Data on Support Vector Machines (SVM) model to forecast photovoltaic power

M Malvoni; M G De Giorgi; P M Congedo

doi:10.1016/j.dib.2016.08.024

Data on Support Vector Machines (SVM) model to forecast photovoltaic power

Data Brief. 2016 Aug 18:9:13-6. doi: 10.1016/j.dib.2016.08.024. eCollection 2016 Dec.

Authors

M Malvoni¹, M G De Giorgi¹, P M Congedo¹

Affiliation

¹ Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy.

Abstract

The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled "Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data" (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

Keywords: Forecast photovoltaic; Least Squares Support Vector Machines (LS-SVM).