@article{pieriForecastingDegradationRates2017,
  title = {Forecasting Degradation Rates of Different Photovoltaic Systems Using Robust Principal Component Analysis and {{ARIMA}}},
  author = {Pieri, Elena and Kyprianou, Andreas and Phinikarides, Alexander and Makrides, George and Georghiou, George E.},
  year = {2017},
  journal = {IET Renewable Power Generation},
  issn = {1752-1416, 1752-1424},
  doi = {10.1049/iet-rpg.2017.0090},
  abstract = {Degradation rates based on forecasting of performance ratio, Rp, time series are computed and compared with actual degradation rates. A 3-year forecasting of monthly Rp , measured from photovoltaic (PV) connected systems of various technologies is performed using the seasonal auto-regressive integrating moving average (ARIMA) time series model. The seasonal ARIMA model is estimated using monthly Rp measured over a 5-year period and based on this model forecasting is implemented for the subsequent 3 years. The degradation rate at the end of the forecasting period, eighth year, is computed using a robust principal component analysis based methodology. The degradation rates obtained for various (PV) systems are then compared with the ones obtained using the actual 8-year data.},
  copyright = {All rights reserved},
  langid = {english},
  file = {/home/alexis/Zotero/storage/Y5PDRU3T/Pieri et al. - 2017 - Forecasting degradation rates of different photovo.pdf}
}

{"_id":"BtFGjG4b2jy4dy4Hy","bibbaseid":"pieri-kyprianou-phinikarides-makrides-georghiou-forecastingdegradationratesofdifferentphotovoltaicsystemsusingrobustprincipalcomponentanalysisandarima-2017","author_short":["Pieri, E.","Kyprianou, A.","Phinikarides, A.","Makrides, G.","Georghiou, G. E."],"bibdata":{"bibtype":"article","type":"article","title":"Forecasting Degradation Rates of Different Photovoltaic Systems Using Robust Principal Component Analysis and ARIMA","author":[{"propositions":[],"lastnames":["Pieri"],"firstnames":["Elena"],"suffixes":[]},{"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":"2017","journal":"IET Renewable Power Generation","issn":"1752-1416, 1752-1424","doi":"10.1049/iet-rpg.2017.0090","abstract":"Degradation rates based on forecasting of performance ratio, Rp, time series are computed and compared with actual degradation rates. A 3-year forecasting of monthly Rp , measured from photovoltaic (PV) connected systems of various technologies is performed using the seasonal auto-regressive integrating moving average (ARIMA) time series model. The seasonal ARIMA model is estimated using monthly Rp measured over a 5-year period and based on this model forecasting is implemented for the subsequent 3 years. The degradation rate at the end of the forecasting period, eighth year, is computed using a robust principal component analysis based methodology. The degradation rates obtained for various (PV) systems are then compared with the ones obtained using the actual 8-year data.","copyright":"All rights reserved","langid":"english","file":"/home/alexis/Zotero/storage/Y5PDRU3T/Pieri et al. - 2017 - Forecasting degradation rates of different photovo.pdf","bibtex":"@article{pieriForecastingDegradationRates2017,\n title = {Forecasting Degradation Rates of Different Photovoltaic Systems Using Robust Principal Component Analysis and {{ARIMA}}},\n author = {Pieri, Elena and Kyprianou, Andreas and Phinikarides, Alexander and Makrides, George and Georghiou, George E.},\n year = {2017},\n journal = {IET Renewable Power Generation},\n issn = {1752-1416, 1752-1424},\n doi = {10.1049/iet-rpg.2017.0090},\n abstract = {Degradation rates based on forecasting of performance ratio, Rp, time series are computed and compared with actual degradation rates. A 3-year forecasting of monthly Rp , measured from photovoltaic (PV) connected systems of various technologies is performed using the seasonal auto-regressive integrating moving average (ARIMA) time series model. The seasonal ARIMA model is estimated using monthly Rp measured over a 5-year period and based on this model forecasting is implemented for the subsequent 3 years. The degradation rate at the end of the forecasting period, eighth year, is computed using a robust principal component analysis based methodology. The degradation rates obtained for various (PV) systems are then compared with the ones obtained using the actual 8-year data.},\n copyright = {All rights reserved},\n langid = {english},\n file = {/home/alexis/Zotero/storage/Y5PDRU3T/Pieri et al. - 2017 - Forecasting degradation rates of different photovo.pdf}\n}\n\n","author_short":["Pieri, E.","Kyprianou, A.","Phinikarides, A.","Makrides, G.","Georghiou, G. E."],"key":"pieriForecastingDegradationRates2017","id":"pieriForecastingDegradationRates2017","bibbaseid":"pieri-kyprianou-phinikarides-makrides-georghiou-forecastingdegradationratesofdifferentphotovoltaicsystemsusingrobustprincipalcomponentanalysisandarima-2017","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","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","pieri","kyprianou","phinikarides","makrides","georghiou"],"title":"Forecasting Degradation Rates of Different Photovoltaic Systems Using Robust Principal Component Analysis and ARIMA","year":2017}