this article

Analysis of photovoltaic system performance time series: Seasonality and performance loss

Author

Listed:
  • Phinikarides, Alexander
  • Makrides, George
  • Zinsser, Bastian
  • Schubert, Markus
  • Georghiou, George E.

Abstract

In this work, the seasonality and performance loss rates of eleven grid-connected photovoltaic (PV) systems of different technologies were evaluated through seasonal adjustment. The classical seasonal decomposition (CSD) and X-12-ARIMA statistical techniques were applied on monthly DC performance ratio, RP, time series, constructed from field measurements over the systems' first five years of operation. The results have shown that the RP of crystalline silicon (c-Si) technologies was higher during winter. This was also the case for the copper–indium gallium-diselenide (CIGS) and cadmium telluride (CdTe) technologies but with lower seasonal amplitude. The amorphous silicon (a-Si) technology exhibited a different seasonal profile, with high RP during summer and autumn and low during winter. In addition, the trends extracted from the application of CSD and X-12-ARIMA on three-year, four-year and five-year RP time series were used to estimate linear performance loss rates. A comparison between standard linear regression (LR), CSD and X-12-ARIMA has shown that CSD and X-12-ARIMA resulted in higher rates overall for c-Si, 1.07 and 0.93%/year respectively, but with significantly less uncertainty than LR. Lastly, it was shown that X-12-ARIMA provided statistical inference in the presence of outliers and produced model residuals that were uncorrelated, in contrast to CSD.

