Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection

Alessandro Niccolai (), Emanuele Ogliari, Alfredo Nespoli, Riccardo Zich and Valentina Vanetti
Additional contact information
Alessandro Niccolai: Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy
Emanuele Ogliari: Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy
Alfredo Nespoli: Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy
Riccardo Zich: Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy
Valentina Vanetti: Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy

Energies, 2022, vol. 15, issue 24, 1-16

Abstract: Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTech LAB in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions.

Keywords: clear sky index; random forest; pattern recognition neural network; nowcasting; all-sky-cam (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/24/9433/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/24/9433/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:24:p:9433-:d:1002079

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().