RT journal article
T1 Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study
A1 Nespoli, Alfredo
A1 Mussetta, Marco
A1 Ogliari, Emanuele
A1 Leva, Sonia
A1 Fernández Ramírez, Luis Miguel
A1 García Triviño, Pablo
A2 Ingeniería Eléctrica
K1 photovoltaic
K1 power forecast
K1 day ahead
K1 artificial neural network
K1 short term
AB Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications.
PB MDPI
SN 2079-9292
YR 2019
FD 2019-12
LK http://hdl.handle.net/10498/22335
UL http://hdl.handle.net/10498/22335
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026