Because of intermittence and fluctuation of photovoltaic (PV) power, it is difficult to enhance prediction accuracy. To sustain high-efficient operation of power system, this paper proposes a hybrid method to predict the short-term PV power. It consists of components separation of PV power, parameters optimization and reconstruction of prediction result. Firstly, the methods based on the identifying of feature frequency and mutual information maximum are proposed to optimize the mode number and penalty factor of VMD, respectively. The optimized VMD (OVMD) is used to decompose the complicated fluctuation components of PV power into single component. Then, the improved PSO (IPSO) based on non-linear inertia weight of anti-sine function is proposed to optimize the number of hidden layer nodes, learning rate and iteration number of LSTM network. The optimized LSTM is used to predict each single component of OVMD decomposition. Thirdly, the prediction result of each single component is reconstructed to obtain the final PV prediction power. The experiment result indicates that the prediction accuracy of the proposed method (OVMD-IPSO-LSTM) outperformances the other typical methods. By the improvement of the traditional method (VMD and PSO) and the parameter optimization of LSTM, this hybrid method makes a contribution to the prediction of short-term PV power.
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.
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