Your browser does not support JavaScript!

Post-graduate theses

Current Record: 39 of 123

Back to Results Previous page
Next page
Add to Basket
[Add to Basket]
Identifier 000443112
Title Machine learning techniques for the estimation of the operating parameters of solar cells
Alternative Title Τεχνικές μηχανικής μάθησης για την εκτίμηση των παραμέτρων λειτουργίας των ηλιακών κελιών
Author Παπαδομιχελάκης, Γεώργιος
Thesis advisor Κατσαούνης, Θεόδωρος
Reviewer Πλεξουσάκης, Μιχαήλ
Χαρμανδάρης ,Ευάγγελος
Abstract We consider the problem of predicting the internal temperature in photovoltaic cells depending on ambient and/or internal factors. In this thesis, we use machine learning techniques, specifically deep learning and neural networks to accurately forecast the temperature, using methods we developed. We present an introduction to the mathematical background of neural networks and build some using the Python3 programming language and TensorFlow. Lastly, we present the numerical results comparing what our neural networks managed to predict to the actual temperatures measured.
Language English
Subject Artificial neural networks
Τεχνιτά νευρωνικά δίκτυα
Issue date 2021-11-26
Collection   School/Department--School of Sciences and Engineering--Department of Mathematics and Applied Mathematics--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/f/8/5/metadata-dlib-1635323396-504340-2466.tkl Bookmark and Share
Views 251

Digital Documents
No preview available

Download document
View document
Views : 9