In this paper a forecasting method for the Next Day's energy production forecast is proposed with respect to photovoltaic plants. A new hybrid method PHANN (Physical Hybrid Artificial Neural Network) based on Artificial Neural Network (ANN) and basic Physical constraints of the PV plant, is presented and compared with an ANN standard method. Furthermore, the accuracy of the two methods have been studied in order to better understand the intrinsic error committed by the PHANN, reporting some numerical results. This computing-based hybrid approach is proposed for PV energy forecasting in view of optimal usage and management of RES in future smart grid applications.

Hybrid model analysis and validation for PV energy production forecasting

GANDELLI, ALESSANDRO;GRIMACCIA, FRANCESCO;LEVA, SONIA;MUSSETTA, MARCO;OGLIARI, EMANUELE GIOVANNI CARLO
2014-01-01

Abstract

In this paper a forecasting method for the Next Day's energy production forecast is proposed with respect to photovoltaic plants. A new hybrid method PHANN (Physical Hybrid Artificial Neural Network) based on Artificial Neural Network (ANN) and basic Physical constraints of the PV plant, is presented and compared with an ANN standard method. Furthermore, the accuracy of the two methods have been studied in order to better understand the intrinsic error committed by the PHANN, reporting some numerical results. This computing-based hybrid approach is proposed for PV energy forecasting in view of optimal usage and management of RES in future smart grid applications.
2014
2014 International Joint Conference on Neural Networks
9781479914845
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/929961
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