发表机构
University of Würzburg; Baden-Wuerttemberg Cooperative State University Mosbach（维尔茨堡大学; 巴登-符腾堡双元制应用技术大学莫斯巴赫分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究可再生能源预测问题，提出基于聚类的顺序特征选择（CSFS）方法，通过结构化文献综述分析特征选择现状，经实证评估，该方法能在可再生能源预测中实现高效可靠的特征选择，性能与SFS相当且降低计算成本。
AI 中文摘要
随着全球能源需求上升以及对气候变化及其影响的认识不断提高，可再生能源在全球能源结构中的占比持续增长。由于其依赖环境条件，可再生能源输出难以像传统发电那样稳定控制，可靠的能源产量预测至关重要。本文报告了两项关于可再生能源预测任务的结构化文献综述结果，涵盖风力涡轮机功率曲线建模和光伏发电预测。分析发现特征选择方法有限且不系统。为此提出基于聚类的顺序特征选择（CSFS）方法，它是一种新颖的、与模型无关的、基于聚类的包装器方法，并在GitHub上提供开源实现。通过实证评估，结果表明基于包装器的方法总体上能提供更好的特征选择，CSFS性能与SFS相当且平均降低计算成本21%。
英文摘要
With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow. Unlike conventional power generation, the output of renewable energy sources cannot be controlled as consistently due to their dependence on environmental conditions. Therefore, reliable prediction of current and future energy production is essential. In this paper, we report findings from two structured literature reviews on real-world renewable energy prediction tasks: wind turbine power curve modeling and photovoltaic power prediction. For the former, we conducted a comprehensive literature review ourselves, while for the latter, we synthesize the key findings regarding frequently selected input features based on an existing survey. Across both domains, our analysis reveals that despite the large number of available monitoring and environmental variables, only limited or unsystematic methods for feature selection exist. To address this gap, we propose Cluster-based Sequential Feature Selection (CSFS), a novel, model-agnostic, clustering-based wrapper method for automatic, efficient, and reliable feature selection in renewable energy prediction pipelines. To support reproducibility and reuse, we provide an open-source implementation of CSFS on GitHub. We empirically evaluate the proposed approach on both use cases and compare it with established feature selection techniques such as wrapper-based sequential feature selection (SFS), filter-based methods, and Random Forest's embedded feature importance. The results show that the wrapper-based methods overall provide better-performing selections of features. CSFS achieves a predictive performance comparable to SFS while reducing computational cost by an average of 21%.
Comments17 pages, 5 figures
