基于DA-RKELM算法的光伏发电功率预测方法

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

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (10): 2041-2051.doi: 10.16182/j.issn1004731x.joss.20-FZ0289

基于DA-RKELM算法的光伏发电功率预测方法

魏铭琦¹, 张天瑞¹, 高秀秀¹, 王淑梅²

1.沈阳大学机械工程学院,辽宁沈阳 110041;
2.沈阳大学工商管理学院,辽宁沈阳 110041

收稿日期:2020-03-25 修回日期:2020-06-02 出版日期:2020-10-18 发布日期:2020-10-14
作者简介:魏铭琦(1995-),男,辽宁阜新,硕士生,研究方向为质量预测;张天瑞(1985-),男,河北深州,博士,副教授,研究方向为供应链管理。
基金资助:
工信部重大专项(201675514),辽宁省自然科学基金(20180551001)

A Photovoltaic Power Forecasting Method Based on DA-RKELM Algorithm

Wei Mingqi¹, Zhang Tianrui¹, Gao Xiuxiu¹, Wang Shumei²

1. School of Mechanical Engineering, Shenyang University, Shenyang 110041, China;
2. School of Business Administration, Shenyang University, Shenyang 110041, China

Received:2020-03-25 Revised:2020-06-02 Online:2020-10-18 Published:2020-10-14

摘要/Abstract

摘要： 针对光伏发电功率具有的波动性和随机性等特点造成的电网安全问题,提出了一种基于蜻蜓算法优化的正则核极限学习机光伏发电功率预测方法。通过相关性分析确定影响光伏发电功率的关键影响因子,构建光伏发电功率预测模型;利用蜻蜓算法获取网络最优的权重和阈值,在标准极限学习基础上引入正则化函数和核函数以避免传统梯度下降法造成的过拟合问题,增强模型空间映射能力;仿真实验表明,与DA-ELM、PSO-ELM以及标准-DA-ELM模型相比,DA-RKELM预测模型能达到更高的预测精度,更贴近光伏发电的实际运行功率。

关键词: 光伏发电, 蜻蜓算法, 正则核极限学习机, 正则函数, 核函数

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

中图分类号:

TM615

魏铭琦, 张天瑞, 高秀秀, 王淑梅. 基于DA-RKELM算法的光伏发电功率预测方法[J]. 系统仿真学报, 2020, 32(10): 2041-2051.

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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基于DA-RKELM算法的光伏发电功率预测方法

A Photovoltaic Power Forecasting Method Based on DA-RKELM Algorithm

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