PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

Energy systems around the world are undergoing substantial changes, with an increasing penetration of Renewable Energy Sources. For this reason, the availability of a pool of suitable forecasting models specific for the needed time horizon and task is becoming crucial in the grid operation. In addition, nowcasting techniques aiming at provideing the power forecast for the immediate future, are more often investigated due to the spread of micro-grids and the need of facing changing electrical market environments. In this paper a novel comprehensive methodology aiming at computing the PV power forecast on different time horizons and resolutions is introduced. Moving from the 24-hours ahead prediction provided by the Physical Hybrid Artificial Neural Network (PHANN), a technique to refine the power forecast for the following 3 hours with an hourly granularity is analyzed, leveraging on newer information available during the operations. Moreover, in order to provide the power forecast for the following 30 minutes on a minutely basis, an innovative modification of a statistical technique is proposed, the robust persistence. The proposed comprehensive approach allowed to greatly reduce the overall error committed when compared with the benchmark models. Finally, the proposed methodology is validated and tested on a freely available database consisting on different parameters recorded at both the meteorological and photovoltaic test facility at SolarTech
^LAB
, Politecnico di Milano, Milan.