PV Plant

AUTHORS:
Andrea Masini, Ciro Lanzetta, Giuseppe Leotta, Gian Lorenzo Giuliattini Burbui, Pasquale Guerrisi and Maria Luisa Lo Trovato.
ABSTRACT:
Nowadays remote sensing information can be exploited to support the management of renewable energy plants during all its life cycles. Earth observation satellite can provide a continuous monitoring of each location of the earth. Low-resolution and high-resolution imagery can be exploited to obtain accurate descriptions of the monitored scenarios/plants. Moreover, the use of unmanned air vehicle can provide complementary information to monitor important features not detectable with satellite sensors. i-EM and Flyby developed and tested a system, called 4D-REDSS (4D Renewable plants Decision Support System) aimed to exploit remote sensed data to advantage and support the management of solar plant during the construction and pre-commissioning phase.

AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Fabrizio Ruffini, Ciro Lanzetta, Antonio Piazzi, Mauro Tucci e Michela Moschella.
ABSTRACT:
An innovative ensamble-based forecasting method based on a number of machine learning techniques independently trained and combined in a cooperative ensemble fashion.

AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper, we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.