@inproceedings{kyprianouForecastingDegradationRates2016,
  title = {Forecasting {{Degradation Rates}} of {{Different Photovoltaic Systems Using Robust Principal Component Analysis}} and {{ARIMA}}},
  booktitle = {32nd {{EU-PVSEC}}},
  author = {Kyprianou, Andreas and Phinikarides, Alexander and Makrides, George and Georghiou, George E},
  year = {2016},
  pages = {2033--2035},
  address = {{Munich, Germany}},
  doi = {10.4229/EUPVSEC20162016-5BV.2.58},
  abstract = {Degradation rates based on forecasting of performance ratio (PR), Rp, time series are computed and compared with actual degradation rates. A three year forecasting of monthly PR, measured from PV connected systems of various technolgies is performed using the seasonal ARIMA (SARIMA) time series model. The seasonal ARIMA model is estimated using monthly PR measured over a 5 year period and based on this model forecasting is implemented for the subsequent three years. The degradation rate  at the end of the forecasting period, eighth year, is computed using a robust principal component analysis (RCPA) based methodology. The degradation rates obtained for various (PV) systems are then compared to the ones obtained using the actual eight year data.},
  copyright = {All rights reserved},
  isbn = {3-936338-41-8},
  file = {/home/alexis/Zotero/storage/59RXHUWR/Kyprianou et al. - 2016 - Forecasting Degradation Rates of Different Photovo.pdf}
}

{"_id":"qayyR6scSybJnHSLE","bibbaseid":"kyprianou-phinikarides-makrides-georghiou-forecastingdegradationratesofdifferentphotovoltaicsystemsusingrobustprincipalcomponentanalysisandarima-2016","author_short":["Kyprianou, A.","Phinikarides, A.","Makrides, G.","Georghiou, G. E"],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Forecasting Degradation Rates of Different Photovoltaic Systems Using Robust Principal Component Analysis and ARIMA","booktitle":"32nd EU-PVSEC","author":[{"propositions":[],"lastnames":["Kyprianou"],"firstnames":["Andreas"],"suffixes":[]},{"propositions":[],"lastnames":["Phinikarides"],"firstnames":["Alexander"],"suffixes":[]},{"propositions":[],"lastnames":["Makrides"],"firstnames":["George"],"suffixes":[]},{"propositions":[],"lastnames":["Georghiou"],"firstnames":["George","E"],"suffixes":[]}],"year":"2016","pages":"2033–2035","address":"Munich, Germany","doi":"10.4229/EUPVSEC20162016-5BV.2.58","abstract":"Degradation rates based on forecasting of performance ratio (PR), Rp, time series are computed and compared with actual degradation rates. A three year forecasting of monthly PR, measured from PV connected systems of various technolgies is performed using the seasonal ARIMA (SARIMA) time series model. The seasonal ARIMA model is estimated using monthly PR measured over a 5 year period and based on this model forecasting is implemented for the subsequent three years. The degradation rate at the end of the forecasting period, eighth year, is computed using a robust principal component analysis (RCPA) based methodology. The degradation rates obtained for various (PV) systems are then compared to the ones obtained using the actual eight year data.","copyright":"All rights reserved","isbn":"3-936338-41-8","file":"/home/alexis/Zotero/storage/59RXHUWR/Kyprianou et al. - 2016 - Forecasting Degradation Rates of Different Photovo.pdf","bibtex":"@inproceedings{kyprianouForecastingDegradationRates2016,\n title = {Forecasting {{Degradation Rates}} of {{Different Photovoltaic Systems Using Robust Principal Component Analysis}} and {{ARIMA}}},\n booktitle = {32nd {{EU-PVSEC}}},\n author = {Kyprianou, Andreas and Phinikarides, Alexander and Makrides, George and Georghiou, George E},\n year = {2016},\n pages = {2033--2035},\n address = {{Munich, Germany}},\n doi = {10.4229/EUPVSEC20162016-5BV.2.58},\n abstract = {Degradation rates based on forecasting of performance ratio (PR), Rp, time series are computed and compared with actual degradation rates. A three year forecasting of monthly PR, measured from PV connected systems of various technolgies is performed using the seasonal ARIMA (SARIMA) time series model. The seasonal ARIMA model is estimated using monthly PR measured over a 5 year period and based on this model forecasting is implemented for the subsequent three years. The degradation rate at the end of the forecasting period, eighth year, is computed using a robust principal component analysis (RCPA) based methodology. The degradation rates obtained for various (PV) systems are then compared to the ones obtained using the actual eight year data.},\n copyright = {All rights reserved},\n isbn = {3-936338-41-8},\n file = {/home/alexis/Zotero/storage/59RXHUWR/Kyprianou et al. - 2016 - Forecasting Degradation Rates of Different Photovo.pdf}\n}\n\n","author_short":["Kyprianou, A.","Phinikarides, A.","Makrides, G.","Georghiou, G. E"],"key":"kyprianouForecastingDegradationRates2016","id":"kyprianouForecastingDegradationRates2016","bibbaseid":"kyprianou-phinikarides-makrides-georghiou-forecastingdegradationratesofdifferentphotovoltaicsystemsusingrobustprincipalcomponentanalysisandarima-2016","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"https://alexisph.github.io/alexander.bib","dataSources":["EdXGkEcjMZQF5CuiR","wkdr2EqAZ8v976o9g"],"keywords":[],"search_terms":["forecasting","degradation","rates","different","photovoltaic","systems","using","robust","principal","component","analysis","arima","kyprianou","phinikarides","makrides","georghiou"],"title":"Forecasting Degradation Rates of Different Photovoltaic Systems Using Robust Principal Component Analysis and ARIMA","year":2016}