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

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-stationa...

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Bibliographic Details
Published in:Energies (Basel) 2020-07, Vol.13 (14), p.3592
Main Authors: Wu, Xiaomei, Lai, Chun Sing, Bai, Chenchen, Lai, Loi Lei, Zhang, Qi, Liu, Bo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alternative energy sources
Confidence intervals
Decomposition
firefly algorithm
Interpolation
kernel extreme learning machine
Learning algorithms
Methods
Model accuracy
Neural networks
photovoltaic power output prediction
Photovoltaics
Prediction models
Principal components analysis
probabilistic prediction interval
Renewable resources
Statistical analysis
Support vector machines
variational mode decomposition
Weather forecasting
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13143592