CAJ | 학술논문

针对光伏电站日前小时短期出力预测问题，提出一种基于集合经验模态分解(ensemble empirical mode decomposition，EEMD)和支持向量机(support vector machines，SVM)的 EEMD-SVM 组合模型预测方法。该方法将天气类型分为突变天气和非突变天气。首先采用EEMD分解法将历史光伏电站小时出力数据分解为一系列相对平稳的分量序列，对不同的天气类型考虑不同的气象因素，然后采用SVM法对所分解的各分量序列分别建立预测模型，选用不同的核函数和参数以取得单个分量序列的最佳预测精度。算例结果表明，分类建模思想和EEMD-SVM组合预测法能够使突变天气预测结果的平均绝对百分比误差减少5%，非突变天气的减少3%。
침대광복전참일전소시단기출력예측문제，제출일충기우집합경험모태분해(ensemble empirical mode decomposition，EEMD)화지지향량궤(support vector machines，SVM)적 EEMD-SVM 조합모형예측방법。해방법장천기류형분위돌변천기화비돌변천기。수선채용EEMD분해법장역사광복전참소시출력수거분해위일계렬상대평은적분량서렬，대불동적천기류형고필불동적기상인소，연후채용SVM법대소분해적각분량서렬분별건립예측모형，선용불동적핵함수화삼수이취득단개분량서렬적최가예측정도。산례결과표명，분류건모사상화EEMD-SVM조합예측법능구사돌변천기예측결과적평균절대백분비오차감소5%，비돌변천기적감소3%。
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.