| KEYWORD |
ELECTRONIC DESIGN AUTOMATION - EDA
Machine Learning techniques to forecast energy produced by Wave Energy Converters
Parole chiave ARTIFICIAL INTELLIGENCE, ARTIFICIAL NEURAL NETWORK, DEEP NEURAL NETWORKS, FORECAST, RENEWABLE ENERGY, WAVE ENERGY
Riferimenti EDOARDO PATTI
Riferimenti esterni Rafael Fontana Crespo (rafael.fontana@gmail.com
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 Wave Energy has recently emerged as a serious contender in the renewables field. To transform the power from the waves into electrical Energy, a Wave Energy Converter (WEC) is needed. However, the power generated from wave energy fluctuates. In the context of a Smart Grid Scenario, the accurate prediction of the power generated by the WEC and delivered to the grid is crucial to enable the full penetration of WECs in the smart grid. To this end, advanced machine learning (ML) techniques for forecasting energy production can be implemented.
This thesis aims to develop an innovative methodology to forecast the power delivered to the grid of WEC devices by taking advantage of advanced deep learning techniques (e.g. Long-Short Term Memory (LSTM) Neural Network, Transformer Neural Network (TNN), etc). In detail, the model would consider past measurements of the power delivered to the grid, as well as other exogenous inputs such as rotor speed, tilt, buoy, wind speed, etc. Additionally, the methodology will employ Prediction Intervals to calculate the inherent uncertainties.
The ideal candidate should be familiar with Python.
Conoscenze richieste Python
Scadenza validita proposta 30/10/2025
PROPONI LA TUA CANDIDATURA