Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches

Alessandro Niccolai, Alberto Dolara and Emanuele Ogliari
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Alessandro Niccolai: Dipartimento di Energia, University of Politecnico di Milano, 20156 Milano, Italy
Alberto Dolara: Dipartimento di Energia, University of Politecnico di Milano, 20156 Milano, Italy
Emanuele Ogliari: Dipartimento di Energia, University of Politecnico di Milano, 20156 Milano, Italy

Energies, 2021, vol. 14, issue 2, 1-18

Abstract: Accurate photovoltaic (PV) prediction has a very positive effect on many problems that power grids can face when there is a high penetration of variable energy sources. This problem can be addressed with computational intelligence algorithms such as neural networks and Evolutionary Optimization. The purpose of this article is to analyze three different hybridizations between physical models and artificial neural networks: the first hybridization combines neural networks with the output of the five-parameter physical model of a photovoltaic module in which the parameters are obtained from a datasheet. In the second hybridization, the parameters are obtained from a matching procedure with historical data exploiting Social Network Optimization. Finally, the third hybridization is PHANN, in which clear sky irradiation is used as an input. These three hybrid methods are compared with two physical approaches and simple neural network-based forecasting. The results show that the hybridization is very effective for achieving good forecasting results, while the performance of the three hybrid methods is comparable.

Keywords: photovoltaic forecasting; artificial neural networks; evolutionary algorithms; PHANN; hybrid models; Social Network Optimization (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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