Forecasting of PV Power Generation using weather input data‐preprocessing techniques. (September 2017)

• Record Type: Journal Article
• Authors: Malvoni, Maria; De Giorgi, Maria Grazia; Congedo, Paolo Maria
• Is Part Of: Energy procedia. Volume 126(2017)
• Page Start: 651
• Page End: 658
• Publication Date: 2017-09
• Subjects: data-driven forecast -- data preprocessing -- GLSSVM -- wavelet -- PCA -- day-ahead forecast -- photovoltaic -- solar power; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.08.293
• Languages: English
• ISSNs: 1876-6102

Abstract

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.