A Photovoltaic Power Forecasting Method Based on DA-RKELM Algorithm

doi:10.16182/j.issn1004731x.joss.20-FZ0289

Abstract

Abstract: Aiming at the power grid safety problems caused by the fluctuation and randomness of photo-voltaic power generation, a method for predicting photo-voltaic power generation of a regular nuclear limit learning machine based on the optimization of a dragonfly algorithm was proposed. Through correlation analysis, the key factors affecting the photo-voltaic power generation are determined, and the photo-voltaic power prediction model is constructed. Dragonfly algorithm is used to obtain the optimal weight and threshold value of the network, and regularization function and kernel function are introduced based on the standard limit learning machine to avoid the over fitting problem caused by the traditional gradient descent method and enhance the spatial mapping ability of the model. Simulation experiments show that compared with DA-ELM, PSO-ELM and GA- ELM models, the DA-RKELM prediction model achieve higher prediction accuracy, closer to the actual operating power of photo-voltaic power generation.

Key words: photo-voltaic power generation, dragonfly algorithm, regular kernel limit learning machine, regular function, kernel function

CLC Number:

TM615

Wei Mingqi, Zhang Tianrui, Gao Xiuxiu, Wang Shumei. A Photovoltaic Power Forecasting Method Based on DA-RKELM Algorithm[J]. Journal of System Simulation, 2020, 32(10): 2041-2051.

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