Work

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

Year: 2022

Type: article

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 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.

Source: Forecasting

DOI: 10.3390/forecast4010019

Authors: Alessandro Niccolai, Seyedamir Orooji, Andrea Matteri, Emanuele Ogliari, Sonia Leva

Institution: Politecnico di Milano

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Climate action, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,466