Malvoni M, De Giorgi M G, Congedo P M
Department of Engineering for Innovation, University of Salento, via per Arnesano I-73100, Italy.
Data Brief. 2016 May 4;7:1639-42. doi: 10.1016/j.dib.2016.04.063. eCollection 2016 Jun.
The weather data have a relevant impact on the photovoltaic (PV) power forecast, furthermore the PV power prediction methods need the historical data as input. The data presented in this article concern measured values of ambient temperature, module temperature, solar radiation in a Mediterranean climate. Hourly samples of the PV output power of 960kWP system located in Southern Italy were supplied for more 500 days. The data sets, given in , were used in DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015) [1] to compare Artificial Neural Networks and Least Square Support Vector Machines. It was found that LS-SVM with Wavelet Decomposition (WD) outperforms ANN method. In DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016) [2] the same data were used for comparing different strategies for multi-step ahead forecast based on the hybrid Group Method of Data Handling networks and Least Square Support Vector Machine. The predicted PV power values by three models were reported in .
气象数据对光伏发电功率预测有显著影响，此外，光伏发电功率预测方法需要历史数据作为输入。本文所呈现的数据涉及地中海气候下的环境温度、组件温度、太阳辐射的测量值。提供了位于意大利南部的960千瓦光伏系统500多天的每小时光伏输出功率样本。文献[1]（DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015)）中使用了文献中给出的数据集来比较人工神经网络和最小二乘支持向量机。结果发现，带小波分解（WD）的最小二乘支持向量机优于人工神经网络方法。在文献[2]（DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016)）中，同样的数据被用于比较基于混合数据处理组网络和最小二乘支持向量机的多步提前预测的不同策略。文献中报告了三种模型预测的光伏功率值。
