| Autor: |
Alessandro Niccolai, Seyedamir Orooji, Andrea Matteri, Emanuele Ogliari, Sonia Leva |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Forecasting, Vol 4, Iss 1, Pp 338-348 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2571-9394 |
| DOI: |
10.3390/forecast4010019 |
| Popis: |
This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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