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,Matteo Saviozzi,Federico Silvestro,Gabriele Mosaico

doi:10.3390/en12071298

Stefano Massucco, Matteo Saviozzi + Show 2 more

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https://doi.org/10.3390/en12071298

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Journal: Energies	Publication Date: Apr 4, 2019
Citations: 36	License type: CC BY 4.0

Affiliation: University of Genoa

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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.

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In recent years, global energy demand has increased dramatically
The results prove that the hybrid methodology outperforms all the physical models
The hybrid model, the Artificial Neural Network (ANN) Ensemble (BEM), the CCSM and the CSM models described in Section 3 are tested on two months (November–December 2018) for a total of 61 days

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Introduction

Global energy demand has increased dramatically. Several factors have contributed to this rise: the growth of the world population, the industrialization of developing countries, and the worldwide process of urbanization [1]. Among all the RESs, PhotoVoltaic (PV) systems have gained a lot of attention for their availability, low maintenance and operational cost, lifetime, ease of application, and environmental benefits. This has implied a growth of the global solar energy production from 3.7 GW (2007) to GW (2017) [2]. In this context, high PV penetration provides many environmental and economic benefits, but the stochastic behavior of the solar power may introduce technical issues (e.g., generation schedule, operating reserve, market regulation, etc.) without robust and precise forecast [3]

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