| KEYWORD |
Energy Center Lab
Deep learning techniques to forecast solar radiation
Parole chiave ARTIFICIAL INTELLIGENCE, ARTIFICIAL NEURAL NETWORK, DEEP NEURAL NETWORKS, FORECAST, RENEWABLE ENERGY, SOLAR RADIATION
Riferimenti EDOARDO PATTI
Riferimenti esterni Alessandro Aliberti (alessandro.aliberti@polito.it), Marco Castangia (marco.castangia@polito.it)
Gruppi di ricerca DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ELECTRONIC DESIGN AUTOMATION - EDA, Energy Center Lab, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ICT4SS - ICT FOR SMART SOCIETIES
Tipo tesi SPERIMENTALE
Descrizione The accurate prediction of solar radiation in the short-term can finally enable the full penetration of renewable energy sources into the smart grids. The correlation between solar radiation and meteorological factors has been already investigated by scientists, showing that variables like cloud coverage, water vapours and atmospheric aerosols can significantly attenuate the amount of solar radiation reaching the Earth's surface.
This thesis aims at developing an innovative methodology to forecast solar irradiation by taking advantage of advanced deep learning techniques to implement a statistical model for solar radiation forecasting. In detail, the model should leverage images/scans from satellites in order to improve the state-of-the-art in solar radiation forecasting, taking into account both spatial and temporal information influencing solar variations. The ideal candidate should be familiar with Python.
Conoscenze richieste Python
Scadenza validita proposta 25/01/2025
PROPONI LA TUA CANDIDATURA