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In order to improve the accuracy of short-term photovoltaic power prediction, a comprehensively artificial intelligent (AI) based algorithm has been proposed. Firstly, a fuzzy C-means (FCM) is used to cluster historical data into different groups according to meteorological variables; Secondly, a variational mode decomposition (VMD) is used to divide original signals into multi-frequency components as a series of intrinsic mode functions (IMF) while whale optimization algorithm (WOA) externally optimizes the parameters of VMD so as to stimulate the data processing. Least squares support vector machine (LSSVM) is adapted to build up the prediction framework with an assistance from improved sparrow search algorithm (ISSA) that aims to solve the key issues on local optimization over LSSVM training process. Under the proposed framework, practical power signals from Northwest China has been tested. The outcomes verify the effectiveness of this comprehensive algorithm. The indicator, mean absolute percentage error (MAPE), has decreased by 17.17%, 15.78% and 5.37% while R2 has increased by 0.17, 0.15 and 0.03 comparing with LSSVM, ISSA-LSSVM and VMD-LSSVM. Moreover, the study outcomes have also evidenced the superiority of ISSA over other fellow heuristic algorithms such as PSO, QPSO and SSA in both convergence speed and accuracy when an ISSA facilitates LSSVM for prediction.

Short-Term Power Prediction By Using Least Square Support Vector Machine With Variational Mode Decomposition In A Photovoltaic System