基于PCA‑VMD‑MVO‑SVM的短期光伏输出功率预测方法

Prediction method of short‑term PV output power based on PCA‑VMD‑MVO‑SVM

为了提高光伏输出功率短期预测的准确性和可靠性,提出一种基于主成分分析法(principal component analysis,PCA)、变分模态分解法(variational mode decomposition,VMD)和多元宇宙算法(multi verse optimizer,MVO)对支持向量机(support vector machine,SVM)进行优化的光伏输出功率短期预测组合模型。先利用PCA具有的数据分析能力和VMD具有的数据分解性能,对多维训练数据进行降维和分解;再将提取后的数据输入由MVO算法优化的SVM预测模型,得到不同本征模态的光伏输出功率预测分量;最后,将各预测分量的结果进行叠加。研究结果表明:该模型在晴天、多云和阴雨天时的平均绝对百分比误差分别为0.7453%、0.5105%和1.0156%。以多云天气为例,该模型的平均绝对百分比误差比MVO‐SVM、VMD‐MVO‐SVM、PCA‐MVO‐SVM模型的分别降低了3.8207%、2.9173%和1.8438%。

To enhance the accuracy and reliability of short-term photovoltaic(PV)output power forecasting,a hybrid model is proposed,which integrates principal component analysis(PCA),variational mode decomposition(VMD),and multi-verse optimizer(MVO)to optimize a support vector machine(SVM)for PV output power prediction.Initially,PCA's data analysis capabilities and VMD's data decomposition performance are leveraged to reduce the dimensionality and decompose the multidimensional training data.Subsequently,the extracted dataset is fed into an SVM prediction model optimized by the MVO algorithm to obtain PV output power forecast components for different intrinsic modes.Finally,the results of these forecast components are aggregated.The research findings indicate that the proposed model achieves mean absolute percentage errors(MAPEs)of 0.7453%,0.5105%,and 1.0156%for sunny,partly cloudy,and rainy days,respectively.Taking partly cloudy weather as an example,the MAPE of the proposed model is reduced by 3.8207%,2.9173%,and 1.8438%compared to the MVO-SVM,VMD-MVO-SVM,and PCAMVO-SVM models,respectively.