A Hybrid Technique for Day-Ahead PV Generation Forecasting Using Clear-Sky Models or Ensemble of Artificial Neural Networks According to a Decision Tree Approach

Stefano Massucco, Gabriele Mosaico, Matteo Saviozzi and Federico Silvestro
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Stefano Massucco: Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy
Gabriele Mosaico: Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy
Matteo Saviozzi: Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy
Federico Silvestro: Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy

Energies, 2019, vol. 12, issue 7, 1-21

Abstract: PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.

Keywords: PV forecasting; hybrid method; clear-sky model; artificial neural networks; basic ensemble method; decision trees; CART tree; weather type partition; weather classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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