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- W4310929479 abstract "Photovoltaic energy is very sensitive to the variation of the climatic parameters such as the solar radiation and the ambient temperature, which are characterized by their intermittent behavior. Thus, to optimize the use of the electrical power generated by the PV plant, it is necessary to have a good prediction of the PV generation. In this paper, 15-minute interval experimental measurements of the solar radiation, the ambient temperature and photovoltaic generation that correspond to the four seasons (winter, spring, summer and autumn) are used to develop models for the prediction of the PV power of a 10 kW polycrystalline photovoltaic plant installed in Mostoles (Madrid, Spain). Hence, a combination of prediction models which are the double exponential smoothing and autoregressive integrated moving average (ARIMAX) are applied to predict the PV generation. The results obtained illustrate the good performance of the proposed prediction models as a response for several weather measurements." @default.
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- W4310929479 date "2022-10-17" @default.
- W4310929479 modified "2023-09-27" @default.
- W4310929479 title "Application of the Double Smoothing and ARIMAX Methods for the Prediction of Polycristalline Photovoltaic Generation" @default.
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- W4310929479 doi "https://doi.org/10.1109/iecon49645.2022.9968612" @default.
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