Abstract

An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP gridconnected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error.

Autore Pugliese

• Congedo Paolo Maria , De Giorgi Maria Grazia

Tutti gli autori

• M.G. De Giorgi , M. Malvoni , P.M. Congedo

Titolo volume/Rivista

IET SCIENCE, MEASUREMENT & TECHNOLOGY

Anno di pubblicazione

2014

ISSN

1751-8822

ISBN

Non Disponibile

Numero di citazioni Wos

33

Ultimo Aggiornamento Citazioni

28/04/2018

Numero di citazioni Scopus

42

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile