@inproceedings{phinikaridesEstimationDegradationRate2016,
  title = {Estimation of the {{Degradation Rate}} of {{Fielded Photovoltaic Arrays}} in the {{Presence}} of {{Measurement Outages}}},
  booktitle = {32nd {{EU-PVSEC}}},
  author = {Phinikarides, Alexander and Makrides, George and Georghiou, George E},
  year = {2016},
  pages = {1754--1757},
  address = {{Munich, Germany}},
  doi = {10.4229/EUPVSEC20162016-5DO.12.6},
  abstract = {This paper presents a study of the sensitivity of the estimated energy degradation rates of grid-connected crystalline Silicon (c-Si) PV arrays to measurement outages and missing data. The arrays under study have been operating side-by-side since June 2006 in Nicosia, Cyprus. Fifteen-minute average field measurements over their first ten years of operation were used to create a data set, in which invalid measurements were removed and logged system downtimes were corrected based on past performance. The resulting data set was randomly sampled to select data points which were designated as Missing Completely at Random (MCAR) in order to create unbiased artificial outage periods. The same data set was treated with missing data imputation techniques such as imputation by the mean, linear interpolation and imputation by bootstrap. The resulting data sets were then analysed with linear regression (LR), classical seasonal decomposition (CSD) and regARIMA models to extract the trend and estimate the energy degradation rates. The results have shown that regARIMA was the most robust method for up to 10 \% of missing data, the LR and CSD methods were sensitive starting from 2 \% of missing data and that imputation by the bootstrap enhanced the accuracy of the estimated degradation rates up to 20 \% for regARIMA and 40 \% for LR.},
  copyright = {All rights reserved},
  isbn = {3-936338-41-8},
  file = {/home/alexis/Zotero/storage/A7L292XU/Phinikarides et al. - 2016 - Estimation of the Degradation Rate of Fielded Phot.pdf}
}

{"_id":"WDpJcH5YRwbfSubSh","bibbaseid":"phinikarides-makrides-georghiou-estimationofthedegradationrateoffieldedphotovoltaicarraysinthepresenceofmeasurementoutages-2016","author_short":["Phinikarides, A.","Makrides, G.","Georghiou, G. E"],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Estimation of the Degradation Rate of Fielded Photovoltaic Arrays in the Presence of Measurement Outages","booktitle":"32nd EU-PVSEC","author":[{"propositions":[],"lastnames":["Phinikarides"],"firstnames":["Alexander"],"suffixes":[]},{"propositions":[],"lastnames":["Makrides"],"firstnames":["George"],"suffixes":[]},{"propositions":[],"lastnames":["Georghiou"],"firstnames":["George","E"],"suffixes":[]}],"year":"2016","pages":"1754–1757","address":"Munich, Germany","doi":"10.4229/EUPVSEC20162016-5DO.12.6","abstract":"This paper presents a study of the sensitivity of the estimated energy degradation rates of grid-connected crystalline Silicon (c-Si) PV arrays to measurement outages and missing data. The arrays under study have been operating side-by-side since June 2006 in Nicosia, Cyprus. Fifteen-minute average field measurements over their first ten years of operation were used to create a data set, in which invalid measurements were removed and logged system downtimes were corrected based on past performance. The resulting data set was randomly sampled to select data points which were designated as Missing Completely at Random (MCAR) in order to create unbiased artificial outage periods. The same data set was treated with missing data imputation techniques such as imputation by the mean, linear interpolation and imputation by bootstrap. The resulting data sets were then analysed with linear regression (LR), classical seasonal decomposition (CSD) and regARIMA models to extract the trend and estimate the energy degradation rates. The results have shown that regARIMA was the most robust method for up to 10 % of missing data, the LR and CSD methods were sensitive starting from 2 % of missing data and that imputation by the bootstrap enhanced the accuracy of the estimated degradation rates up to 20 % for regARIMA and 40 % for LR.","copyright":"All rights reserved","isbn":"3-936338-41-8","file":"/home/alexis/Zotero/storage/A7L292XU/Phinikarides et al. - 2016 - Estimation of the Degradation Rate of Fielded Phot.pdf","bibtex":"@inproceedings{phinikaridesEstimationDegradationRate2016,\n title = {Estimation of the {{Degradation Rate}} of {{Fielded Photovoltaic Arrays}} in the {{Presence}} of {{Measurement Outages}}},\n booktitle = {32nd {{EU-PVSEC}}},\n author = {Phinikarides, Alexander and Makrides, George and Georghiou, George E},\n year = {2016},\n pages = {1754--1757},\n address = {{Munich, Germany}},\n doi = {10.4229/EUPVSEC20162016-5DO.12.6},\n abstract = {This paper presents a study of the sensitivity of the estimated energy degradation rates of grid-connected crystalline Silicon (c-Si) PV arrays to measurement outages and missing data. The arrays under study have been operating side-by-side since June 2006 in Nicosia, Cyprus. Fifteen-minute average field measurements over their first ten years of operation were used to create a data set, in which invalid measurements were removed and logged system downtimes were corrected based on past performance. The resulting data set was randomly sampled to select data points which were designated as Missing Completely at Random (MCAR) in order to create unbiased artificial outage periods. The same data set was treated with missing data imputation techniques such as imputation by the mean, linear interpolation and imputation by bootstrap. The resulting data sets were then analysed with linear regression (LR), classical seasonal decomposition (CSD) and regARIMA models to extract the trend and estimate the energy degradation rates. The results have shown that regARIMA was the most robust method for up to 10 \\% of missing data, the LR and CSD methods were sensitive starting from 2 \\% of missing data and that imputation by the bootstrap enhanced the accuracy of the estimated degradation rates up to 20 \\% for regARIMA and 40 \\% for LR.},\n copyright = {All rights reserved},\n isbn = {3-936338-41-8},\n file = {/home/alexis/Zotero/storage/A7L292XU/Phinikarides et al. - 2016 - Estimation of the Degradation Rate of Fielded Phot.pdf}\n}\n\n","author_short":["Phinikarides, A.","Makrides, G.","Georghiou, G. E"],"key":"phinikaridesEstimationDegradationRate2016","id":"phinikaridesEstimationDegradationRate2016","bibbaseid":"phinikarides-makrides-georghiou-estimationofthedegradationrateoffieldedphotovoltaicarraysinthepresenceofmeasurementoutages-2016","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"https://alexisph.github.io/alexander.bib","dataSources":["EdXGkEcjMZQF5CuiR","wkdr2EqAZ8v976o9g"],"keywords":[],"search_terms":["estimation","degradation","rate","fielded","photovoltaic","arrays","presence","measurement","outages","phinikarides","makrides","georghiou"],"title":"Estimation of the Degradation Rate of Fielded Photovoltaic Arrays in the Presence of Measurement Outages","year":2016}