Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images

Alessandro Niccolai, Seyedamir Orooji, Andrea Matteri, Emanuele Ogliari and Sonia Leva
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Alessandro Niccolai: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Seyedamir Orooji: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Andrea Matteri: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Emanuele Ogliari: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Sonia Leva: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy

Forecasting, 2022, vol. 4, issue 1, 1-11

Abstract: 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 SolarTech LAB 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.

Keywords: deep learning; infrared sky images; irradiance nowcasting; PV production forecasting (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: 2022
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