The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications. © 2015 by the authors; licensee MDPI, Basel, Switzerland.

A physical hybrid artificial neural network for short term forecasting of PV plant power output

DOLARA, ALBERTO;GRIMACCIA, FRANCESCO;LEVA, SONIA;MUSSETTA, MARCO;OGLIARI, EMANUELE GIOVANNI CARLO
2015-01-01

Abstract

The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications. © 2015 by the authors; licensee MDPI, Basel, Switzerland.
Artificial neural network (ANN); Energy forecasting; Renewable energy source (RES) integration; Computer Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/970338
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