Abstract

The forecasting techniques are affected by the renewable sources randomness. Improvements of the prediction models with more accurate results and lower error are necessary for future development of the microgrids projects and of the economic dispatch sector. The LS-SVM (Least Square Support Vector Machine), a relatively unexplored neural network known as GMDH (Group Method of Data Handling) and a novel hybrid algorithm GLSSVM (Group Least Square Support Vector Machine), based on the combination of the first two models, were implemented to forecast the PV (Photovoltaic) output power at several time horizons up to 24 h. In order to improve the forecasting accuracy, each model was combined with three strategies for multi-step ahead forecast (Direct, Recursive and DirRec). A detail analysis of the normalized mean error is carried out to compare the different forecasting methods, using the historical PV output power data of a 960 kWP grid connected PV system in the south of Italy. The outcomes demonstrate the GLSSVM method with the DirRec strategy can give a normalized error of 2.92% under different weather conditions with evident improvements respect to the traditional ANN (Artificial Neural Network)

Autore Pugliese

• Congedo Paolo Maria , De Giorgi Maria Grazia

Tutti gli autori

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

Titolo volume/Rivista

ENERGY

Anno di pubblicazione

2016

ISSN

0360-5442

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

21

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile