Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction

Xiaomei Wu, Chun Sing Lai, Chenchen Bai, Loi Lei Lai, Qi Zhang and Bo Liu
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Xiaomei Wu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Chun Sing Lai: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Chenchen Bai: Department of Mathematics, Southern University of Science and Technology, Shenzhen 518055, China
Loi Lei Lai: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Qi Zhang: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Bo Liu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Energies, 2020, vol. 13, issue 14, 1-21

Abstract: A probabilistic prediction interval (PI) model based on variational mode decomposition (VMD) and a kernel extreme learning machine using the firefly algorithm (FA-KELM) is presented to tackle the problem of photovoltaic (PV) power for intra-day-ahead prediction. Firstly, considering the non-stationary and nonlinear characteristics of a PV power output sequence, the decomposition of the original PV power output series is carried out using VMD. Secondly, to further improve the prediction accuracy, KELM is established for each decomposed component and the firefly algorithm is introduced to optimize the penalty factor and kernel parameter. Finally, the point predicted value is obtained through the summation of predicted results of each component and then using the nonlinear kernel density estimation to fit it. The cubic spline interpolation algorithm is applied to obtain the shortest confidence interval. Results from practical cases show that this probabilistic prediction interval could achieve higher accuracy as compared with other prediction models.

Keywords: photovoltaic power output prediction; variational mode decomposition; firefly algorithm; kernel extreme learning machine; probabilistic prediction interval (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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