Suggested Citation

  • Phinikarides, Alexander & Makrides, George & Zinsser, Bastian & Schubert, Markus & Georghiou, George E., 2015. "Analysis of photovoltaic system performance time series: Seasonality and performance loss," Renewable Energy, Elsevier, vol. 77(C), pages 51-63.
    • Handle: RePEc:eee:renene:v:77:y:2015:i:c:p:51-63
    DOI: 10.1016/j.renene.2014.11.091
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114008222
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2014.11.091?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. G. E. P. Box & G. M. Jenkins & J. F. MacGregor, 1974. "Some Recent Advances in Forecasting and Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(2), pages 158-179, June.
    2. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    3. Victor Gómez & Agustín Maravall, 1996. "Programs TRAMO and SEATS, Instruction for User (Beta Version: september 1996)," Working Papers 9628, Banco de España.
    4. Phinikarides, Alexander & Kindyni, Nitsa & Makrides, George & Georghiou, George E., 2014. "Review of photovoltaic degradation rate methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 143-152.
    5. Miller, Don M. & Williams, Dan, 2003. "Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 19(4), pages 669-684.
    6. Makrides, George & Zinsser, Bastian & Phinikarides, Alexander & Schubert, Markus & Georghiou, George E., 2012. "Temperature and thermal annealing effects on different photovoltaic technologies," Renewable Energy, Elsevier, vol. 43(C), pages 407-417.
    7. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    8. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    9. Agustín Maravall, 1996. "Unobserved Components in Economic Time Series," Working Papers 9609, Banco de España.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ozden, Talat & Akinoglu, Bulent G. & Turan, Rasit, 2017. "Long term outdoor performances of three different on-grid PV arrays in central Anatolia – An extended analysis," Renewable Energy, Elsevier, vol. 101(C), pages 182-195.
    2. Piotrowska–Woroniak, Joanna & Woroniak, Grzegorz & Załuska, Wiesław, 2015. "Energy production from PV and carbon reduction in great lakes region of Masuria Poland: A case study of water park in Elk," Renewable Energy, Elsevier, vol. 83(C), pages 1315-1325.
    3. Kumar, Manish & Kumar, Arun, 2017. "Performance assessment and degradation analysis of solar photovoltaic technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 554-587.
    4. Silvestre, Santiago & Tahri, Ali & Tahri, Fatima & Benlebna, Soumiya & Chouder, Aissa, 2018. "Evaluation of the performance and degradation of crystalline silicon-based photovoltaic modules in the Saharan environment," Energy, Elsevier, vol. 152(C), pages 57-63.
    5. Bórawski, Piotr & Holden, Lisa & Bełdycka-Bórawska, Aneta, 2023. "Perspectives of photovoltaic energy market development in the european union," Energy, Elsevier, vol. 270(C).
    6. 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).
    7. Agata Zdyb & Slawomir Gulkowski, 2020. "Performance Assessment of Four Different Photovoltaic Technologies in Poland," Energies, MDPI, vol. 13(1), pages 1-17, January.
    8. Piotr Olczak & Małgorzata Olek & Dominika Matuszewska & Artur Dyczko & Tomasz Mania, 2021. "Monofacial and Bifacial Micro PV Installation as Element of Energy Transition—The Case of Poland," Energies, MDPI, vol. 14(2), pages 1-22, January.
    9. 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.
    10. Aghaei, M. & Fairbrother, A. & Gok, A. & Ahmad, S. & Kazim, S. & Lobato, K. & Oreski, G. & Reinders, A. & Schmitz, J. & Theelen, M. & Yilmaz, P. & Kettle, J., 2022. "Review of degradation and failure phenomena in photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    11. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2020. "Field Analysis of three different silicon-based Technologies in Composite Climate Condition – Part II – Seasonal assessment and performance degradation rates using statistical tools," Renewable Energy, Elsevier, vol. 147(P1), pages 2102-2117.
    12. Ameur, Arechkik & Berrada, Asmae & Bouaichi, Abdellatif & Loudiyi, Khalid, 2022. "Long-term performance and degradation analysis of different PV modules under temperate climate," Renewable Energy, Elsevier, vol. 188(C), pages 37-51.
    13. Limmanee, Amornrat & Songtrai, Sasiwimon & Udomdachanut, Nuttakarn & Kaewniyompanit, Songpakit & Sato, Yukinobu & Nakaishi, Masaki & Kittisontirak, Songkiate & Sriprapha, Kobsak & Sakamoto, Yukitaka, 2017. "Degradation analysis of photovoltaic modules under tropical climatic conditions and its impacts on LCOE," Renewable Energy, Elsevier, vol. 102(PA), pages 199-204.
    14. Andreas Livera & Georgios Tziolis & Marios Theristis & Joshua S. Stein & George E. Georghiou, 2023. "Estimating the Performance Loss Rate of Photovoltaic Systems Using Time Series Change Point Analysis," Energies, MDPI, vol. 16(9), pages 1-18, April.
    15. Armendariz-Lopez, J.F. & Luna-Leon, A. & Gonzalez-Trevizo, M.E. & Arena-Granados, A.P. & Bojorquez-Morales, G., 2016. "Life cycle cost of photovoltaic technologies in commercial buildings in Baja California, Mexico," Renewable Energy, Elsevier, vol. 87(P1), pages 564-571.
