Sun Position Identification in Sky Images for Nowcasting Application

Alessandro Niccolai and Alfredo Nespoli
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Alessandro Niccolai: Politecnico di Milano, Dipartimento di Energia, Via La Masa, 34, 20156 Milan, Italy
Alfredo Nespoli: Politecnico di Milano, Dipartimento di Energia, Via La Masa, 34, 20156 Milan, Italy

Forecasting, 2020, vol. 2, issue 4, 1-17

Abstract: Very-short-term photovoltaic power forecast, namely nowcasting, is gaining increasing attention to face grid stability issues and to optimize microgrid energy management systems in the presence of large penetration of renewable energy sources. In order to identify local phenomena as sharp ramps in photovoltaic production, whole sky images can be used effectively. The first step in the implementation of new and effective nowcasting algorithms is the identification of Sun positions. In this paper, three different techniques (solar angle-based, image processing-based, and neural network-based techniques) are proposed, described, and compared. These techniques are tested on real images obtained with a camera installed at SolarTech Lab at Politecnico di Milano, Milan, Italy. Finally, the three techniques are compared by introducing some performance parameters aiming to evaluate of their reliability, accuracy, and computational effort. The neural network-based technique obtains the best performance: in fact, this method is able to identify accurately the Sun position and to estimate it when the Sun is covered by clouds.

Keywords: photovoltaic forecasting; nowcasting; image processing; solar position; neural networks (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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