Abstract

The main crucial and challenging issue in solar energy production is the intermittency of the photovoltaic (PV) system due to weather conditions. In particular, the variation of the temperature and irradiance can have a profound impact on the quality of electric power production. A drop of more than 20% of power PV production can be observed in real PV energy plants. This fact usually limits the integration of PV systems into the power grid. Hence, accurately forecasting the power output of PV modules in a short-term is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. In this project, the student will perform real data analysis, and learn how to employ machine-learning methods for short-term forecasting of photovoltaic power generation

Deliverables

Apply and compare different machine learning methods to forecast photovoltaic power production.