基于参数优化多核支持向量机的光伏功率预测算法

贺亦琛; 师长立; 郭小强; 贺伟; 韩涛

doi:10.19912/j.0254-0096.tynxb.2023-0826

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (9) : 394-404. DOI: 10.19912/j.0254-0096.tynxb.2023-0826

基于参数优化多核支持向量机的光伏功率预测算法

贺亦琛^1,2, 师长立², 郭小强¹, 贺伟³, 韩涛³

作者信息 +

PHOTOVOLTAIC POWER PREDICTION ALGORITHM BASED ON PARAMETER OPTIMAZATION OF MULTI-KERNEL SVM

He Yichen^1,2, Shi Changli², Guo Xiaoqiang¹, He Wei³, Han Tao³

Author information +

文章历史 +

摘要

准确的光伏功率预测对电力系统的稳定运行具有重大意义。针对现有预测算法在处理多维输入天气变量时存在的运算时间过长和特征提取能力较差的问题,提出一种基于参数优化的多核函数支持向量机的预测算法。首先,该新型算法对数据进行预处理,灰色关联度提取与预测日相似度高的历史日以提升预测精度,主成分分析（PCA）对输入数据进行降维,从而提高光伏功率预测的速度。其次,针对单核支持向量机对多维数据特征提取能力相对较差的问题,基于线性核函数和径向基核函数建立多核支持向量机预测模型,根据每个核函数支持向量机的预测误差计算不同的权重,从而增强对输入数据特征提取能力并提高预测精度。采用灰狼优化（GWO）算法确定不同核函数支持向量机的参数以提高预测精度。最后,通过北京某光伏电站的历史数据集验证了该算法的预测效果。实例分析表明,与传统预测算法相比,预测精度和速度都有显著提高。

Abstract

Accurate photovoltaic power prediction is of great significance to the stable operation of power systems. Aiming at the problems of long operation time and poor feature extraction ability in existing forecasting algorithms when dealing with multi-dimensional input weather variables, this paper proposes a forecasting algorithm based on multi-kernel function support vector machine with parameter optimization. First of all, the new algorithm preprocesses the data, gray correlation degree is used to extract historical days with high similarity to the forecasted day to improve the forecasting accuracy, and principal component analysis （PCA） reduces the dimensionality of the input data to improve the accuracy of photovoltaic power forecasting. Secondly, in view of the relatively poor ability of single-kernel support vector machine to extract multi-dimensional data features, a multi-kernel support vector machine model is established based on linear kernel function and radial basis kernel function to predict photovoltaic power generation. Different weights are calculated according to the prediction error of each kernel function support vector machine to enhance the feature extraction ability of the input data and improve the prediction accuracy. The gray wolf optimization （GWO） algorithm is used to determine the parameters of the support vector machine with different kernel functions to improve the prediction accuracy. Finally, the prediction effect of the algorithm is verified by the historical dataset of a photovoltaic power station in Beijing. The example analysis shows that compared with the traditional forecasting algorithm, the forecasting accuracy and speed are significantly improved.

导出引用

贺亦琛, 师长立, 郭小强, 贺伟, 韩涛. 基于参数优化多核支持向量机的光伏功率预测算法[J]. 太阳能学报. 2024, 45(9): 394-404 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0826

He Yichen, Shi Changli, Guo Xiaoqiang, He Wei, Han Tao. PHOTOVOLTAIC POWER PREDICTION ALGORITHM BASED ON PARAMETER OPTIMAZATION OF MULTI-KERNEL SVM[J]. Acta Energiae Solaris Sinica. 2024, 45(9): 394-404 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0826

中图分类号： TM615

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