Short-term photovoltaic generation forecasting based on EEMD-SVM combined method

作者：Mao, Meiqin^*; Gong, Wenjian; Chang, Liuchen; Cao, Yu; Xu, Haibo

来源：Proceedings of the CSEE, 2013, 33(34): 17-24.

• Combination forecasting
• Combined forecasting
• Ensemble empirical mode decomposition
• Ensemble empirical mode decompositions (EEMD)
• Mean absolute percentage error
• Photovoltaic power stations
• Photovoltaic system (PVS)
• Short term prediction

A combined prediction method based on ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) was proposed to tackle the problem of the short-term forecast of hourly output photovoltaic system (PVS) a day ahead. Weather types were classified into abnormal day (weather changed suddenly) and normal day. Firstly, the history data for hourly output of PVS was decomposed into a series of components by using EEMD method. Considering different factors for different types of weather, the different models were built and different kernel functions and parameters were chosen to deal with each component of the data by using SVM. Simulation results show that the proposed classification modeling ideas and EEMD-SVM combination forecasting method enable the mean absolute percentage error results for the abnormal days decreased by 5%, and normal day decreased by 3% compared with the traditional SVM method.

• 出版日期2013
• 单位合肥工业大学