@inproceedings{liveraImpactMissingData2017,
  title = {Impact of {{Missing Data}} on the {{Estimation}} of {{Photovoltaic System Degradation Rate}}},
  booktitle = {44th {{IEEE PVSC}}},
  author = {Livera, Andreas and Phinikarides, Alexander and Makrides, George and Georghiou, George E.},
  year = {2017},
  pages = {1954--1958},
  doi = {10.1109/PVSC.2017.8366442},
  abstract = {In this paper, the impact of missing data and the robustness of commonly used statistical techniques to calculate the annual degradation rate of crystalline-Silicon (c-Si) photovoltaic (PV) systems was analyzed. In addition, the performance of different imputation techniques was assessed in order to develop an optimal methodology for treating missing data for degradation rate estimation studies. The results obtained clearly demonstrate that the application of the different statistical methods to estimate the annual degradation rate is sensitive to the amount of missing data, since all the statistical methods underestimated the degradation rate consistently with the increasing level of missing data. In addition, the application of the Seasonal Decomposition (Seas) technique yielded robust annual degradation rate estimates since for a level of 40 \% of missing data, the absolute percentage error (APE) of the annual degradation rate estimated with all statistical techniques, was less than 7.5 \%.  Finally, Classical Seasonal Decomposition (CSD) was shown to be the most robust technique for estimating the degradation rate when imputation was applied, while Autoregressive Integrated Moving Average (ARIMA) was the most successful technique in providing robust degradation rate estimates when not applying any imputation.},
  copyright = {All rights reserved},
  file = {/home/alexis/Zotero/storage/6748IMCH/Livera et al. - 2017 - Impact of Missing Data on the Estimation of Photov.pdf;/home/alexis/Zotero/storage/M8M5Z7DM/Livera et al. - 2017 - Impact of Missing Data on the Estimation of Photov.pdf}
}

{"_id":"kxKPNARLW9Yx4Huxu","bibbaseid":"livera-phinikarides-makrides-georghiou-impactofmissingdataontheestimationofphotovoltaicsystemdegradationrate-2017","author_short":["Livera, A.","Phinikarides, A.","Makrides, G.","Georghiou, G. E."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Impact of Missing Data on the Estimation of Photovoltaic System Degradation Rate","booktitle":"44th IEEE PVSC","author":[{"propositions":[],"lastnames":["Livera"],"firstnames":["Andreas"],"suffixes":[]},{"propositions":[],"lastnames":["Phinikarides"],"firstnames":["Alexander"],"suffixes":[]},{"propositions":[],"lastnames":["Makrides"],"firstnames":["George"],"suffixes":[]},{"propositions":[],"lastnames":["Georghiou"],"firstnames":["George","E."],"suffixes":[]}],"year":"2017","pages":"1954–1958","doi":"10.1109/PVSC.2017.8366442","abstract":"In this paper, the impact of missing data and the robustness of commonly used statistical techniques to calculate the annual degradation rate of crystalline-Silicon (c-Si) photovoltaic (PV) systems was analyzed. In addition, the performance of different imputation techniques was assessed in order to develop an optimal methodology for treating missing data for degradation rate estimation studies. The results obtained clearly demonstrate that the application of the different statistical methods to estimate the annual degradation rate is sensitive to the amount of missing data, since all the statistical methods underestimated the degradation rate consistently with the increasing level of missing data. In addition, the application of the Seasonal Decomposition (Seas) technique yielded robust annual degradation rate estimates since for a level of 40 % of missing data, the absolute percentage error (APE) of the annual degradation rate estimated with all statistical techniques, was less than 7.5 %. Finally, Classical Seasonal Decomposition (CSD) was shown to be the most robust technique for estimating the degradation rate when imputation was applied, while Autoregressive Integrated Moving Average (ARIMA) was the most successful technique in providing robust degradation rate estimates when not applying any imputation.","copyright":"All rights reserved","file":"/home/alexis/Zotero/storage/6748IMCH/Livera et al. - 2017 - Impact of Missing Data on the Estimation of Photov.pdf;/home/alexis/Zotero/storage/M8M5Z7DM/Livera et al. - 2017 - Impact of Missing Data on the Estimation of Photov.pdf","bibtex":"@inproceedings{liveraImpactMissingData2017,\n title = {Impact of {{Missing Data}} on the {{Estimation}} of {{Photovoltaic System Degradation Rate}}},\n booktitle = {44th {{IEEE PVSC}}},\n author = {Livera, Andreas and Phinikarides, Alexander and Makrides, George and Georghiou, George E.},\n year = {2017},\n pages = {1954--1958},\n doi = {10.1109/PVSC.2017.8366442},\n abstract = {In this paper, the impact of missing data and the robustness of commonly used statistical techniques to calculate the annual degradation rate of crystalline-Silicon (c-Si) photovoltaic (PV) systems was analyzed. In addition, the performance of different imputation techniques was assessed in order to develop an optimal methodology for treating missing data for degradation rate estimation studies. The results obtained clearly demonstrate that the application of the different statistical methods to estimate the annual degradation rate is sensitive to the amount of missing data, since all the statistical methods underestimated the degradation rate consistently with the increasing level of missing data. In addition, the application of the Seasonal Decomposition (Seas) technique yielded robust annual degradation rate estimates since for a level of 40 \\% of missing data, the absolute percentage error (APE) of the annual degradation rate estimated with all statistical techniques, was less than 7.5 \\%. Finally, Classical Seasonal Decomposition (CSD) was shown to be the most robust technique for estimating the degradation rate when imputation was applied, while Autoregressive Integrated Moving Average (ARIMA) was the most successful technique in providing robust degradation rate estimates when not applying any imputation.},\n copyright = {All rights reserved},\n file = {/home/alexis/Zotero/storage/6748IMCH/Livera et al. - 2017 - Impact of Missing Data on the Estimation of Photov.pdf;/home/alexis/Zotero/storage/M8M5Z7DM/Livera et al. - 2017 - Impact of Missing Data on the Estimation of Photov.pdf}\n}\n\n","author_short":["Livera, A.","Phinikarides, A.","Makrides, G.","Georghiou, G. E."],"key":"liveraImpactMissingData2017","id":"liveraImpactMissingData2017","bibbaseid":"livera-phinikarides-makrides-georghiou-impactofmissingdataontheestimationofphotovoltaicsystemdegradationrate-2017","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"https://alexisph.github.io/alexander.bib","dataSources":["EdXGkEcjMZQF5CuiR","wkdr2EqAZ8v976o9g"],"keywords":[],"search_terms":["impact","missing","data","estimation","photovoltaic","system","degradation","rate","livera","phinikarides","makrides","georghiou"],"title":"Impact of Missing Data on the Estimation of Photovoltaic System Degradation Rate","year":2017}