Malvoni M, De Giorgi M G, Congedo P M
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy.
Data Brief. 2016 Aug 18;9:13-6. doi: 10.1016/j.dib.2016.08.024. eCollection 2016 Dec.
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.
这些数据涉及光伏（PV）功率，由一个混合模型预测，该模型考虑天气变化并应用一种技术来减小输入数据大小，如题为《基于降维数据的混合主成分分析-最小二乘支持向量机的光伏预测》（M. 马尔沃尼、M.G. 德乔吉、P.M. 孔杰多，2015年）[1]的论文中所述。二次Renyi熵准则与主成分分析（PCA）一起应用于最小二乘支持向量机（LS-SVM），以预测提前一天时间范围内的光伏功率。这里共享的数据代表了所提出方法的结果。补充材料中提供了提前1、3、6、12、24小时以及不同数据缩减规模下的每小时光伏功率预测。
