Stochastic nature of weather conditions influences the photovoltaic power forecasts. The present work investigates the accuracy performance of data-driven methods for PV power ahead prediction when different data preprocessing techniques are applied to input datasets. The Wavelet Decomposition and the Principal Component Analysis were proposed to decompose meteorological data used as inputs for the forecasts. A time series forecasting method as the GLSSVM (Group Least Square Support Vector Machine) that combines the Least Square Support Vector Machines (LS-SVM) and Group Method of Data Handling (GMDH) was applied to the measured weather data and implemented for day-ahead PV generation forecast.

Forecasting of PV Power Generation using weather input data-preprocessing techniques

Maria Malvoni
Investigation
;
Maria Grazia De Giorgi
Methodology
;
Paolo Maria Congedo
Writing – Review & Editing
2017-01-01

Abstract

Stochastic nature of weather conditions influences the photovoltaic power forecasts. The present work investigates the accuracy performance of data-driven methods for PV power ahead prediction when different data preprocessing techniques are applied to input datasets. The Wavelet Decomposition and the Principal Component Analysis were proposed to decompose meteorological data used as inputs for the forecasts. A time series forecasting method as the GLSSVM (Group Least Square Support Vector Machine) that combines the Least Square Support Vector Machines (LS-SVM) and Group Method of Data Handling (GMDH) was applied to the measured weather data and implemented for day-ahead PV generation forecast.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/418279
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 70
  • ???jsp.display-item.citation.isi??? 57
social impact