Attitude Determination in Space with Ambient Light Sensors using Machine Learning for Solar Cell Characterization. Issue 11 (8th November 2022)

• Record Type: Journal Article
• Authors: Reb, Lennart K.; Böhmer, Michael; Predeschly, Benjamin; Spanier, Lukas V.; Dreißigacker, Christoph; Meyer, Andreas; Müller-Buschbaum, Peter
• Is Part Of: Solar RRL. Volume 6:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-08
• Subjects: Solar energy -- Periodicals; Photovoltaic power generation -- Periodicals; Solar energy -- Research -- Periodicals; Photovoltaic power generation -- Research -- Periodicals; Periodicals; 333.7923
• DOI: 10.1002/solr.202270112
• Languages: English
• ISSNs: 2367-198X

Abstract

Abstract : Organic Solar Cells In article number 2200537, Peter Müller‐Buschbaum and co‐workers present a new method for solar position triangulation using ambient light sensors, which determines the solar irradiance for evaluating the performance of novel material solar cells in space. The approach uses machine learning and can be applied to space or terrestrial solar cell applications.