    16. Piotr Olczak & Agnieszka Żelazna & Dominika Matuszewska & Małgorzata Olek, 2021. "The “My Electricity” Program as One of the Ways to Reduce CO 2 Emissions in Poland," Energies, MDPI, vol. 14(22), pages 1-17, November.
    17. Balaska, Amira & Tahri, Ali & Tahri, Fatima & Stambouli, Amine Boudghene, 2017. "Performance assessment of five different photovoltaic module technologies under outdoor conditions in Algeria," Renewable Energy, Elsevier, vol. 107(C), pages 53-60.
    18. Alexander Frick & George Makrides & Markus Schubert & Matthias Schlecht & George E. Georghiou, 2020. "Degradation Rate Location Dependency of Photovoltaic Systems," Energies, MDPI, vol. 13(24), pages 1-20, December.
    19. Irene Romero-Fiances & Emilio Muñoz-Cerón & Rafael Espinoza-Paredes & Gustavo Nofuentes & Juan De la Casa, 2019. "Analysis of the Performance of Various PV Module Technologies in Peru," Energies, MDPI, vol. 12(1), pages 1-19, January.
    20. Meng, B. & Loonen, R.C.G.M. & Hensen, J.L.M., 2022. "Performance variability and implications for yield prediction of rooftop PV systems – Analysis of 246 identical systems," Applied Energy, Elsevier, vol. 322(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fok, D. & Franses, Ph.H.B.F. & Paap, R., 2005. "Performance of Seasonal Adjustment Procedures: Simulation and Empirical Results," Econometric Institute Research Papers EI 2005-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Carlos A. Medel, 2018. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986–2009 with X-12-ARIMA," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 47-87, April.
    3. Kirchner, Robert, 1999. "Auswirkungen des neuen Saisonbereinigungsverfahrens Census X-12-ARIMA auf die aktuelle Wirtschaftsanalyse in Deutschland," Discussion Paper Series 1: Economic Studies 1999,07, Deutsche Bundesbank.
    4. Thornton, Michael A., 2013. "Removing seasonality under a changing regime: Filtering new car sales," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 4-14.
    5. Alexander Frick & George Makrides & Markus Schubert & Matthias Schlecht & George E. Georghiou, 2020. "Degradation Rate Location Dependency of Photovoltaic Systems," Energies, MDPI, vol. 13(24), pages 1-20, December.
    6. Siem Jan Koopman & Philip Hans Franses, 2002. "Constructing Seasonally Adjusted Data with Time‐varying Confidence Intervals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(5), pages 509-526, December.
    7. Maravall, Agustín, 2000. "Notes on time serie analysis, ARIMA models and signal extraction," DES - Working Papers. Statistics and Econometrics. WS 10058, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Giancarlo Bruno & Edoardo Otranto, 2006. "The choice of time interval in seasonal adjustment: A heuristic approach," Statistical Papers, Springer, vol. 47(3), pages 393-417, June.
    9. Miller, Don M. & Williams, Dan, 2004. "Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program," International Journal of Forecasting, Elsevier, vol. 20(4), pages 529-549.
    10. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    11. Proietti, Tommaso & Riani, Marco, 2007. "Transformations and Seasonal Adjustment: Analytic Solutions and Case Studies," MPRA Paper 7862, University Library of Munich, Germany.
    12. Edoardo Otrano & Umberto Triacca, 2007. "Testing for Equal Predictability of Stationary ARMA Processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1091-1108.
    13. Maravall, Agustín, 2000. "An application of tramo-seats: changes in seasonality and current trend-cycle assesment: the german retail trade turnover series," DES - Working Papers. Statistics and Econometrics. WS 10010, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Mauricio Gallardo & Hernán Rubio, 2009. "Diagnóstico de estacionalidad con X-12-ARIMA," Economic Statistics Series 76, Central Bank of Chile.
    15. Kroes, James R. & Manikas, Andrew S. & Gattiker, Thomas F., 2018. "Operational leanness and retail firm performance since 1980," International Journal of Production Economics, Elsevier, vol. 197(C), pages 262-274.
    16. Quenneville, Benoit & Ladiray, Dominique & Lefrancois, Bernard, 2003. "A note on Musgrave asymmetrical trend-cycle filters," International Journal of Forecasting, Elsevier, vol. 19(4), pages 727-734.
    17. Massmann, Michael & Mitchell, James, 2003. "Reconsidering the evidence: Are Eurozone business cycles converging," ZEI Working Papers B 05-2003, University of Bonn, ZEI - Center for European Integration Studies.
    18. Hai Yue Liu & Xiao Lan Chen, 2017. "The imported price, inflation and exchange rate pass-through in China," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1279814-127, January.
    19. Stefania D'Amico & Athanasios Orphanides, 2008. "Uncertainty and disagreement in economic forecasting," Finance and Economics Discussion Series 2008-56, Board of Governors of the Federal Reserve System (U.S.).
    20. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:77:y:2015:i:c:p:51-63. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.