Oblačna tehnologija in napovedovanje proizvodnje sončnih celic s strojnim učenjem : magistrsko delo

Nabergoj, Veronika (author), Bernik, Janez (mentor)

V magistrskem delu je opisano delovanje oblačne tehnologije in vse storitve, ki jih vključuje. V sklopu oblačne tehnologije so opisane tudi prednosti in slabosti takšnega načina dela. Opisano je tudi strojno učenje, podrobno pa je opisan algoritem XGBoost, ki je bil tudi uporabljen pri napovedovanju proizvodnje sončnih celic. S pomočjo oblačnih storitev je bilo vzpostavljeno tudi napovedovanje proizvodnje sončnih celic. Učenje modelov in napovedovanje je potekalo s pomočjo strojnega učenja. Vsi podatki in potek dela so opisani in ustrezno prikazani.

• Language: Slovenian
• Keywords: strojno učenje, napoved proizvodnje sončnih celic, oblačna tehnologija, trgovanje z električno energijo
• Work type: Master's thesis/paper
• Typology: 2.09 - Master's Thesis
• Organization: FMF - Faculty of Mathematics and Physics
• Year: 2023
• PID: 20.500.12556/RUL-144958
• UDC: 004.4
• COBISS.SI-ID: 146443267
• Publication date in RUL: 25.03.2023

Secondary language

• Language: English
• Title: Cloud Technology and Solar Panel Production Prediction with Machine Learning

Abstract

In this master's thesis the cloud technology and all the services they include are described. In the cloud technology part of the thesis advantages and disadvantages of working with the services are stated. The machine learning part is also described with an emphasis on the algorithm XGBoost that was used for solar panel production prediction. Solar panel production prediction has been constructed with the help of cloud services. The model training and prediction was performed with machine learning. All the data used and the steps taken are described and suitably presented.

• Keywords: machine learning, solar panel production prediction, cloud technology, electricity trading