EconPapers    
Economics at your fingertips  
 

High-Performance Solar Cells by Machine Learning and Pareto Optimality

Giovanni Nastasi (), Vittorio Romano () and Giuseppe Nicosia ()
Additional contact information
Giovanni Nastasi: University of Catania
Vittorio Romano: University of Catania
Giuseppe Nicosia: University of Catania

A chapter in Handbook of Smart Energy Systems, 2023, pp 1265-1272 from Springer

Abstract: Abstract Photovoltaic energy represents a keystone for the transition to renewable energy. A crucial point in the design of new technologies is represented by the optimization of solar cell structures. Here we review the main results obtained using multiobjective optimization algorithms, machine learning techniques, and new perspectives given by organic solar cells.

Keywords: Solar cells; Multi-objective optimization; Pareto optimality; Machine learning (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-030-97940-9_166

Ordering information: This item can be ordered from
http://www.springer.com/9783030979409

DOI: 10.1007/978-3-030-97940-9_166

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-02
Handle: RePEc:spr:sprchp:978-3-030-97940-9_166