| KEYWORD |
Area Engineering
Forecasting of photovoltaic power production and energy load
keywords DEEP LEARNING, MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS, MACHINE LEARNING, DEEP LEARNING, OPTIMIZATION, PHOTOVOLTAICS, PYTHON, REINFORCEMENT LEARNING
Reference persons EROS GIAN ALESSANDRO PASERO, VINCENZO RANDAZZO
Research Groups Neuronics (Artificial Neural Networks)
Thesis type EXPERIMENTAL, EXPERIMENTAL APPLIED
Description Recent years have seen a rise in the importance of renewable energy sources due to the advantages they provide, in terms of the preservation of the environment and inexhaustible supply of energy. In particular, photovoltaic (PV) energy will be prioritized in the future and it is expanding worldwide. However, the high variability of solar irradiance and, consequently, of PV power generation causes uncertainty in the planning and operation of power systems. Thus, the forecasting of PV power generation is crucial to solve this issue. LSTM-based methods were compared for the 6-hour-ahead and day-ahead forecasting of PV power generated by a PV power station, based on the Global Horizontal Irradiance and the air temperature forecasts of the same period.
Required skills passion for thesis work. basic knowledge of neural networks, deep learning and photovoltaic can be useful. otherwise, we will provide all the material
Deadline 28/02/2025
PROPONI LA TUA CANDIDATURA