为实现对光伏集群的科学聚类划分,该文融合考虑气象因素和地理位置对光伏电站聚类的影响,将出力特性作为子集群的划分特征,提出一种基于改进曼哈顿距离的K中心点聚类算法（K-中心点）。光伏集群聚类的两个关键是气象特征的有效识别和各电站的相似性度量,为此采用变异系数法与秩和比法相结合对气象输入特征进行提取,可提高特征的识别有效性。另一方面将改进曼哈顿距离引入K-中心点聚类算法中,充分挖掘数据的动态特性,并采用3种内部有效指标优化聚类划分数,避免常规划分带来的分散性和偏差,提高聚类的整体效果。此外,在聚类基础上进行预测验证,为解决预测模型的参数设置问题,在预测模型中融入优化算法,采用改进灰狼优化算法（IGWO）对iTransformer和极度梯度提升（XGBoost）模型中的参数进行优化,功率预测结果表明所提聚类算法的有效性以及较高的精度。
Abstract
For the purpose of achieving a scientifically rigorous and methodologically sound clustering partition of photovoltaic clusters, this study incorporates the synergistic effects of meteorological factors and geographical positioning on the clustering of photovoltaic power stations. Employing output characteristics as the basis for subgroup delineation, the paper introduces a K-medoids clustering algorithm based upon an enhanced Manhattan distance metric. The two pivotal dimensions in photovoltaic cluster analysis encompass the robust identification of meteorological features and the precise quantification of inter-station similarity. Accordingly, an integrated approach combining the coefficient of variation method with the rank-sum ratio method is utilized to derive meteorological input features, thereby augmenting the efficacy of feature discrimination. Moreover, the refined Manhattan distance is embedded within the K-medoids framework to comprehensively elucidate the dynamic attributes inherent in the dataset. Three internally validated clustering metrics are leveraged to optimize cluster cardinality, effectively mitigating the dispersion and systematic bias endemic to conventional partitioning approaches while enhancing overall clustering robustness. Subsequent predictive validation is performed based on the established clusters. To resolve the challenge of parameter calibration within the forecasting model, metaheuristic optimization techniques are integrated. Specifically, an improved grey wolf optimization （IGWO） algorithm is deployed to fine-tune the hyperparameters of both the iTransformer and extreme gradient boosting （XGBoost） architectures. The power forecasting results verify the operational efficacy and superior predictive accuracy of the proposed clustering methodology.
关键词
光伏发电 /
聚类分析 /
功率预测 /
改进灰狼优化算法 /
iTransformer /
极度梯度提升
Key words
photovoltaic power /
cluster analysis /
power forecasting /
improved gray wolf optimization algorithm /
iTransformer /
extreme gradient boosting
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