2015
DOI: 10.1016/j.renene.2014.11.091
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Analysis of photovoltaic system performance time series: Seasonality and performance loss

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Cited by 77 publications
(36 citation statements)
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“…Regarding the technology-dependent degradation, 1% degradation is reported for the crystalline silicon and a-Si modules, 0.6% for the CdTe and 0.5% for the CIGS modules [43]. On average, the degradation rate is in the range 4.5%-6% and even 10% in the case of CdTe [6,[44][45][46].…”
Section: Discussionmentioning
confidence: 96%
“…Regarding the technology-dependent degradation, 1% degradation is reported for the crystalline silicon and a-Si modules, 0.6% for the CdTe and 0.5% for the CIGS modules [43]. On average, the degradation rate is in the range 4.5%-6% and even 10% in the case of CdTe [6,[44][45][46].…”
Section: Discussionmentioning
confidence: 96%
“…Regarding outdoor, change in parameters were observed depending on the location and climatic conditions (Bücher et al, 1997, Gottschalg et al, 2013. Phinikarides et al (2015) carried out an investigation on 12 grid-connected photovoltaic systems in Cyprus centered upon multi-crystalline silicon (multi-c-Si), monocrystalline silicon (mono-c-Si), Heterojunction with Intrinsic Thin layer (HIT), CdTe, CIGS and a-Si. They also observed that both the mono-c-Si and multi-c-Si technologies displayed higher seasonal variations, with greater average standard deviation when compared with thin film technologies (Phinikarides et al, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…In addition, the undersized inverter affects the seasonality of PV system performance not only from the amplitude but also the seasonal patterns (only in a-Si system). Meanwhile, the seasonal time series decomposition methods (CSD, STL and ARIMA) are superior in the degradation rate calculation with robustness and insensitivity to outliers, as recommended by other authors (Phinikarides et al, 2013;Phinikarides et al, 2015;Ye et al, 2014).…”
Section: Degradation Rate and Seasonalitymentioning
confidence: 97%
“…Among the numerous statistical software involving ARIMA, Bereau, 2016). Due to the monthly PR time series as short as three years, some ARIMA models were unable to converge and therefore, the airline model ARIMA(0, 1, 1)(0, 1, 1) 12 was pre-selected with successful description about the majority of seasonal time series (Phinikarides et al, 2015). Another fundamentally important use of X-13ARIMA-SEATS is to extend the series with forecasts as long as 5 years.…”
Section: Autoregressive Integrated Moving Averagementioning
confidence: 99%
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