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

Xiaomei Wu,Qi Zhang,Loi Lei Lai,Chun Sing Lai,Bo Liu,Chenchen Bai

doi:10.3390/en13143592

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.

Highlights

Driven by the global severe condition of fossil fuel depletion and growing environmental pollution, as an environmentally friendly renewable energy, photovoltaic (PV) generation is an exemplar of widely used power generation methods in the renewable energy industry
To validate the proposed approach, the simulation results under different confidence levels are compared
The prediction error is obtained, and the prediction interval model based on the kernel density estimation is produced

Summary

Introduction

Driven by the global severe condition of fossil fuel depletion and growing environmental pollution, as an environmentally friendly renewable energy, photovoltaic (PV) generation is an exemplar of widely used power generation methods in the renewable energy industry. PV generation is susceptible to surface solar irradiance, and its output is strongly random, which challenges frequency regulation, peak load regulation, and system reserve. With the increase in grid integration capacity, the randomness of PV generation brings more and more risks to power system scheduling and operation. There are four main techniques to predict PV power output, namely physical, artificial intelligence (AI), statistical, and hybrid approaches [2,3,4]. The physical method uses numerical weather prediction (NWP) data and measured data. The statistical method establishes a relationship between historical data and forecasted variables based on data-driven formulations such as regression models [5,6], time series [7], and cluster analysis of clearness index [8,9].

Results

Discussion

Conclusion