The Short-Term Forecasting of Asymmetry Photovoltaic Power Based on the Feature Extraction of PV Power and SVM Algorithm

被引:10
作者
Wang, Lishu [1 ]
Liu, Yanhui [1 ,2 ]
Li, Tianshu [1 ]
Xie, Xinze [1 ]
Chang, Chengming [1 ]
机构
[1] Northeast Agr Univ, Inst Elect & Informat, Harbin 150030, Peoples R China
[2] Suihua Univ, Inst Elect Engn, Suihua 152061, Peoples R China
来源
SYMMETRY-BASEL | 2020年 / 12卷 / 11期
关键词
ICEEMDAN; relative modes with symmetry; MPSO; SVM; PV power output; EMPIRICAL MODE DECOMPOSITION; PERFORMANCE; PREDICTION;
D O I
10.3390/sym12111777
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
To improve forecasting accuracy for photovoltaic (PV) power output, this paper proposes a hybrid method for forecasting the short-term PV power output. First, by introducing the noise level, an improved complementary ensemble empirical mode decomposition (EEMD) with adaptive noise (ICEEMDAN) is developed to determine the ensemble size and amplitude of the added white noise adaptively. ICEEMDAN can change PV power output with non-symmetry into intrinsic mode functions (IMFs) with symmetry. ICEEMDAN can enhance the forecasting accuracy for PV power by IMFs with physical meaning (not including spurious modes). Second, the selection method of relative modes (IF), which is determined by the comprehensive factor, including the shape factor, crest factor and Kurtosis, is introduced to adaptively classify the IMFs into groups including similar fluctuating components. The IF can avoid the drawbacks of threshold determination by an empirical method. Third, the modified particle swarm optimization (PSO) (MPSO) is proposed to optimize the hyper-parameters in the support vector machine (SVM) by introducing the piecewise inertial weight. MPSO can improve the global and local search ability to make the particles traverse the global space and strengthen the performance of local convergence. Finally, the proposed method (ICEEMDAN-IF-MPSO-SVM) is used to forecast the PV power output of each group individually, and then, the single forecasting result is reconstructed to obtain the desired forecasting result for PV power output. By comparison with the other typical methods, the proposed method is more suitable for forecasting PV power output.
引用
收藏
页码:1 / 20
页数:20
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