标题： 基于模糊集特征提取的短期风速综合预测系统 显示英文标题

标题： A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction

摘要： 由于风力发电的持续增长和技术的进步，风能具有巨大的潜力。 然而，风速的变化受到多种因素复杂相互作用的影响，使其高度变化。 风速演变的非线性和非平稳性可能对整个电力系统产生重大影响。 为了解决这一挑战，我们提出了一种基于模糊特征提取的集成多帧风速预测系统。 该系统采用三角隶属函数的凸子集划分方法进行模糊特征提取。 通过将软聚类应用于子集，构建隶属矩阵并识别聚类中心，该系统引入了内部和边界区域的概念。 随后，通过测量类间和类内距离，计算数据点到聚类中心的距离。 该方法使用隶属矩阵更新聚类中心，生成最优特征值。 在此基础上，我们使用多种机器学习方法将模糊特征输入预测模型，并集成学习技术来预测特征值。 由于不同的数据集需要不同的建模方法，采用了集成权重更新模块，通过设置双目标函数动态调整模型权重，以确保预测的准确性和稳定性。 通过蓬莱风电场数据的经验分析，展示了所提出的模型在预测性能和泛化能力方面的有效性。 显示英文摘要

摘要： Wind energy has significant potential owing to the continuous growth of wind power and advancements in technology. However, the evolution of wind speed is influenced by the complex interaction of multiple factors, making it highly variable. The nonlinear and nonstationary nature of wind speed evolution can have a considerable impact on the overall power system. To address this challenge, we propose an integrated multiframe wind speed prediction system based on fuzzy feature extraction. This system employs a convex subset partitioning approach using a triangular affiliation function for fuzzy feature extraction. By applying soft clustering to the subsets, constructing an affiliation matrix, and identifying clustering centers, the system introduces the concepts of inner and boundary domains. It subsequently calculates the distances from data points to the clustering centers by measuring both interclass and intraclass distances. This method updates the cluster centers using the membership matrix, generating optimal feature values. Building on this foundation, we use multiple machine learning methods to input the fuzzy features into the prediction model and integrate learning techniques to predict feature values. Because different datasets require different modeling approaches, the integrated weight-updating module was used to dynamically adjust model weights by setting a dual objective function to ensure the accuracy and stability of the prediction. The effectiveness of the proposed model in terms of prediction performance and generalization ability is demonstrated through an empirical analysis of data from the Penglai wind farm.