基于支持向量机优化和加权复合灰色关系分析的风能预测方法

Miaona You, Sumei Zhuang, Ruxue Luo
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引用次数: 0

摘要

本研究提出了一种利用数值天气预报(NWP)和支持向量机(SVM)进行灰色关系分析(GRA)的加权复合方法。该方法采用改进的灰狼优化(IGWO)算法进行优化。首先,通过 t 分布随机邻域嵌入(t-SNE)降低 NWP 数据的维度,然后通过熵权法(EWM)计算样本系数的权重,并针对不同的天气数值时间序列数据计算数据点的加权灰色关系。同时,结合历史日和待测日 NWP 值的加权余弦相似度,形成新的加权复合灰色关系度。通过时间序列数据构建 SVM 的回归功率预测模型。为了提高系统预测的准确性,选择灰色关系时间序列数据作为 SVM 的输入变量,并利用 IGWO 技术发现理想 SVM 的影响参数。根据基于 NWP 的模拟预测和分析,可以看出本研究提出的方法显著提高了数据的预测精度。具体来说,均方根误差(RMSE)、回归相关系数(r 2)、平均绝对误差(MAE)和平均绝对百分误差(MAPE)等评价指标都有相应的提高,而计算负担仍然相对较低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method for wind power forecasting based on support vector machines optimized and weighted composite gray relational analysis
This study proposes a weighted composite approach for grey relational analysis (GRA) that utilizes a numerical weather prediction (NWP) and support vector machine (SVM). The approach is optimized using an improved grey wolf optimization (IGWO) algorithm. Initially, the dimension of NWP data is decreased by t-distributed stochastic neighbor embedding (t-SNE), then the weight of sample coefficients is calculated by entropy-weight method (EWM), and the weighted grey relational of data points is calculated for different weather numerical time series data. At the same time, a new weighted composite grey relational degree is formed by combining the weighted cosine similarity of NWP values of the historical day and to be measured day. The SVM’s regression power prediction model is constructed by the time series data. To improve the accuracy of the system’s predictions, the grey relational time series data is chosen as the input variable for the SVM, and the influence parameters of the ideal SVM are discovered using the IGWO technique. According to the simulated prediction and analysis based on NWP, it can be observed that the proposed method in this study significantly improves the prediction accuracy of the data. Specifically, evaluation metrics such as root mean squared error (RMSE), regression correlation coefficient (r 2), mean absolute error (MAE) and mean absolute percent error (MAPE) all show corresponding enhancements, while the computational burden remains relatively low.
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