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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Phinikarides, A. Makrides, G. Kindyni, N. Kyprianou, A. Georghiou, G.E. |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Cyprus, Nicosia, Cyprus (Phinikarides, A.; Makrides, G.; Kindyni, N.; Georghiou, G.E.) || Dept. of Mech. & Manuf. Eng., Univ. of Cyprus, Nicosia, Cyprus (Kyprianou, A.) |
| Abstract | In this paper, the performance of different technology photovoltaic (PV) systems was modeled using autoregressive integrated moving average (ARIMA) processes. Measurements from mono-crystalline (mono-c-Si), multi-crystalline (multi-c-Si) and amorphous (a-Si) silicon, cadmium telluride (CdTe) and copper indium gallium diselenide (CIGS) systems were used to construct monthly dc performance ratio (PR) time-series, from outdoor measurements. Each PR time-series was modeled a) with multiplicative ARIMA, b) with linear regression and c) with Seasonal-Trend Decomposition by Loess (STL) using the first 4 years of each time-series in order to compare the accuracy of the different methods. The models were used to forecast the PR of the $5^{th}$ year of the different PV technologies and the results from the aforementioned statistical methods were compared based on the root-mean-square error (RMSE). The results showed that ARIMA produced the lowest RMSE for crystalline silicon (c-Si) technologies, whereas for thin-film technologies, STL was more accurate. The results from ARIMA also showed that thin-film technologies were optimally modeled with identical model orders, whereas for c-Si, each technology required a different optimal model order. |
| Sponsorship | IEEE Electron Devices Soc. |
| Starting Page | 0797 |
| Ending Page | 0801 |
| File Size | 318460 |
| Page Count | 5 |
| File Format | |
| DOI | 10.1109/PVSC.2013.6744268 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Forecasting Predictive models Photovoltaic systems Silicon Linear regression Temperature measurement performance ratio photovoltaic systems modeling autoregressive processes performance evaluation forecasting |
| Content Type | Text |
| Resource Type | Article |
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